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import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir("fixtures/spiece.model")
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase__ ( A__ ,unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = DebertaVaTokenizer
UpperCamelCase = DebertaVaTokenizerFast
UpperCamelCase = True
UpperCamelCase = True
def snake_case__ ( self : List[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Optional[Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self : Union[str, Any] , a_ : Any ):
'''simple docstring'''
__UpperCAmelCase : Dict = '''this is a test'''
__UpperCAmelCase : str = '''this is a test'''
return input_text, output_text
def snake_case__ ( self : Tuple ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = '''<pad>'''
__UpperCAmelCase : Dict = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : str ):
'''simple docstring'''
__UpperCAmelCase : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 3_00_01 )
def snake_case__ ( self : Any ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 3_00_00 )
def snake_case__ ( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = ''' \tHeLLo!how \n Are yoU? '''
__UpperCAmelCase : Optional[int] = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
__UpperCAmelCase : Tuple = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Tuple = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : str = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def snake_case__ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def snake_case__ ( self : Optional[int] ):
'''simple docstring'''
pass
def snake_case__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : List[str] = '''I was born in 92000, and this is falsé.'''
__UpperCAmelCase : Dict = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
__UpperCAmelCase : Optional[int] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : int = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[str] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = '''I was born in 92000, and this is falsé.'''
__UpperCAmelCase : int = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
__UpperCAmelCase : List[Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Union[str, Any] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Union[str, Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : int ):
'''simple docstring'''
__UpperCAmelCase : List[str] = '''I was born in 92000, and this is falsé.'''
__UpperCAmelCase : Tuple = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
__UpperCAmelCase : Optional[Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : str ):
'''simple docstring'''
__UpperCAmelCase : Dict = '''I was born in 92000, and this is falsé.'''
__UpperCAmelCase : Tuple = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
__UpperCAmelCase : Optional[int] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Union[str, Any] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = ''' \tHeLLo!how \n Are yoU? '''
__UpperCAmelCase : Union[str, Any] = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
__UpperCAmelCase : Any = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Dict = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ , split_by_punct=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
__UpperCAmelCase : int = self.get_rust_tokenizer()
__UpperCAmelCase : Tuple = '''I was born in 92000, and this is falsé.'''
__UpperCAmelCase : int = tokenizer.convert_ids_to_tokens(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
__UpperCAmelCase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[Any] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[Any] = self.get_rust_tokenizer()
__UpperCAmelCase : str = tokenizer.encode(SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : Tuple ):
'''simple docstring'''
__UpperCAmelCase : Any = '''This is a test'''
__UpperCAmelCase : Dict = [13, 1, 43_98, 25, 21, 12_89]
__UpperCAmelCase : Optional[Any] = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
__UpperCAmelCase : str = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
__UpperCAmelCase : Union[str, Any] = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[Any] = DebertaVaTokenizerFast(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Dict = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Dict = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Tuple = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : int = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[Any] = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = rust_tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# fmt: off
__UpperCAmelCase : Optional[int] = '''I was born in 92000, and this is falsé.'''
__UpperCAmelCase : str = [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
__UpperCAmelCase : List[str] = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
__UpperCAmelCase : Dict = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
__UpperCAmelCase : Union[str, Any] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[Any] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : List[Any] = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Any = rust_tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = DebertaVaTokenizer(SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : Optional[int] = tokenizer.encode('''sequence builders''' )
__UpperCAmelCase : str = tokenizer.encode('''multi-sequence build''' )
__UpperCAmelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ )
__UpperCAmelCase : str = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , SCREAMING_SNAKE_CASE__ )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , SCREAMING_SNAKE_CASE__ , )
@slow
def snake_case__ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : str = {'''input_ids''': [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE__ , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 226
|
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 337
| 0
|
from math import pow, sqrt
def A ( *_lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = len(a__ ) > 0 and all(value > 0.0 for value in values )
return result
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(a__ , a__ )
else ValueError("Input Error: Molar mass values must greater than 0." )
)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(a__ , a__ , a__ )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(a__ , a__ , a__ )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(a__ , a__ , a__ )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(a__ , a__ , a__ )
else ValueError(
"Input Error: Molar mass and effusion rate values must greater than 0." )
)
| 361
|
class UpperCAmelCase_ :
def __init__( self):
'''simple docstring'''
_lowerCAmelCase : Dict = 0
_lowerCAmelCase : Optional[int] = 0
_lowerCAmelCase : Tuple = {}
def snake_case__ ( self, __a):
'''simple docstring'''
if vertex not in self.adjacency:
_lowerCAmelCase : List[Any] = {}
self.num_vertices += 1
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
self.add_vertex(__a)
self.add_vertex(__a)
if head == tail:
return
_lowerCAmelCase : Dict = weight
_lowerCAmelCase : Dict = weight
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = self.get_edges()
for edge in edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = edge
edges.remove((tail, head, weight))
for i in range(len(__a)):
_lowerCAmelCase : Optional[int] = list(edges[i])
edges.sort(key=lambda __a: e[2])
for i in range(len(__a) - 1):
if edges[i][2] >= edges[i + 1][2]:
_lowerCAmelCase : Tuple = edges[i][2] + 1
for edge in edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = edge
_lowerCAmelCase : Union[str, Any] = weight
_lowerCAmelCase : Optional[int] = weight
def __str__( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ""
for tail in self.adjacency:
for head in self.adjacency[tail]:
_lowerCAmelCase : List[Any] = self.adjacency[head][tail]
string += f"{head} -> {tail} == {weight}\n"
return string.rstrip("\n")
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]))
return output
def snake_case__ ( self):
'''simple docstring'''
return self.adjacency.keys()
@staticmethod
def snake_case__ ( __a=None, __a=None):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = Graph()
if vertices is None:
_lowerCAmelCase : Any = []
if edges is None:
_lowerCAmelCase : Any = []
for vertex in vertices:
g.add_vertex(__a)
for edge in edges:
g.add_edge(*__a)
return g
class UpperCAmelCase_ :
def __init__( self):
'''simple docstring'''
_lowerCAmelCase : Dict = {}
_lowerCAmelCase : List[Any] = {}
def __len__( self):
'''simple docstring'''
return len(self.parent)
def snake_case__ ( self, __a):
'''simple docstring'''
if item in self.parent:
return self.find(__a)
_lowerCAmelCase : Optional[int] = item
_lowerCAmelCase : Any = 0
return item
def snake_case__ ( self, __a):
'''simple docstring'''
if item not in self.parent:
return self.make_set(__a)
if item != self.parent[item]:
_lowerCAmelCase : Any = self.find(self.parent[item])
return self.parent[item]
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.find(__a)
_lowerCAmelCase : List[str] = self.find(__a)
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
_lowerCAmelCase : Any = roota
return roota
if self.rank[roota] < self.rank[roota]:
_lowerCAmelCase : List[Any] = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
_lowerCAmelCase : int = roota
return roota
return None
@staticmethod
def snake_case__ ( __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = graph.num_vertices
_lowerCAmelCase : Optional[int] = Graph.UnionFind()
_lowerCAmelCase : str = []
while num_components > 1:
_lowerCAmelCase : List[str] = {}
for vertex in graph.get_vertices():
_lowerCAmelCase : Optional[Any] = -1
_lowerCAmelCase : Union[str, Any] = graph.get_edges()
for edge in edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : str = edge
edges.remove((tail, head, weight))
for edge in edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[int] = edge
_lowerCAmelCase : Dict = union_find.find(__a)
_lowerCAmelCase : Optional[Any] = union_find.find(__a)
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_lowerCAmelCase : Union[str, Any] = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_lowerCAmelCase : Tuple = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[int] = cheap_edge[vertex]
if union_find.find(__a) != union_find.find(__a):
union_find.union(__a, __a)
mst_edges.append(cheap_edge[vertex])
_lowerCAmelCase : Any = num_components - 1
_lowerCAmelCase : List[str] = Graph.build(edges=__a)
return mst
| 300
| 0
|
UpperCAmelCase_ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int , __A : int ) -> str:
"""simple docstring"""
assert len(str(__A ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a_ : List[str] = year // 1_00
a_ : Optional[int] = (5 * (century % 4) + 2) % 7
a_ : List[str] = year % 1_00
a_ : str = centurian % 12
a_ : List[str] = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a_ : Any = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a_ : Any = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple=1_3 , SCREAMING_SNAKE_CASE__ : str=7 , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=9_9 , SCREAMING_SNAKE_CASE__ : Optional[Any]=3_2 , SCREAMING_SNAKE_CASE__ : List[str]=5 , SCREAMING_SNAKE_CASE__ : List[Any]=4 , SCREAMING_SNAKE_CASE__ : Tuple=3_7 , SCREAMING_SNAKE_CASE__ : Any="gelu" , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : int=5_1_2 , SCREAMING_SNAKE_CASE__ : int=1_6 , SCREAMING_SNAKE_CASE__ : Optional[int]=2 , SCREAMING_SNAKE_CASE__ : Any=0.02 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE__ : Optional[Any]=4 , SCREAMING_SNAKE_CASE__ : Optional[int]=None , ) -> Any:
a_ : Tuple = parent
a_ : int = batch_size
a_ : Tuple = seq_length
a_ : List[Any] = is_training
a_ : List[str] = use_token_type_ids
a_ : Dict = use_labels
a_ : Any = vocab_size
a_ : List[str] = hidden_size
a_ : Tuple = num_hidden_layers
a_ : List[Any] = num_attention_heads
a_ : Dict = intermediate_size
a_ : Any = hidden_act
a_ : List[str] = hidden_dropout_prob
a_ : Tuple = attention_probs_dropout_prob
a_ : Optional[Any] = max_position_embeddings
a_ : List[Any] = type_vocab_size
a_ : int = type_sequence_label_size
a_ : List[Any] = initializer_range
a_ : List[str] = num_labels
a_ : Union[str, Any] = num_choices
a_ : str = scope
a_ : Tuple = self.vocab_size - 1
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Any:
a_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a_ : Any = None
if self.use_token_type_ids:
a_ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a_ : List[Any] = None
a_ : Union[str, Any] = None
a_ : List[Any] = None
if self.use_labels:
a_ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a_ : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
a_ : Union[str, Any] = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
a_ : List[str] = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , *SCREAMING_SNAKE_CASE__ : Tuple ) -> Union[str, Any]:
a_ : Dict = OpenAIGPTModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : str = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , head_mask=SCREAMING_SNAKE_CASE__ )
a_ : Dict = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ )
a_ : Dict = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , *SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Any:
a_ : str = OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : Optional[int] = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[Any] , *SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Dict:
a_ : int = OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : str = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , *SCREAMING_SNAKE_CASE__ : str ) -> List[str]:
a_ : Any = self.num_labels
a_ : Dict = OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a_ : Any = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
a_ : Optional[Any] = self.prepare_config_and_inputs()
(
(
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) ,
) : Optional[Any] = config_and_inputs
a_ : Optional[int] = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
snake_case__ : Tuple = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
snake_case__ : List[str] = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
snake_case__ : Dict = (
{
'''feature-extraction''': OpenAIGPTModel,
'''text-classification''': OpenAIGPTForSequenceClassification,
'''text-generation''': OpenAIGPTLMHeadModel,
'''zero-shot''': OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] ) -> Dict:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Any=False ) -> List[str]:
a_ : str = super()._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_labels=SCREAMING_SNAKE_CASE__ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
a_ : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ , )
a_ : str = inputs_dict['labels']
a_ : Optional[int] = inputs_dict['labels']
a_ : Optional[int] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ , )
a_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
return inputs_dict
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
a_ : str = OpenAIGPTModelTester(self )
a_ : int = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , n_embd=3_7 )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Tuple:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Tuple:
a_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Tuple:
a_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[Any]:
a_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[Any]:
a_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> str:
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ : str = OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
a_ : Dict = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ ) # the president is
a_ : Tuple = [
4_8_1,
4_7_3_5,
5_4_4,
2_4_6,
9_6_3,
8_7_0,
7_6_2,
2_3_9,
2_4_4,
4_0_4_7_7,
2_4_4,
2_4_9,
7_1_9,
8_8_1,
4_8_7,
5_4_4,
2_4_0,
2_4_4,
6_0_3,
4_8_1,
] # the president is a very good man. " \n " i\'m sure he is, " said the
a_ : Dict = model.generate(SCREAMING_SNAKE_CASE__ , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(output_ids[0].tolist() , SCREAMING_SNAKE_CASE__ )
| 32
| 1
|
'''simple docstring'''
# Imports
import numpy as np
class UpperCamelCase_ :
def __init__( self , A=None , A=None , A=None , A=None , A=None ) -> Union[str, Any]:
self.set_matricies(red=A , green=A , blue=A , red_edge=A , nir=A )
def _lowercase( self , A=None , A=None , A=None , A=None , A=None ) -> Dict:
if red is not None:
UpperCAmelCase : Optional[Any] = red
if green is not None:
UpperCAmelCase : Optional[Any] = green
if blue is not None:
UpperCAmelCase : List[Any] = blue
if red_edge is not None:
UpperCAmelCase : Dict = red_edge
if nir is not None:
UpperCAmelCase : str = nir
return True
def _lowercase( self , A="" , A=None , A=None , A=None , A=None , A=None ) -> List[Any]:
self.set_matricies(red=A , green=A , blue=A , red_edge=A , nir=A )
UpperCAmelCase : Tuple = {
"""ARVI2""": self.arvaa,
"""CCCI""": self.ccci,
"""CVI""": self.cvi,
"""GLI""": self.gli,
"""NDVI""": self.ndvi,
"""BNDVI""": self.bndvi,
"""redEdgeNDVI""": self.red_edge_ndvi,
"""GNDVI""": self.gndvi,
"""GBNDVI""": self.gbndvi,
"""GRNDVI""": self.grndvi,
"""RBNDVI""": self.rbndvi,
"""PNDVI""": self.pndvi,
"""ATSAVI""": self.atsavi,
"""BWDRVI""": self.bwdrvi,
"""CIgreen""": self.ci_green,
"""CIrededge""": self.ci_rededge,
"""CI""": self.ci,
"""CTVI""": self.ctvi,
"""GDVI""": self.gdvi,
"""EVI""": self.evi,
"""GEMI""": self.gemi,
"""GOSAVI""": self.gosavi,
"""GSAVI""": self.gsavi,
"""Hue""": self.hue,
"""IVI""": self.ivi,
"""IPVI""": self.ipvi,
"""I""": self.i,
"""RVI""": self.rvi,
"""MRVI""": self.mrvi,
"""MSAVI""": self.m_savi,
"""NormG""": self.norm_g,
"""NormNIR""": self.norm_nir,
"""NormR""": self.norm_r,
"""NGRDI""": self.ngrdi,
"""RI""": self.ri,
"""S""": self.s,
"""IF""": self._if,
"""DVI""": self.dvi,
"""TVI""": self.tvi,
"""NDRE""": self.ndre,
}
try:
return funcs[index]()
except KeyError:
print("""Index not in the list!""" )
return False
def _lowercase( self ) -> int:
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def _lowercase( self ) -> Optional[int]:
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def _lowercase( self ) -> Any:
return self.nir * (self.red / (self.green**2))
def _lowercase( self ) -> int:
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def _lowercase( self ) -> Any:
return (self.nir - self.red) / (self.nir + self.red)
def _lowercase( self ) -> Optional[Any]:
return (self.nir - self.blue) / (self.nir + self.blue)
def _lowercase( self ) -> Dict:
return (self.redEdge - self.red) / (self.redEdge + self.red)
def _lowercase( self ) -> Dict:
return (self.nir - self.green) / (self.nir + self.green)
def _lowercase( self ) -> str:
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def _lowercase( self ) -> List[Any]:
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def _lowercase( self ) -> List[str]:
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def _lowercase( self ) -> int:
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def _lowercase( self , A=0.0_8 , A=1.2_2 , A=0.0_3 ) -> List[str]:
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def _lowercase( self ) -> List[str]:
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def _lowercase( self ) -> Union[str, Any]:
return (self.nir / self.green) - 1
def _lowercase( self ) -> Dict:
return (self.nir / self.redEdge) - 1
def _lowercase( self ) -> str:
return (self.red - self.blue) / self.red
def _lowercase( self ) -> Any:
UpperCAmelCase : Optional[Any] = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def _lowercase( self ) -> int:
return self.nir - self.green
def _lowercase( self ) -> str:
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def _lowercase( self ) -> Dict:
UpperCAmelCase : Optional[int] = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def _lowercase( self , A=0.1_6 ) -> Any:
return (self.nir - self.green) / (self.nir + self.green + y)
def _lowercase( self , A=0.5 ) -> Optional[int]:
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def _lowercase( self ) -> Optional[int]:
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def _lowercase( self , A=None , A=None ) -> Union[str, Any]:
return (self.nir - b) / (a * self.red)
def _lowercase( self ) -> Any:
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def _lowercase( self ) -> Optional[Any]:
return (self.red + self.green + self.blue) / 3_0.5
def _lowercase( self ) -> Dict:
return self.nir / self.red
def _lowercase( self ) -> List[str]:
return (self.rvi() - 1) / (self.rvi() + 1)
def _lowercase( self ) -> List[Any]:
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def _lowercase( self ) -> Dict:
return self.green / (self.nir + self.red + self.green)
def _lowercase( self ) -> Any:
return self.nir / (self.nir + self.red + self.green)
def _lowercase( self ) -> int:
return self.red / (self.nir + self.red + self.green)
def _lowercase( self ) -> Tuple:
return (self.green - self.red) / (self.green + self.red)
def _lowercase( self ) -> Union[str, Any]:
return (self.red - self.green) / (self.red + self.green)
def _lowercase( self ) -> List[str]:
UpperCAmelCase : Union[str, Any] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
UpperCAmelCase : Optional[Any] = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def _lowercase( self ) -> Tuple:
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def _lowercase( self ) -> str:
return self.nir / self.red
def _lowercase( self ) -> Optional[int]:
return (self.ndvi() + 0.5) ** (1 / 2)
def _lowercase( self ) -> List[str]:
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 338
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
a : int = logging.get_logger(__name__)
a : int = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
a : Tuple = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_7, 3_6_6, 4_3_8, 5_3_2, 6_8_5,
7_0_5, 7_9_6, 9_3_0, 1_0_5_8, 1_2_2_0, 1_2_6_7, 1_2_7_9, 1_3_0_3, 1_3_4_3, 1_3_7_7,
1_3_9_1, 1_6_3_5, 1_7_8_2, 1_8_7_5, 2_1_6_2, 2_3_6_1, 2_4_8_8, 3_4_6_7, 4_0_0_8, 4_2_1_1,
4_6_0_0, 4_8_0_8, 5_2_9_9, 5_8_5_5, 6_3_2_9, 7_2_0_3, 9_6_0_9, 9_9_5_9, 1_0_5_6_3, 1_0_7_8_6,
1_1_4_2_0, 1_1_7_0_9, 1_1_9_0_7, 1_3_1_6_3, 1_3_6_9_7, 1_3_7_0_0, 1_4_8_0_8, 1_5_3_0_6, 1_6_4_1_0, 1_6_7_9_1,
1_7_9_9_2, 1_9_2_0_3, 1_9_5_1_0, 2_0_7_2_4, 2_2_3_0_5, 2_2_9_3_5, 2_7_0_0_7, 3_0_1_0_9, 3_0_4_2_0, 3_3_4_0_9,
3_4_9_4_9, 4_0_2_8_3, 4_0_4_9_3, 4_0_5_4_9, 4_7_2_8_2, 4_9_1_4_6, 5_0_2_5_7, 5_0_3_5_9, 5_0_3_6_0, 5_0_3_6_1
]
a : Optional[int] = [
1, 2, 7, 8, 9, 1_0, 1_4, 2_5,
2_6, 2_7, 2_8, 2_9, 3_1, 5_8, 5_9, 6_0, 6_1, 6_2,
6_3, 9_0, 9_1, 9_2, 9_3, 3_5_9, 5_0_3, 5_2_2, 5_4_2, 8_7_3,
8_9_3, 9_0_2, 9_1_8, 9_2_2, 9_3_1, 1_3_5_0, 1_8_5_3, 1_9_8_2, 2_4_6_0, 2_6_2_7,
3_2_4_6, 3_2_5_3, 3_2_6_8, 3_5_3_6, 3_8_4_6, 3_9_6_1, 4_1_8_3, 4_6_6_7, 6_5_8_5, 6_6_4_7,
7_2_7_3, 9_0_6_1, 9_3_8_3, 1_0_4_2_8, 1_0_9_2_9, 1_1_9_3_8, 1_2_0_3_3, 1_2_3_3_1, 1_2_5_6_2, 1_3_7_9_3,
1_4_1_5_7, 1_4_6_3_5, 1_5_2_6_5, 1_5_6_1_8, 1_6_5_5_3, 1_6_6_0_4, 1_8_3_6_2, 1_8_9_5_6, 2_0_0_7_5, 2_1_6_7_5,
2_2_5_2_0, 2_6_1_3_0, 2_6_1_6_1, 2_6_4_3_5, 2_8_2_7_9, 2_9_4_6_4, 3_1_6_5_0, 3_2_3_0_2, 3_2_4_7_0, 3_6_8_6_5,
4_2_8_6_3, 4_7_4_2_5, 4_9_8_7_0, 5_0_2_5_4, 5_0_2_5_8, 5_0_3_6_0, 5_0_3_6_1, 5_0_3_6_2
]
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'whisper'
lowercase = ['past_key_values']
lowercase = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , A=51865 , A=80 , A=6 , A=4 , A=6 , A=4 , A=1536 , A=1536 , A=0.0 , A=0.0 , A=50257 , A=True , A=True , A="gelu" , A=256 , A=0.0 , A=0.0 , A=0.0 , A=0.0_2 , A=False , A=1500 , A=448 , A=50256 , A=50256 , A=50256 , A=None , A=[220, 50256] , A=False , A=256 , A=False , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A=7 , **A , ) -> Optional[Any]:
UpperCAmelCase : str = vocab_size
UpperCAmelCase : Union[str, Any] = num_mel_bins
UpperCAmelCase : Tuple = d_model
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : List[str] = encoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_layers
UpperCAmelCase : int = decoder_attention_heads
UpperCAmelCase : Optional[int] = decoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : List[str] = dropout
UpperCAmelCase : Optional[Any] = attention_dropout
UpperCAmelCase : Optional[Any] = activation_dropout
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[Any] = init_std
UpperCAmelCase : int = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : Optional[int] = use_cache
UpperCAmelCase : List[str] = encoder_layers
UpperCAmelCase : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Union[str, Any] = max_source_positions
UpperCAmelCase : Tuple = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase : List[str] = classifier_proj_size
UpperCAmelCase : Optional[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase : Optional[Any] = apply_spec_augment
UpperCAmelCase : int = mask_time_prob
UpperCAmelCase : int = mask_time_length
UpperCAmelCase : Dict = mask_time_min_masks
UpperCAmelCase : List[str] = mask_feature_prob
UpperCAmelCase : Optional[int] = mask_feature_length
UpperCAmelCase : int = mask_feature_min_masks
UpperCAmelCase : List[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class UpperCamelCase_ ( __magic_name__ ):
@property
def _lowercase( self ) -> Mapping[str, Mapping[int, str]]:
UpperCAmelCase : str = OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
UpperCAmelCase : List[Any] = {0: """batch"""}
else:
UpperCAmelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(A , direction="""inputs""" )
return common_inputs
def _lowercase( self , A , A = -1 , A = -1 , A = False , A = None , A = 22050 , A = 5.0 , A = 220 , ) -> Mapping[str, Any]:
UpperCAmelCase : Optional[int] = OrderedDict()
UpperCAmelCase : Any = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
UpperCAmelCase : List[str] = encoder_inputs["""input_features"""].shape[2]
UpperCAmelCase : List[Any] = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
UpperCAmelCase : List[str] = encoder_inputs.pop("""input_features""" )
UpperCAmelCase : Any = decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase : Union[str, Any] = decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def _lowercase( self ) -> float:
return 1e-3
| 338
| 1
|
"""simple docstring"""
from math import ceil, sqrt
def lowercase ( a__ : int = 1000000 ) -> int:
_UpperCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_UpperCamelCase = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
_UpperCamelCase = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 256
|
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCAmelCase__( lowercase : Dict , lowercase : bool = True , lowercase : float = math.inf , lowercase : float = -math.inf , lowercase : float = math.inf , lowercase : float = -math.inf , lowercase : bool = False , lowercase : float = 100 , lowercase : float = 0.0_1 , lowercase : float = 1 , ) -> Any:
__snake_case : Optional[Any] = False
__snake_case : Optional[Any] = search_prob
__snake_case : str = start_temperate
__snake_case : List[Any] = []
__snake_case : str = 0
__snake_case : Dict = None
while not search_end:
__snake_case : List[Any] = current_state.score()
if best_state is None or current_score > best_state.score():
__snake_case : List[Any] = current_state
scores.append(lowercase )
iterations += 1
__snake_case : Dict = None
__snake_case : str = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
__snake_case : Any = random.randint(0 , len(lowercase ) - 1 ) # picking a random neighbor
__snake_case : int = neighbors.pop(lowercase )
__snake_case : Optional[Any] = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
__snake_case : Any = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
__snake_case : List[str] = picked_neighbor
else:
__snake_case : Optional[Any] = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
__snake_case : str = picked_neighbor
__snake_case : Optional[Any] = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
__snake_case : Optional[Any] = True
else:
__snake_case : str = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(lowercase ) , lowercase )
plt.xlabel("Iterations" )
plt.ylabel("Function values" )
plt.show()
return best_state
if __name__ == "__main__":
def lowerCAmelCase__( lowercase : List[str] , lowercase : Tuple ) -> str:
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
_UpperCamelCase = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
_UpperCamelCase = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
# starting the problem with initial coordinates (12, 47)
_UpperCamelCase = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
_UpperCamelCase = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
def lowerCAmelCase__( lowercase : Any , lowercase : Union[str, Any] ) -> Any:
return (3 * x**2) - (6 * y)
_UpperCamelCase = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
_UpperCamelCase = simulated_annealing(prob, find_max=False, visualization=True)
print(
'''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
F'''{local_min.score()}'''
)
_UpperCamelCase = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
_UpperCamelCase = simulated_annealing(prob, find_max=True, visualization=True)
print(
'''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
F'''{local_min.score()}'''
)
| 326
| 0
|
'''simple docstring'''
def _a( UpperCamelCase__ : int, UpperCamelCase__ : int ):
'''simple docstring'''
return base * power(UpperCamelCase__, (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
a_ = int(input('Enter the base: ').strip())
a_ = int(input('Enter the exponent: ').strip())
a_ = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
a_ = 1 / result
print(F'''{base} to the power of {exponent} is {result}''')
| 367
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 222
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
lowerCamelCase : Dict = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class __lowerCAmelCase (lowercase_ ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = """time_series_transformer"""
lowerCAmelCase__ : Optional[int] = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
"""num_hidden_layers""": """encoder_layers""",
}
def __init__(self : Any , UpperCamelCase : Optional[int] = None , UpperCamelCase : Optional[int] = None , UpperCamelCase : str = "student_t" , UpperCamelCase : str = "nll" , UpperCamelCase : int = 1 , UpperCamelCase : List[int] = [1, 2, 3, 4, 5, 6, 7] , UpperCamelCase : Optional[Union[str, bool]] = "mean" , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 , UpperCamelCase : int = 0 , UpperCamelCase : Optional[List[int]] = None , UpperCamelCase : Optional[List[int]] = None , UpperCamelCase : int = 32 , UpperCamelCase : int = 32 , UpperCamelCase : int = 2 , UpperCamelCase : int = 2 , UpperCamelCase : int = 2 , UpperCamelCase : int = 2 , UpperCamelCase : bool = True , UpperCamelCase : str = "gelu" , UpperCamelCase : int = 64 , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : float = 0.1 , UpperCamelCase : int = 100 , UpperCamelCase : float = 0.02 , UpperCamelCase : Tuple=True , **UpperCamelCase : Optional[Any] , ):
'''simple docstring'''
lowercase__ = prediction_length
lowercase__ = context_length or prediction_length
lowercase__ = distribution_output
lowercase__ = loss
lowercase__ = input_size
lowercase__ = num_time_features
lowercase__ = lags_sequence
lowercase__ = scaling
lowercase__ = num_dynamic_real_features
lowercase__ = num_static_real_features
lowercase__ = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(UpperCamelCase ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
lowercase__ = cardinality
else:
lowercase__ = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(UpperCamelCase ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
lowercase__ = embedding_dimension
else:
lowercase__ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowercase__ = num_parallel_samples
# Transformer architecture configuration
lowercase__ = input_size * len(UpperCamelCase ) + self._number_of_features
lowercase__ = d_model
lowercase__ = encoder_attention_heads
lowercase__ = decoder_attention_heads
lowercase__ = encoder_ffn_dim
lowercase__ = decoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = decoder_layers
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = use_cache
super().__init__(is_encoder_decoder=UpperCamelCase , **UpperCamelCase )
@property
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 2
|
'''simple docstring'''
from datetime import datetime
import requests
def _lowercase ( __A ):
'''simple docstring'''
__UpperCamelCase = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
__UpperCamelCase = requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(__A ).content
if __name__ == "__main__":
a__ : int = input('Enter Video/IGTV url: ').strip()
a__ : int = f'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, 'wb') as fp:
fp.write(download_video(url))
print(f'''Done. Video saved to disk as {file_name}.''')
| 349
| 0
|
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir('fixtures/test_sentencepiece_no_bos.model')
@require_sentencepiece
@require_tokenizers
class a__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = PegasusTokenizer
_SCREAMING_SNAKE_CASE : List[Any] = PegasusTokenizerFast
_SCREAMING_SNAKE_CASE : Union[str, Any] = True
_SCREAMING_SNAKE_CASE : int = True
def _lowerCamelCase ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_lowercase : List[str] = PegasusTokenizer(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _lowerCamelCase ( self ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def _lowerCamelCase ( self , **_UpperCamelCase ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
return ("This is a test", "This is a test")
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : int = "</s>"
_lowercase : str = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<pad>" )
self.assertEqual(vocab_keys[1] , "</s>" )
self.assertEqual(vocab_keys[-1] , "v" )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , 1103 )
def _lowerCamelCase ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Any = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_lowercase : Optional[Any] = self.tokenizer_class.from_pretrained(self.tmpdirname )
_lowercase : Optional[int] = (
"Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
_lowercase : Dict = rust_tokenizer([raw_input_str] , return_tensors=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids[0]
_lowercase : List[Any] = py_tokenizer([raw_input_str] , return_tensors=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids[0]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[int] = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
_lowercase : int = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
_lowercase : int = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
_lowercase : Optional[Any] = tokenizer([raw_input_str] , return_tensors=_SCREAMING_SNAKE_CASE ).input_ids[0]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : str = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
_lowercase : Union[str, Any] = "To ensure a smooth flow of bank resolutions."
_lowercase : Optional[int] = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
_lowercase : int = tokenizer([raw_input_str] , return_tensors=_SCREAMING_SNAKE_CASE ).input_ids[0]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[int] = ["This is going to be way too long." * 150, "short example"]
_lowercase : List[str] = ["not super long but more than 5 tokens", "tiny"]
_lowercase : Optional[int] = self._large_tokenizer(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
_lowercase : Optional[Any] = self._large_tokenizer(
text_target=_SCREAMING_SNAKE_CASE , max_length=5 , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(_SCREAMING_SNAKE_CASE ) == 2 # input_ids, attention_mask.
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[int] = {"input_ids": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_SCREAMING_SNAKE_CASE , model_name="google/bigbird-pegasus-large-arxiv" , revision="ba85d0851d708441f91440d509690f1ab6353415" , )
@require_sentencepiece
@require_tokenizers
class a__ ( _UpperCamelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : int = PegasusTokenizer
_SCREAMING_SNAKE_CASE : List[str] = PegasusTokenizerFast
_SCREAMING_SNAKE_CASE : Dict = True
_SCREAMING_SNAKE_CASE : Tuple = True
def _lowerCamelCase ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_lowercase : Dict = PegasusTokenizer(_SCREAMING_SNAKE_CASE , offset=0 , mask_token_sent=_SCREAMING_SNAKE_CASE , mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _lowerCamelCase ( self ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def _lowerCamelCase ( self , **_UpperCamelCase ):
"""simple docstring"""
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def _lowerCamelCase ( self , _UpperCamelCase ):
"""simple docstring"""
return ("This is a test", "This is a test")
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : int = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
_lowercase : List[str] = self.tokenizer_class.from_pretrained(self.tmpdirname )
_lowercase : Optional[Any] = (
"Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
_lowercase : List[str] = rust_tokenizer([raw_input_str] , return_tensors=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids[0]
_lowercase : Dict = py_tokenizer([raw_input_str] , return_tensors=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE ).input_ids[0]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@require_torch
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : int = ["This is going to be way too long." * 1000, "short example"]
_lowercase : Tuple = ["not super long but more than 5 tokens", "tiny"]
_lowercase : List[str] = self._large_tokenizer(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
_lowercase : str = self._large_tokenizer(
text_target=_SCREAMING_SNAKE_CASE , max_length=5 , padding=_SCREAMING_SNAKE_CASE , truncation=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(_SCREAMING_SNAKE_CASE ) == 2 # input_ids, attention_mask.
def _lowerCamelCase ( self ):
"""simple docstring"""
_lowercase : Optional[Any] = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
_lowercase : Tuple = self._large_tokenizer(_SCREAMING_SNAKE_CASE ).input_ids
self.assertListEqual(
_SCREAMING_SNAKE_CASE , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1] , )
| 368
|
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
_snake_case = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n year={2020},\n booktitle={Findings of EMNLP},\n}\n'
_snake_case = '\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'
_snake_case = '\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n predictions: list of predictions to score (as int64),\n except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n references: list of ground truth labels corresponding to the predictions (as int64),\n except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n "accuracy": Accuracy\n "f1": F1 score\n "precision": Precision@10\nExamples:\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\') # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'precision@10\': 1.0}\n\n'
def _A ( snake_case , snake_case ) -> int:
return float((preds == labels).mean() )
def _A ( snake_case , snake_case ) -> Union[str, Any]:
_lowercase : Any = simple_accuracy(snake_case , snake_case )
_lowercase : List[Any] = float(fa_score(y_true=snake_case , y_pred=snake_case ) )
return {
"accuracy": acc,
"f1": fa,
}
def _A ( snake_case , snake_case ) -> List[str]:
_lowercase : Any = np.array(snake_case )
_lowercase : Any = np.array(snake_case )
_lowercase : str = en_sentvecs.shape[0]
# mean centering
_lowercase : List[Any] = en_sentvecs - np.mean(snake_case , axis=0 )
_lowercase : Tuple = in_sentvecs - np.mean(snake_case , axis=0 )
_lowercase : Any = cdist(snake_case , snake_case , "cosine" )
_lowercase : Any = np.array(range(snake_case ) )
_lowercase : Dict = sim.argsort(axis=1 )[:, :10]
_lowercase : List[str] = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a__ ( datasets.Metric ):
def _lowerCamelCase ( self ):
"""simple docstring"""
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
"\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
"\"wiki-ner\"]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" )
if self.config_name != "cvit-mkb-clsr"
else datasets.Sequence(datasets.Value("float32" ) ),
"references": datasets.Value("int64" )
if self.config_name != "cvit-mkb-clsr"
else datasets.Sequence(datasets.Value("float32" ) ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" if self.config_name != "cvit-mkb-clsr" else None , )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(_UpperCamelCase , _UpperCamelCase )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(_UpperCamelCase , _UpperCamelCase )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(_UpperCamelCase , _UpperCamelCase )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
"\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
"\"wiki-ner\"]" )
| 199
| 0
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_xmod": [
"XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XmodConfig",
"XmodOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
"XmodForCausalLM",
"XmodForMaskedLM",
"XmodForMultipleChoice",
"XmodForQuestionAnswering",
"XmodForSequenceClassification",
"XmodForTokenClassification",
"XmodModel",
"XmodPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 26
|
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMSNForImageClassification, ViTMSNModel
from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __magic_name__ :
"""simple docstring"""
def __init__( self :Dict , snake_case :Optional[int] , snake_case :Tuple=13 , snake_case :List[Any]=30 , snake_case :Union[str, Any]=2 , snake_case :List[Any]=3 , snake_case :Tuple=True , snake_case :Dict=True , snake_case :Dict=32 , snake_case :List[str]=5 , snake_case :Optional[Any]=4 , snake_case :Any=37 , snake_case :Dict="gelu" , snake_case :List[str]=0.1 , snake_case :str=0.1 , snake_case :Tuple=10 , snake_case :str=0.02 , snake_case :Optional[Any]=None , ):
'''simple docstring'''
A_ : Tuple = parent
A_ : int = batch_size
A_ : List[str] = image_size
A_ : List[Any] = patch_size
A_ : Optional[Any] = num_channels
A_ : List[Any] = is_training
A_ : Tuple = use_labels
A_ : Union[str, Any] = hidden_size
A_ : Tuple = num_hidden_layers
A_ : Any = num_attention_heads
A_ : List[str] = intermediate_size
A_ : Optional[int] = hidden_act
A_ : List[str] = hidden_dropout_prob
A_ : str = attention_probs_dropout_prob
A_ : Any = type_sequence_label_size
A_ : List[str] = initializer_range
A_ : Dict = scope
# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
A_ : Optional[int] = (image_size // patch_size) ** 2
A_ : List[str] = num_patches + 1
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
A_ : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Tuple = None
if self.use_labels:
A_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Dict = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] ):
'''simple docstring'''
return ViTMSNConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE ( self :List[Any] , snake_case :List[Any] , snake_case :str , snake_case :Tuple ):
'''simple docstring'''
A_ : Optional[Any] = ViTMSNModel(config=snake_case )
model.to(snake_case )
model.eval()
A_ : int = model(snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self :int , snake_case :Optional[int] , snake_case :List[str] , snake_case :List[str] ):
'''simple docstring'''
A_ : Dict = self.type_sequence_label_size
A_ : Tuple = ViTMSNForImageClassification(snake_case )
model.to(snake_case )
model.eval()
A_ : Union[str, Any] = model(snake_case , labels=snake_case )
print("Pixel and labels shape: {pixel_values.shape}, {labels.shape}" )
print("Labels: {labels}" )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A_ : Union[str, Any] = 1
A_ : int = ViTMSNForImageClassification(snake_case )
model.to(snake_case )
model.eval()
A_ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : Optional[Any] = model(snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
A_ : List[str] = self.prepare_config_and_inputs()
A_ , A_ , A_ : Optional[int] = config_and_inputs
A_ : Dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
__UpperCamelCase = (
{'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def SCREAMING_SNAKE_CASE ( self :Union[str, Any] ):
'''simple docstring'''
A_ : Tuple = ViTMSNModelTester(self )
A_ : str = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMSN does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE ( self :Dict ):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
A_ , A_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Optional[int] = model_class(snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ : Optional[int] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case , nn.Linear ) )
def SCREAMING_SNAKE_CASE ( self :Optional[Any] ):
'''simple docstring'''
A_ , A_ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Optional[Any] = model_class(snake_case )
A_ : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : List[str] = [*signature.parameters.keys()]
A_ : List[Any] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case )
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
A_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
A_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@slow
def SCREAMING_SNAKE_CASE ( self :Any ):
'''simple docstring'''
for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Optional[Any] = ViTMSNModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def __snake_case ( ) -> Optional[Any]:
A_ : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self :str ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained("facebook/vit-msn-small" ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self :List[Any] ):
'''simple docstring'''
torch.manual_seed(2 )
A_ : Any = ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small" ).to(snake_case )
A_ : List[str] = self.default_image_processor
A_ : int = prepare_img()
A_ : List[str] = image_processor(images=snake_case , return_tensors="pt" ).to(snake_case )
# forward pass
with torch.no_grad():
A_ : Optional[int] = model(**snake_case )
# verify the logits
A_ : List[Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , snake_case )
A_ : int = torch.tensor([-0.0803, -0.4454, -0.2375] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1e-4 ) )
| 300
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
'''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
'''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
'''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
'''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
'''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
'''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
'''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
'''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}
class _a ( _lowercase):
_a : Dict = '''xmod'''
def __init__( self : Any , _SCREAMING_SNAKE_CASE : List[str]=3_0522 , _SCREAMING_SNAKE_CASE : Optional[int]=768 , _SCREAMING_SNAKE_CASE : List[str]=12 , _SCREAMING_SNAKE_CASE : str=12 , _SCREAMING_SNAKE_CASE : Dict=3072 , _SCREAMING_SNAKE_CASE : str="gelu" , _SCREAMING_SNAKE_CASE : List[str]=0.1 , _SCREAMING_SNAKE_CASE : int=0.1 , _SCREAMING_SNAKE_CASE : Any=512 , _SCREAMING_SNAKE_CASE : Dict=2 , _SCREAMING_SNAKE_CASE : Tuple=0.02 , _SCREAMING_SNAKE_CASE : Optional[int]=1E-12 , _SCREAMING_SNAKE_CASE : str=1 , _SCREAMING_SNAKE_CASE : List[Any]=0 , _SCREAMING_SNAKE_CASE : int=2 , _SCREAMING_SNAKE_CASE : str="absolute" , _SCREAMING_SNAKE_CASE : Dict=True , _SCREAMING_SNAKE_CASE : Optional[int]=None , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : Tuple=2 , _SCREAMING_SNAKE_CASE : int=False , _SCREAMING_SNAKE_CASE : Optional[Any]=True , _SCREAMING_SNAKE_CASE : Any=True , _SCREAMING_SNAKE_CASE : Optional[Any]=("en_XX",) , _SCREAMING_SNAKE_CASE : str=None , **_SCREAMING_SNAKE_CASE : Dict , )-> Union[str, Any]:
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = vocab_size
lowerCAmelCase__ : int = hidden_size
lowerCAmelCase__ : Tuple = num_hidden_layers
lowerCAmelCase__ : Any = num_attention_heads
lowerCAmelCase__ : List[Any] = hidden_act
lowerCAmelCase__ : str = intermediate_size
lowerCAmelCase__ : Optional[Any] = hidden_dropout_prob
lowerCAmelCase__ : List[str] = attention_probs_dropout_prob
lowerCAmelCase__ : Any = max_position_embeddings
lowerCAmelCase__ : List[str] = type_vocab_size
lowerCAmelCase__ : List[str] = initializer_range
lowerCAmelCase__ : Optional[int] = layer_norm_eps
lowerCAmelCase__ : List[Any] = position_embedding_type
lowerCAmelCase__ : Optional[int] = use_cache
lowerCAmelCase__ : str = classifier_dropout
lowerCAmelCase__ : Union[str, Any] = pre_norm
lowerCAmelCase__ : int = adapter_reduction_factor
lowerCAmelCase__ : Union[str, Any] = adapter_layer_norm
lowerCAmelCase__ : str = adapter_reuse_layer_norm
lowerCAmelCase__ : Tuple = ln_before_adapter
lowerCAmelCase__ : Any = list(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[Any] = default_language
class _a ( _lowercase):
@property
def UpperCAmelCase__( self : int )-> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase__ : str = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCAmelCase__ : List[str] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 211
|
import math
class _a :
def __init__( self : List[Any] , _SCREAMING_SNAKE_CASE : Any=0 )-> Optional[Any]: # a graph with Node 0,1,...,N-1
lowerCAmelCase__ : Optional[int] = n
lowerCAmelCase__ : List[Any] = [
[math.inf for j in range(0 , _SCREAMING_SNAKE_CASE )] for i in range(0 , _SCREAMING_SNAKE_CASE )
] # adjacency matrix for weight
lowerCAmelCase__ : str = [
[math.inf for j in range(0 , _SCREAMING_SNAKE_CASE )] for i in range(0 , _SCREAMING_SNAKE_CASE )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase__( self : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : str )-> List[str]:
lowerCAmelCase__ : Optional[int] = w
def UpperCAmelCase__( self : List[Any] )-> Optional[int]:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
lowerCAmelCase__ : Dict = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase__( self : str , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : str )-> str:
return self.dp[u][v]
if __name__ == "__main__":
lowerCamelCase = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 211
| 1
|
# Imports
import numpy as np
class lowercase_ :
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ) ->int:
self.set_matricies(red=__SCREAMING_SNAKE_CASE , green=__SCREAMING_SNAKE_CASE , blue=__SCREAMING_SNAKE_CASE , red_edge=__SCREAMING_SNAKE_CASE , nir=__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ) ->List[Any]:
if red is not None:
lowerCAmelCase = red
if green is not None:
lowerCAmelCase = green
if blue is not None:
lowerCAmelCase = blue
if red_edge is not None:
lowerCAmelCase = red_edge
if nir is not None:
lowerCAmelCase = nir
return True
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE="" , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ) ->Dict:
self.set_matricies(red=__SCREAMING_SNAKE_CASE , green=__SCREAMING_SNAKE_CASE , blue=__SCREAMING_SNAKE_CASE , red_edge=__SCREAMING_SNAKE_CASE , nir=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''' )
return False
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
return self.nir * (self.red / (self.green**2))
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
return (self.nir - self.red) / (self.nir + self.red)
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
return (self.nir - self.blue) / (self.nir + self.blue)
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
return (self.redEdge - self.red) / (self.redEdge + self.red)
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
return (self.nir - self.green) / (self.nir + self.green)
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[int]:
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE=0.0_8 , __SCREAMING_SNAKE_CASE=1.2_2 , __SCREAMING_SNAKE_CASE=0.0_3 ) ->List[str]:
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
return (self.nir / self.green) - 1
def SCREAMING_SNAKE_CASE_ ( self ) ->Dict:
return (self.nir / self.redEdge) - 1
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
return (self.red - self.blue) / self.red
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
lowerCAmelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
return self.nir - self.green
def SCREAMING_SNAKE_CASE_ ( self ) ->Union[str, Any]:
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
lowerCAmelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE=0.1_6 ) ->List[Any]:
return (self.nir - self.green) / (self.nir + self.green + y)
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE=0.5 ) ->Optional[Any]:
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None ) ->Tuple:
return (self.nir - b) / (a * self.red)
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
return (self.red + self.green + self.blue) / 3_0.5
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
return self.nir / self.red
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
return (self.rvi() - 1) / (self.rvi() + 1)
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def SCREAMING_SNAKE_CASE_ ( self ) ->Optional[Any]:
return self.green / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE_ ( self ) ->Union[str, Any]:
return self.nir / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
return self.red / (self.nir + self.red + self.green)
def SCREAMING_SNAKE_CASE_ ( self ) ->Union[str, Any]:
return (self.green - self.red) / (self.green + self.red)
def SCREAMING_SNAKE_CASE_ ( self ) ->Any:
return (self.red - self.green) / (self.red + self.green)
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
lowerCAmelCase = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
lowerCAmelCase = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
return self.nir / self.red
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
return (self.ndvi() + 0.5) ** (1 / 2)
def SCREAMING_SNAKE_CASE_ ( self ) ->Tuple:
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 338
|
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
@property
def SCREAMING_SNAKE_CASE_ ( self ) ->List[str]:
torch.manual_seed(0 )
lowerCAmelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def SCREAMING_SNAKE_CASE_ ( self ) ->int:
lowerCAmelCase = self.dummy_uncond_unet
lowerCAmelCase = KarrasVeScheduler()
lowerCAmelCase = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(num_inference_steps=2 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' , return_dict=__SCREAMING_SNAKE_CASE )[0]
lowerCAmelCase = image[0, -3:, -3:, -1]
lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
lowerCAmelCase = '''google/ncsnpp-celebahq-256'''
lowerCAmelCase = UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE )
lowerCAmelCase = KarrasVeScheduler()
lowerCAmelCase = KarrasVePipeline(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE )
pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
lowerCAmelCase = torch.manual_seed(0 )
lowerCAmelCase = pipe(num_inference_steps=20 , generator=__SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCAmelCase = np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 338
| 1
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class lowerCamelCase__ ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
@register_to_config
def __init__( self , __UpperCAmelCase = 7_68 , ) -> Dict:
super().__init__()
_lowerCAmelCase =nn.Parameter(torch.zeros(1 , __UpperCAmelCase ) )
_lowerCAmelCase =nn.Parameter(torch.ones(1 , __UpperCAmelCase ) )
def _lowerCAmelCase ( self , __UpperCAmelCase = None , __UpperCAmelCase = None , ) -> Any:
_lowerCAmelCase =nn.Parameter(self.mean.to(__UpperCAmelCase ).to(__UpperCAmelCase ) )
_lowerCAmelCase =nn.Parameter(self.std.to(__UpperCAmelCase ).to(__UpperCAmelCase ) )
return self
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Union[str, Any]:
_lowerCAmelCase =(embeds - self.mean) * 1.0 / self.std
return embeds
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Any:
_lowerCAmelCase =(embeds * self.std) + self.mean
return embeds
| 341
|
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
# warning at import time
warnings.warn(
'''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
'''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''' , __magic_name__ , )
| 341
| 1
|
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
A ='0.12' # assumed parallelism: 8
if is_torch_available():
import torch
def snake_case_ (_a : str , _a : Optional[Any] , _a : Dict=None ):
if rng is None:
UpperCAmelCase = random.Random()
UpperCAmelCase = 1
for dim in shape:
total_dims *= dim
UpperCAmelCase = []
for _ in range(_a ):
values.append(rng.randint(0 , vocab_size - 1 ) )
UpperCAmelCase = np.array(_a , dtype=jnp.intaa ).reshape(_a )
return output
def snake_case_ (_a : Any , _a : int=None ):
UpperCAmelCase = ids_tensor(_a , vocab_size=2 , rng=_a )
# make sure that at least one token is attended to for each batch
UpperCAmelCase = 1
return attn_mask
@require_flax
class _a :
__a : Dict = None
__a : List[Any] = ()
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
UpperCAmelCase = 2
UpperCAmelCase = inputs['''input_ids'''].shape[-1] // 2
UpperCAmelCase = inputs['''input_ids'''][:max_batch_size, :sequence_length]
UpperCAmelCase = jnp.ones_like(lowercase )
UpperCAmelCase = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
UpperCAmelCase = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
UpperCAmelCase = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def A ( self : Dict ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 0
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model_class.__name__[4:] # Skip the "Flax" at the beginning
UpperCAmelCase = getattr(lowercase , lowercase )
UpperCAmelCase = pt_model_class(lowercase ).eval()
UpperCAmelCase = load_flax_weights_in_pytorch_model(lowercase , flax_model.params )
UpperCAmelCase = flax_model.generate(lowercase ).sequences
UpperCAmelCase = pt_model.generate(torch.tensor(lowercase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
UpperCAmelCase = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = True
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = False
UpperCAmelCase = max_length
UpperCAmelCase = 2
UpperCAmelCase = 2
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def A ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = True
UpperCAmelCase = max_length
UpperCAmelCase = 0.8
UpperCAmelCase = 10
UpperCAmelCase = 0.3
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = max_length
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
UpperCAmelCase = max_length
UpperCAmelCase = 2
UpperCAmelCase = 1
UpperCAmelCase = 8
UpperCAmelCase = 9
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = False
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase , attention_mask=lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase , attention_mask=lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = True
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase , attention_mask=lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase , attention_mask=lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def A ( self : int ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = self._get_input_ids_and_config()
# pad attention mask on the left
UpperCAmelCase = attention_mask.at[(0, 0)].set(0 )
UpperCAmelCase = 2
UpperCAmelCase = max_length
for model_class in self.all_generative_model_classes:
UpperCAmelCase = model_class(lowercase )
UpperCAmelCase = model.generate(lowercase , attention_mask=lowercase ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowercase )
UpperCAmelCase = jit(model.generate )
UpperCAmelCase = jit_generate(lowercase , attention_mask=lowercase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class _a ( unittest.TestCase ):
def A ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
UpperCAmelCase = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
UpperCAmelCase = '''Hello world'''
UpperCAmelCase = tokenizer(lowercase , return_tensors='''np''' ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(lowercase , '''do_samples''' ):
model.generate(lowercase , do_samples=lowercase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(lowercase , '''foo''' ):
UpperCAmelCase = {'''foo''': '''bar'''}
model.generate(lowercase , **lowercase )
| 34
|
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : int = IFInpaintingPipeline
__lowercase : str = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
__lowercase : Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__lowercase : Optional[int] = PipelineTesterMixin.required_optional_params - {"latents"}
def __UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
return self._get_dummy_components()
def __UpperCamelCase ( self , A_ , A_=0 ) -> List[Any]:
"""simple docstring"""
if str(A_ ).startswith('mps' ):
UpperCamelCase = torch.manual_seed(A_ )
else:
UpperCamelCase = torch.Generator(device=A_ ).manual_seed(A_ )
UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A_ ) ).to(A_ )
UpperCamelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(A_ ) ).to(A_ )
UpperCamelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def __UpperCamelCase ( self ) -> str:
"""simple docstring"""
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
self._test_save_load_local()
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 222
| 0
|
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[str]:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError('only integers accepted as input' )
else:
lowercase__ = str(abs(_SCREAMING_SNAKE_CASE ) )
lowercase__ = [list(_SCREAMING_SNAKE_CASE ) for char in range(len(_SCREAMING_SNAKE_CASE ) )]
for index in range(len(_SCREAMING_SNAKE_CASE ) ):
num_transpositions[index].pop(_SCREAMING_SNAKE_CASE )
return max(
int(''.join(list(_SCREAMING_SNAKE_CASE ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 356
|
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowercase_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
"""convert""": ["""export""", """validate_model_outputs"""],
"""features""": ["""FeaturesManager"""],
"""utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 269
| 0
|
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
@slow
@require_torch
def SCREAMING_SNAKE_CASE ( self : Tuple) ->List[Any]:
'''simple docstring'''
A__ = EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' , '''prajjwal1/bert-tiny''')
A__ = BertTokenizer.from_pretrained('''bert-base-uncased''')
A__ = bertabert.config.encoder.vocab_size
A__ = tokenizer.sep_token_id
A__ = tokenizer.cls_token_id
A__ = 128
A__ = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''train[:1%]''')
A__ = datasets.load_dataset('''cnn_dailymail''' , '''3.0.0''' , split='''validation[:1%]''')
A__ = train_dataset.select(range(32))
A__ = val_dataset.select(range(16))
A__ = 4
def _map_to_encoder_decoder_inputs(UpperCAmelCase__ : List[str]):
# Tokenizer will automatically set [BOS] <text> [EOS]
A__ = tokenizer(batch['''article'''] , padding='''max_length''' , truncation=UpperCAmelCase__ , max_length=512)
A__ = tokenizer(batch['''highlights'''] , padding='''max_length''' , truncation=UpperCAmelCase__ , max_length=128)
A__ = inputs.input_ids
A__ = inputs.attention_mask
A__ = outputs.input_ids
A__ = outputs.input_ids.copy()
A__ = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
]
A__ = outputs.attention_mask
assert all(len(UpperCAmelCase__) == 512 for x in inputs.input_ids)
assert all(len(UpperCAmelCase__) == 128 for x in outputs.input_ids)
return batch
def _compute_metrics(UpperCAmelCase__ : Tuple):
A__ = pred.label_ids
A__ = pred.predictions
# all unnecessary tokens are removed
A__ = tokenizer.batch_decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__)
A__ = tokenizer.batch_decode(UpperCAmelCase__ , skip_special_tokens=UpperCAmelCase__)
A__ = sum([int(pred_str[i] == label_str[i]) for i in range(len(UpperCAmelCase__))]) / len(UpperCAmelCase__)
return {"accuracy": accuracy}
# map train dataset
A__ = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , remove_columns=['''article''', '''highlights'''] , )
train_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
# same for validation dataset
A__ = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , remove_columns=['''article''', '''highlights'''] , )
val_dataset.set_format(
type='''torch''' , columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] , )
A__ = self.get_auto_remove_tmp_dir()
A__ = SeqaSeqTrainingArguments(
output_dir=UpperCAmelCase__ , per_device_train_batch_size=UpperCAmelCase__ , per_device_eval_batch_size=UpperCAmelCase__ , predict_with_generate=UpperCAmelCase__ , evaluation_strategy='''steps''' , do_train=UpperCAmelCase__ , do_eval=UpperCAmelCase__ , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
A__ = SeqaSeqTrainer(
model=UpperCAmelCase__ , args=UpperCAmelCase__ , compute_metrics=_compute_metrics , train_dataset=UpperCAmelCase__ , eval_dataset=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , )
# start training
trainer.train()
| 14
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( UpperCamelCase_ ):
UpperCamelCase__ : Any =(DPMSolverSinglestepScheduler,)
UpperCamelCase__ : Tuple =(('num_inference_steps', 25),)
def lowerCamelCase ( self : Optional[Any] , **lowercase_ : List[Any] ) -> Optional[Any]:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] ={
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**lowercase_ )
return config
def lowerCamelCase ( self : Optional[int] , lowercase_ : Optional[Any]=0 , **lowercase_ : Any ) -> List[Any]:
"""simple docstring"""
_lowerCamelCase : List[Any] =dict(self.forward_default_kwargs )
_lowerCamelCase : Optional[Any] =kwargs.pop('num_inference_steps' , lowercase_ )
_lowerCamelCase : Optional[int] =self.dummy_sample
_lowerCamelCase : Dict =0.1 * sample
_lowerCamelCase : Optional[int] =[residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCamelCase : int =self.get_scheduler_config(**lowercase_ )
_lowerCamelCase : str =scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
_lowerCamelCase : int =dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
_lowerCamelCase : Dict =scheduler_class.from_pretrained(lowercase_ )
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
_lowerCamelCase : str =dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCamelCase , _lowerCamelCase : Any =sample, sample
for t in range(lowercase_ , time_step + scheduler.config.solver_order + 1 ):
_lowerCamelCase : List[str] =scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
_lowerCamelCase : Optional[Any] =new_scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
pass
def lowerCamelCase ( self : str , lowercase_ : str=0 , **lowercase_ : List[Any] ) -> Dict:
"""simple docstring"""
_lowerCamelCase : int =dict(self.forward_default_kwargs )
_lowerCamelCase : Dict =kwargs.pop('num_inference_steps' , lowercase_ )
_lowerCamelCase : Optional[int] =self.dummy_sample
_lowerCamelCase : Any =0.1 * sample
_lowerCamelCase : int =[residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCamelCase : Optional[int] =self.get_scheduler_config()
_lowerCamelCase : Any =scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals (must be after setting timesteps)
_lowerCamelCase : Dict =dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
_lowerCamelCase : Optional[int] =scheduler_class.from_pretrained(lowercase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residual (must be after setting timesteps)
_lowerCamelCase : str =dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCamelCase : Dict =scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
_lowerCamelCase : Dict =new_scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def lowerCamelCase ( self : Any , lowercase_ : Union[str, Any]=None , **lowercase_ : List[str] ) -> str:
"""simple docstring"""
if scheduler is None:
_lowerCamelCase : Tuple =self.scheduler_classes[0]
_lowerCamelCase : Optional[int] =self.get_scheduler_config(**lowercase_ )
_lowerCamelCase : Union[str, Any] =scheduler_class(**lowercase_ )
_lowerCamelCase : List[Any] =self.scheduler_classes[0]
_lowerCamelCase : Optional[Any] =self.get_scheduler_config(**lowercase_ )
_lowerCamelCase : List[Any] =scheduler_class(**lowercase_ )
_lowerCamelCase : str =10
_lowerCamelCase : Union[str, Any] =self.dummy_model()
_lowerCamelCase : Dict =self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.timesteps ):
_lowerCamelCase : List[str] =model(lowercase_ , lowercase_ )
_lowerCamelCase : Optional[int] =scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
return sample
def lowerCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_lowerCamelCase : Any =DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_lowerCamelCase : int =50
_lowerCamelCase : Optional[int] =self.dummy_model()
_lowerCamelCase : Tuple =self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_lowerCamelCase : Any =model(lowercase_ , lowercase_ )
_lowerCamelCase : List[str] =scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
_lowerCamelCase : int =torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def lowerCamelCase ( self : Any ) -> List[str]:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def lowerCamelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
_lowerCamelCase : Optional[int] =DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_lowerCamelCase : Optional[Any] =self.full_loop(scheduler=lowercase_ )
_lowerCamelCase : Tuple =torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
_lowerCamelCase : Dict =DEISMultistepScheduler.from_config(scheduler.config )
_lowerCamelCase : Optional[int] =DPMSolverMultistepScheduler.from_config(scheduler.config )
_lowerCamelCase : Union[str, Any] =UniPCMultistepScheduler.from_config(scheduler.config )
_lowerCamelCase : List[str] =DPMSolverSinglestepScheduler.from_config(scheduler.config )
_lowerCamelCase : List[Any] =self.full_loop(scheduler=lowercase_ )
_lowerCamelCase : List[str] =torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def lowerCamelCase ( self : str ) -> Dict:
"""simple docstring"""
self.check_over_configs(thresholding=lowercase_ )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowercase_ , prediction_type=lowercase_ , sample_max_value=lowercase_ , algorithm_type='dpmsolver++' , solver_order=lowercase_ , solver_type=lowercase_ , )
def lowerCamelCase ( self : Any ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def lowerCamelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowercase_ , solver_type=lowercase_ , prediction_type=lowercase_ , algorithm_type=lowercase_ , )
_lowerCamelCase : Any =self.full_loop(
solver_order=lowercase_ , solver_type=lowercase_ , prediction_type=lowercase_ , algorithm_type=lowercase_ , )
assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"
def lowerCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
self.check_over_configs(lower_order_final=lowercase_ )
self.check_over_configs(lower_order_final=lowercase_ )
def lowerCamelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def lowerCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
self.check_over_configs(variance_type=lowercase_ )
self.check_over_configs(variance_type='learned_range' )
def lowerCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=lowercase_ , time_step=0 )
def lowerCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : Dict =self.full_loop()
_lowerCamelCase : Dict =torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def lowerCamelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : str =self.full_loop(use_karras_sigmas=lowercase_ )
_lowerCamelCase : Optional[Any] =torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def lowerCamelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
_lowerCamelCase : Any =self.full_loop(prediction_type='v_prediction' )
_lowerCamelCase : Dict =torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def lowerCamelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase : str =self.full_loop(prediction_type='v_prediction' , use_karras_sigmas=lowercase_ )
_lowerCamelCase : List[str] =torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def lowerCamelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_lowerCamelCase : List[str] =self.scheduler_classes[0]
_lowerCamelCase : List[str] =self.get_scheduler_config(thresholding=lowercase_ , dynamic_thresholding_ratio=0 )
_lowerCamelCase : List[Any] =scheduler_class(**lowercase_ )
_lowerCamelCase : Optional[Any] =10
_lowerCamelCase : Optional[int] =self.dummy_model()
_lowerCamelCase : List[Any] =self.dummy_sample_deter.half()
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.timesteps ):
_lowerCamelCase : str =model(lowercase_ , lowercase_ )
_lowerCamelCase : Any =scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
assert sample.dtype == torch.floataa
| 199
| 0
|
def lowerCamelCase ( a_ ) -> int:
assert column_title.isupper()
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(a_ ) - 1
lowerCAmelCase_ = 0
while index >= 0:
lowerCAmelCase_ = (ord(column_title[index] ) - 64) * pow(26 , a_ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 14
|
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
lowerCamelCase_ = """pytorch_model.bin"""
lowerCamelCase_ = """pytorch_model.bin.index.json"""
lowerCamelCase_ = """adapter_config.json"""
lowerCamelCase_ = """adapter_model.bin"""
lowerCamelCase_ = """adapter_model.safetensors"""
lowerCamelCase_ = """tf_model.h5"""
lowerCamelCase_ = """tf_model.h5.index.json"""
lowerCamelCase_ = """model.ckpt"""
lowerCamelCase_ = """flax_model.msgpack"""
lowerCamelCase_ = """flax_model.msgpack.index.json"""
lowerCamelCase_ = """model.safetensors"""
lowerCamelCase_ = """model.safetensors.index.json"""
lowerCamelCase_ = """config.json"""
lowerCamelCase_ = """preprocessor_config.json"""
lowerCamelCase_ = FEATURE_EXTRACTOR_NAME
lowerCamelCase_ = """generation_config.json"""
lowerCamelCase_ = """modelcard.json"""
lowerCamelCase_ = """▁"""
lowerCamelCase_ = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
lowerCamelCase_ = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCamelCase_ = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCamelCase_ = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def lowerCamelCase ( a_ ) -> Dict:
if version.parse(a_ ) < version.parse(a_ ):
if "dev" in min_version:
lowerCAmelCase_ = (
'This example requires a source install from HuggingFace Transformers (see '
'`https://huggingface.co/docs/transformers/installation#install-from-source`),'
)
else:
lowerCAmelCase_ = F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
'versions of HuggingFace Transformers.' )
| 14
| 1
|
'''simple docstring'''
from math import isqrt
def lowerCAmelCase (__A):
"""simple docstring"""
_a = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , __A , __A):
_a = False
return [i for i in range(2 , __A) if is_prime[i]]
def lowerCAmelCase (__A = 10**8):
"""simple docstring"""
_a = calculate_prime_numbers(max_number // 2)
_a = 0
_a = 0
_a = len(__A) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 211
|
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowercase_ = datasets.utils.logging.get_logger(__name__)
class __A ( folder_based_builder.FolderBasedBuilderConfig ):
'''simple docstring'''
__lowerCamelCase : bool = None
__lowerCamelCase : bool = None
class __A ( folder_based_builder.FolderBasedBuilder ):
'''simple docstring'''
__lowerCamelCase : int = datasets.Audio()
__lowerCamelCase : str = 'audio'
__lowerCamelCase : Optional[Any] = AudioFolderConfig
__lowerCamelCase : List[str] # definition at the bottom of the script
__lowerCamelCase : Union[str, Any] = AudioClassification(audio_column='audio' , label_column='label' )
lowercase_ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
lowercase_ = AUDIO_EXTENSIONS
| 211
| 1
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Dict = ["flax"]
def __init__( self , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> str:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Dict = ["flax"]
def __init__( self , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Optional[Any] = ["flax"]
def __init__( self , *a__ , **a__ ) -> str:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Optional[Any] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : int = ["flax"]
def __init__( self , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Any:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Optional[Any] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Any:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> int:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Dict = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> List[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : int = ["flax"]
def __init__( self , *a__ , **a__ ) -> Dict:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> int:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Any = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Tuple:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> str:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : str = ["flax"]
def __init__( self , *a__ , **a__ ) -> List[str]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
class _snake_case ( metaclass=lowercase_ ):
lowerCAmelCase_ : Tuple = ["flax"]
def __init__( self , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(self , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> int:
'''simple docstring'''
requires_backends(cls , ["flax"] )
@classmethod
def lowerCAmelCase__ ( cls , *a__ , **a__ ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls , ["flax"] )
| 92
|
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class _snake_case ( lowercase_ , lowercase_ , unittest.TestCase ):
lowerCAmelCase_ : List[str] = IFPipeline
lowerCAmelCase_ : int = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
lowerCAmelCase_ : Optional[int] = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase_ : List[Any] = PipelineTesterMixin.required_optional_params - {"latents"}
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
return self._get_dummy_components()
def lowerCAmelCase__ ( self , a__ , a__=0 ) -> str:
'''simple docstring'''
if str(a__ ).startswith("mps" ):
snake_case_ = torch.manual_seed(a__ )
else:
snake_case_ = torch.Generator(device=a__ ).manual_seed(a__ )
snake_case_ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
self._test_save_load_local()
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0" , variant="fp16" , torch_dtype=torch.floataa )
snake_case_ = IFSuperResolutionPipeline.from_pretrained(
"DeepFloyd/IF-II-L-v1.0" , variant="fp16" , torch_dtype=torch.floataa , text_encoder=a__ , tokenizer=a__ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("cuda" )
snake_case_ , snake_case_ = pipe_a.encode_prompt("anime turtle" , device="cuda" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
snake_case_ = None
snake_case_ = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(a__ , a__ , a__ , a__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
snake_case_ = IFImgaImgPipeline(**pipe_a.components )
snake_case_ = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(a__ , a__ , a__ , a__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
snake_case_ = IFInpaintingPipeline(**pipe_a.components )
snake_case_ = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(a__ , a__ , a__ , a__ )
def lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ ) -> Dict:
'''simple docstring'''
_start_torch_memory_measurement()
snake_case_ = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ = pipe_a(
prompt_embeds=a__ , negative_prompt_embeds=a__ , num_inference_steps=2 , generator=a__ , output_type="np" , )
snake_case_ = output.images[0]
assert image.shape == (64, 64, 3)
snake_case_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
snake_case_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy" )
assert_mean_pixel_difference(a__ , a__ )
# pipeline 2
_start_torch_memory_measurement()
snake_case_ = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(a__ )
snake_case_ = pipe_a(
prompt_embeds=a__ , negative_prompt_embeds=a__ , image=a__ , generator=a__ , num_inference_steps=2 , output_type="np" , )
snake_case_ = output.images[0]
assert image.shape == (256, 256, 3)
snake_case_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
snake_case_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy" )
assert_mean_pixel_difference(a__ , a__ )
def lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ ) -> Dict:
'''simple docstring'''
_start_torch_memory_measurement()
snake_case_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(a__ )
snake_case_ = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ = pipe_a(
prompt_embeds=a__ , negative_prompt_embeds=a__ , image=a__ , num_inference_steps=2 , generator=a__ , output_type="np" , )
snake_case_ = output.images[0]
assert image.shape == (64, 64, 3)
snake_case_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
snake_case_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy" )
assert_mean_pixel_difference(a__ , a__ )
# pipeline 2
_start_torch_memory_measurement()
snake_case_ = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(a__ )
snake_case_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(a__ )
snake_case_ = pipe_a(
prompt_embeds=a__ , negative_prompt_embeds=a__ , image=a__ , original_image=a__ , generator=a__ , num_inference_steps=2 , output_type="np" , )
snake_case_ = output.images[0]
assert image.shape == (256, 256, 3)
snake_case_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
snake_case_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy" )
assert_mean_pixel_difference(a__ , a__ )
def lowerCAmelCase__ ( self , a__ , a__ , a__ , a__ ) -> str:
'''simple docstring'''
_start_torch_memory_measurement()
snake_case_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(a__ )
snake_case_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(a__ )
snake_case_ = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ = pipe_a(
prompt_embeds=a__ , negative_prompt_embeds=a__ , image=a__ , mask_image=a__ , num_inference_steps=2 , generator=a__ , output_type="np" , )
snake_case_ = output.images[0]
assert image.shape == (64, 64, 3)
snake_case_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
snake_case_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy" )
assert_mean_pixel_difference(a__ , a__ )
# pipeline 2
_start_torch_memory_measurement()
snake_case_ = torch.Generator(device="cpu" ).manual_seed(0 )
snake_case_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(a__ )
snake_case_ = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(a__ )
snake_case_ = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(a__ )
snake_case_ = pipe_a(
prompt_embeds=a__ , negative_prompt_embeds=a__ , image=a__ , mask_image=a__ , original_image=a__ , generator=a__ , num_inference_steps=2 , output_type="np" , )
snake_case_ = output.images[0]
assert image.shape == (256, 256, 3)
snake_case_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
snake_case_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy" )
assert_mean_pixel_difference(a__ , a__ )
def UpperCamelCase_( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 92
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'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _lowerCAmelCase ( __snake_case , __snake_case ):
'''simple docstring'''
@register_to_config
def __init__(self , UpperCAmelCase = 768 , ) -> Union[str, Any]:
super().__init__()
_snake_case = nn.Parameter(torch.zeros(1 , UpperCAmelCase ) )
_snake_case = nn.Parameter(torch.ones(1 , UpperCAmelCase ) )
def lowercase (self , UpperCAmelCase = None , UpperCAmelCase = None , ) -> Union[str, Any]:
_snake_case = nn.Parameter(self.mean.to(UpperCAmelCase ).to(UpperCAmelCase ) )
_snake_case = nn.Parameter(self.std.to(UpperCAmelCase ).to(UpperCAmelCase ) )
return self
def lowercase (self , UpperCAmelCase ) -> Optional[Any]:
_snake_case = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowercase (self , UpperCAmelCase ) -> Optional[int]:
_snake_case = (embeds * self.std) + self.mean
return embeds
| 341
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'''simple docstring'''
from scipy.stats import spearmanr
import datasets
__lowerCAmelCase = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'
__lowerCAmelCase = '\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {\'spearmanr\': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results[\'spearmanr\'])\n -0.7\n >>> print(round(results[\'spearmanr_pvalue\'], 2))\n 0.19\n'
__lowerCAmelCase = r'\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
'''simple docstring'''
def lowercase (self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ) , reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""] , )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=False ) -> Optional[Any]:
_snake_case = spearmanr(UpperCAmelCase , UpperCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 341
| 1
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'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_UpperCamelCase = logging.get_logger(__name__)
class _A ( __SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE : List[str] = ["pixel_values"]
def __init__( self , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = True , __UpperCAmelCase = None , __UpperCAmelCase = True , __UpperCAmelCase = 1 / 255 , __UpperCAmelCase = True , __UpperCAmelCase = IMAGENET_DEFAULT_MEAN , __UpperCAmelCase = IMAGENET_DEFAULT_STD , **__UpperCAmelCase , ) -> None:
'''simple docstring'''
super().__init__(**__UpperCAmelCase )
__UpperCAmelCase : str = size if size is not None else {"""shortest_edge""": 224}
__UpperCAmelCase : Dict = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
__UpperCAmelCase : Any = get_size_dict(__UpperCAmelCase , param_name="""crop_size""" )
__UpperCAmelCase : Union[str, Any] = do_resize
__UpperCAmelCase : Optional[Any] = size
__UpperCAmelCase : List[Any] = resample
__UpperCAmelCase : Dict = do_center_crop
__UpperCAmelCase : List[str] = crop_size
__UpperCAmelCase : List[str] = do_rescale
__UpperCAmelCase : List[str] = rescale_factor
__UpperCAmelCase : Dict = do_normalize
__UpperCAmelCase : List[Any] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
__UpperCAmelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = PILImageResampling.BICUBIC , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
__UpperCAmelCase : Optional[Any] = int((256 / 224) * size["""shortest_edge"""] )
__UpperCAmelCase : str = get_resize_output_image_size(__UpperCAmelCase , size=__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__UpperCAmelCase : str = {"""height""": output_size[0], """width""": output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}' )
return resize(
__UpperCAmelCase , size=(size_dict["""height"""], size_dict["""width"""]) , resample=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
'''simple docstring'''
__UpperCAmelCase : Dict = get_size_dict(__UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}' )
return center_crop(__UpperCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
'''simple docstring'''
return rescale(__UpperCAmelCase , scale=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , **__UpperCAmelCase , ) -> np.ndarray:
'''simple docstring'''
return normalize(__UpperCAmelCase , mean=__UpperCAmelCase , std=__UpperCAmelCase , data_format=__UpperCAmelCase , **__UpperCAmelCase )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = ChannelDimension.FIRST , **__UpperCAmelCase , ) -> BatchFeature:
'''simple docstring'''
__UpperCAmelCase : str = do_resize if do_resize is not None else self.do_resize
__UpperCAmelCase : Optional[Any] = resample if resample is not None else self.resample
__UpperCAmelCase : str = do_center_crop if do_center_crop is not None else self.do_center_crop
__UpperCAmelCase : Tuple = do_rescale if do_rescale is not None else self.do_rescale
__UpperCAmelCase : str = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCAmelCase : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
__UpperCAmelCase : List[str] = image_mean if image_mean is not None else self.image_mean
__UpperCAmelCase : int = image_std if image_std is not None else self.image_std
__UpperCAmelCase : Any = size if size is not None else self.size
__UpperCAmelCase : Union[str, Any] = get_size_dict(__UpperCAmelCase , default_to_square=__UpperCAmelCase )
__UpperCAmelCase : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
__UpperCAmelCase : Dict = get_size_dict(__UpperCAmelCase , param_name="""crop_size""" )
__UpperCAmelCase : Dict = make_list_of_images(__UpperCAmelCase )
if not valid_images(__UpperCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
__UpperCAmelCase : Dict = [to_numpy_array(__UpperCAmelCase ) for image in images]
if do_resize:
__UpperCAmelCase : Dict = [self.resize(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) for image in images]
if do_center_crop:
__UpperCAmelCase : Tuple = [self.center_crop(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
if do_rescale:
__UpperCAmelCase : Optional[int] = [self.rescale(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
if do_normalize:
__UpperCAmelCase : str = [self.normalize(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) for image in images]
__UpperCAmelCase : Optional[int] = [to_channel_dimension_format(__UpperCAmelCase , __UpperCAmelCase ) for image in images]
__UpperCAmelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=__UpperCAmelCase , tensor_type=__UpperCAmelCase )
| 16
|
'''simple docstring'''
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class _A ( unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = MODEL_FOR_MASKED_LM_MAPPING
_SCREAMING_SNAKE_CASE : Tuple = TF_MODEL_FOR_MASKED_LM_MAPPING
def __A ( self ) -> Any:
'''simple docstring'''
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , top_k=2 , framework="""tf""" )
__UpperCAmelCase : Union[str, Any] = unmasker("""My name is <mask>""" )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6 ) , [
{"""sequence""": """My name is grouped""", """score""": 2.1E-05, """token""": 38_015, """token_str""": """ grouped"""},
{"""sequence""": """My name is accuser""", """score""": 2.1E-05, """token""": 25_506, """token_str""": """ accuser"""},
] , )
__UpperCAmelCase : List[str] = unmasker("""The largest city in France is <mask>""" )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6 ) , [
{
"""sequence""": """The largest city in France is grouped""",
"""score""": 2.1E-05,
"""token""": 38_015,
"""token_str""": """ grouped""",
},
{
"""sequence""": """The largest city in France is accuser""",
"""score""": 2.1E-05,
"""token""": 25_506,
"""token_str""": """ accuser""",
},
] , )
__UpperCAmelCase : Union[str, Any] = unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6 ) , [
{"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 13_606, """token_str""": """ Clara"""},
{"""sequence""": """My name is Patrick""", """score""": 2E-05, """token""": 3_499, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Te""", """score""": 1.9E-05, """token""": 2_941, """token_str""": """ Te"""},
] , )
@require_torch
def __A ( self ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : Dict = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , top_k=2 , framework="""pt""" )
__UpperCAmelCase : Union[str, Any] = unmasker("""My name is <mask>""" )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6 ) , [
{"""sequence""": """My name is Maul""", """score""": 2.2E-05, """token""": 35_676, """token_str""": """ Maul"""},
{"""sequence""": """My name isELS""", """score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS"""},
] , )
__UpperCAmelCase : Dict = unmasker("""The largest city in France is <mask>""" )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6 ) , [
{
"""sequence""": """The largest city in France is Maul""",
"""score""": 2.2E-05,
"""token""": 35_676,
"""token_str""": """ Maul""",
},
{"""sequence""": """The largest city in France isELS""", """score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS"""},
] , )
__UpperCAmelCase : str = unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6 ) , [
{"""sequence""": """My name is Patrick""", """score""": 2.1E-05, """token""": 3_499, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Te""", """score""": 2E-05, """token""": 2_941, """token_str""": """ Te"""},
{"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 13_606, """token_str""": """ Clara"""},
] , )
__UpperCAmelCase : Optional[int] = unmasker("""My name is <mask> <mask>""" , top_k=2 )
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6 ) , [
[
{
"""score""": 2.2E-05,
"""token""": 35_676,
"""token_str""": """ Maul""",
"""sequence""": """<s>My name is Maul<mask></s>""",
},
{"""score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
],
[
{
"""score""": 2.2E-05,
"""token""": 35_676,
"""token_str""": """ Maul""",
"""sequence""": """<s>My name is<mask> Maul</s>""",
},
{"""score""": 2.2E-05, """token""": 16_416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
],
] , )
@require_torch_gpu
def __A ( self ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : List[str] = pipeline("""fill-mask""" , model="""hf-internal-testing/tiny-random-distilbert""" , device=0 , framework="""pt""" )
# convert model to fp16
pipe.model.half()
__UpperCAmelCase : str = pipe("""Paris is the [MASK] of France.""" )
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
@slow
@require_torch
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Any = pipeline(task="""fill-mask""" , model="""distilroberta-base""" , top_k=2 , framework="""pt""" )
self.run_large_test(__UpperCAmelCase )
@slow
@require_tf
def __A ( self ) -> int:
'''simple docstring'''
__UpperCAmelCase : int = pipeline(task="""fill-mask""" , model="""distilroberta-base""" , top_k=2 , framework="""tf""" )
self.run_large_test(__UpperCAmelCase )
def __A ( self , __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = unmasker("""My name is <mask>""" )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
{"""sequence""": """My name is John""", """score""": 0.008, """token""": 610, """token_str""": """ John"""},
{"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 1_573, """token_str""": """ Chris"""},
] , )
__UpperCAmelCase : Optional[int] = unmasker("""The largest city in France is <mask>""" )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
{
"""sequence""": """The largest city in France is Paris""",
"""score""": 0.251,
"""token""": 2_201,
"""token_str""": """ Paris""",
},
{
"""sequence""": """The largest city in France is Lyon""",
"""score""": 0.214,
"""token""": 12_790,
"""token_str""": """ Lyon""",
},
] , )
__UpperCAmelCase : Optional[int] = unmasker("""My name is <mask>""" , targets=[""" Patrick""", """ Clara""", """ Teven"""] , top_k=3 )
self.assertEqual(
nested_simplify(__UpperCAmelCase ) , [
{"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3_499, """token_str""": """ Patrick"""},
{"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 13_606, """token_str""": """ Clara"""},
{"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2_941, """token_str""": """ Te"""},
] , )
@require_torch
def __A ( self ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase : Dict = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , framework="""pt""" )
__UpperCAmelCase : Tuple = None
__UpperCAmelCase : int = None
self.run_pipeline_test(__UpperCAmelCase , [] )
@require_tf
def __A ( self ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Dict = pipeline(task="""fill-mask""" , model="""sshleifer/tiny-distilroberta-base""" , framework="""tf""" )
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : str = None
self.run_pipeline_test(__UpperCAmelCase , [] )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Any:
'''simple docstring'''
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)""" )
__UpperCAmelCase : str = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
__UpperCAmelCase : int = [
f'This is another {tokenizer.mask_token} test',
]
return fill_masker, examples
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = fill_masker.tokenizer
__UpperCAmelCase : Union[str, Any] = fill_masker.model
__UpperCAmelCase : Tuple = fill_masker(
f'This is a {tokenizer.mask_token}' , )
self.assertEqual(
__UpperCAmelCase , [
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
] , )
__UpperCAmelCase : int = fill_masker([f'This is a {tokenizer.mask_token}'] )
self.assertEqual(
__UpperCAmelCase , [
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
] , )
__UpperCAmelCase : Union[str, Any] = fill_masker([f'This is a {tokenizer.mask_token}', f'Another {tokenizer.mask_token} great test.'] )
self.assertEqual(
__UpperCAmelCase , [
[
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
],
[
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
],
] , )
with self.assertRaises(__UpperCAmelCase ):
fill_masker([None] )
# No mask_token is not supported
with self.assertRaises(__UpperCAmelCase ):
fill_masker("""This is""" )
self.run_test_top_k(__UpperCAmelCase , __UpperCAmelCase )
self.run_test_targets(__UpperCAmelCase , __UpperCAmelCase )
self.run_test_top_k_targets(__UpperCAmelCase , __UpperCAmelCase )
self.fill_mask_with_duplicate_targets_and_top_k(__UpperCAmelCase , __UpperCAmelCase )
self.fill_mask_with_multiple_masks(__UpperCAmelCase , __UpperCAmelCase )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Dict = tokenizer.get_vocab()
__UpperCAmelCase : Dict = sorted(vocab.keys() )[:2]
# Pipeline argument
__UpperCAmelCase : Dict = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , targets=__UpperCAmelCase )
__UpperCAmelCase : List[str] = fill_masker(f'This is a {tokenizer.mask_token}' )
self.assertEqual(
__UpperCAmelCase , [
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
] , )
__UpperCAmelCase : Any = {vocab[el] for el in targets}
self.assertEqual({el["""token"""] for el in outputs} , __UpperCAmelCase )
__UpperCAmelCase : int = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el["""token_str"""] for el in outputs} , set(__UpperCAmelCase ) )
# Call argument
__UpperCAmelCase : List[Any] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
__UpperCAmelCase : Tuple = fill_masker(f'This is a {tokenizer.mask_token}' , targets=__UpperCAmelCase )
self.assertEqual(
__UpperCAmelCase , [
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
] , )
__UpperCAmelCase : List[Any] = {vocab[el] for el in targets}
self.assertEqual({el["""token"""] for el in outputs} , __UpperCAmelCase )
__UpperCAmelCase : List[Any] = [tokenizer.decode([x] ) for x in target_ids]
self.assertEqual({el["""token_str"""] for el in outputs} , set(__UpperCAmelCase ) )
# Score equivalence
__UpperCAmelCase : Dict = fill_masker(f'This is a {tokenizer.mask_token}' , targets=__UpperCAmelCase )
__UpperCAmelCase : Dict = [top_mask["""token_str"""] for top_mask in outputs]
__UpperCAmelCase : str = [top_mask["""score"""] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(__UpperCAmelCase ) == set(__UpperCAmelCase ):
__UpperCAmelCase : str = fill_masker(f'This is a {tokenizer.mask_token}' , targets=__UpperCAmelCase )
__UpperCAmelCase : int = [top_mask["""score"""] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(__UpperCAmelCase ) , nested_simplify(__UpperCAmelCase ) )
# Raises with invalid
with self.assertRaises(__UpperCAmelCase ):
__UpperCAmelCase : Any = fill_masker(f'This is a {tokenizer.mask_token}' , targets=[] )
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(__UpperCAmelCase ):
__UpperCAmelCase : Dict = fill_masker(f'This is a {tokenizer.mask_token}' , targets=[""""""] )
with self.assertRaises(__UpperCAmelCase ):
__UpperCAmelCase : Union[str, Any] = fill_masker(f'This is a {tokenizer.mask_token}' , targets="""""" )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : Dict = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , top_k=2 )
__UpperCAmelCase : Optional[int] = fill_masker(f'This is a {tokenizer.mask_token}' )
self.assertEqual(
__UpperCAmelCase , [
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
] , )
__UpperCAmelCase : List[Any] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
__UpperCAmelCase : int = fill_masker(f'This is a {tokenizer.mask_token}' , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
] , )
self.assertEqual(nested_simplify(__UpperCAmelCase ) , nested_simplify(__UpperCAmelCase ) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : int = tokenizer.get_vocab()
__UpperCAmelCase : List[Any] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
# top_k=2, ntargets=3
__UpperCAmelCase : Dict = sorted(vocab.keys() )[:3]
__UpperCAmelCase : str = fill_masker(f'This is a {tokenizer.mask_token}' , top_k=2 , targets=__UpperCAmelCase )
# If we use the most probably targets, and filter differently, we should still
# have the same results
__UpperCAmelCase : Tuple = [el["""token_str"""] for el in sorted(__UpperCAmelCase , key=lambda __UpperCAmelCase : x["score"] , reverse=__UpperCAmelCase )]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(__UpperCAmelCase ).issubset(__UpperCAmelCase ):
__UpperCAmelCase : Union[str, Any] = fill_masker(f'This is a {tokenizer.mask_token}' , top_k=3 , targets=__UpperCAmelCase )
# They should yield exactly the same result
self.assertEqual(nested_simplify(__UpperCAmelCase ) , nested_simplify(__UpperCAmelCase ) )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
__UpperCAmelCase : List[Any] = tokenizer.get_vocab()
# String duplicates + id duplicates
__UpperCAmelCase : Dict = sorted(vocab.keys() )[:3]
__UpperCAmelCase : Dict = [targets[0], targets[1], targets[0], targets[2], targets[1]]
__UpperCAmelCase : Optional[int] = fill_masker(f'My name is {tokenizer.mask_token}' , targets=__UpperCAmelCase , top_k=10 )
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(__UpperCAmelCase ) , 3 )
def __A ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[str] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
__UpperCAmelCase : Dict = fill_masker(
f'This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}' , top_k=2 )
self.assertEqual(
__UpperCAmelCase , [
[
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
],
[
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
],
[
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
{"""sequence""": ANY(__UpperCAmelCase ), """score""": ANY(__UpperCAmelCase ), """token""": ANY(__UpperCAmelCase ), """token_str""": ANY(__UpperCAmelCase )},
],
] , )
| 16
| 1
|
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
__lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
__lowercase = 256
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Tuple = ["""melgan"""]
def __init__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , ) -> None:
super().__init__()
# From MELGAN
__UpperCamelCase :int = math.log(1E-5) # Matches MelGAN training.
__UpperCamelCase :int = 4.0 # Largest value for most examples
__UpperCamelCase :str = 128
self.register_modules(
notes_encoder=__lowercase , continuous_encoder=__lowercase , decoder=__lowercase , scheduler=__lowercase , melgan=__lowercase , )
def UpperCamelCase__ ( self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False) -> Dict:
__UpperCamelCase , __UpperCamelCase :str = output_range
if clip:
__UpperCamelCase :Union[str, Any] = torch.clip(__lowercase , self.min_value , self.max_value)
# Scale to [0, 1].
__UpperCamelCase :Union[str, Any] = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def UpperCamelCase__ ( self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False) -> Optional[int]:
__UpperCamelCase , __UpperCamelCase :int = input_range
__UpperCamelCase :Optional[int] = torch.clip(__lowercase , __lowercase , __lowercase) if clip else outputs
# Scale to [0, 1].
__UpperCamelCase :List[str] = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> List[Any]:
__UpperCamelCase :List[str] = input_tokens > 0
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = self.notes_encoder(
encoder_input_tokens=__lowercase , encoder_inputs_mask=__lowercase)
__UpperCamelCase , __UpperCamelCase :Union[str, Any] = self.continuous_encoder(
encoder_inputs=__lowercase , encoder_inputs_mask=__lowercase)
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def UpperCamelCase__ ( self , __lowercase , __lowercase , __lowercase) -> str:
__UpperCamelCase :Optional[int] = noise_time
if not torch.is_tensor(__lowercase):
__UpperCamelCase :str = torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
elif torch.is_tensor(__lowercase) and len(timesteps.shape) == 0:
__UpperCamelCase :Dict = timesteps[None].to(input_tokens.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__UpperCamelCase :List[str] = timesteps * torch.ones(input_tokens.shape[0] , dtype=timesteps.dtype , device=timesteps.device)
__UpperCamelCase :Tuple = self.decoder(
encodings_and_masks=__lowercase , decoder_input_tokens=__lowercase , decoder_noise_time=__lowercase)
return logits
@torch.no_grad()
def __call__( self , __lowercase , __lowercase = None , __lowercase = 100 , __lowercase = True , __lowercase = "numpy" , __lowercase = None , __lowercase = 1 , ) -> Union[AudioPipelineOutput, Tuple]:
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__lowercase , __lowercase) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(__lowercase)}.""")
__UpperCamelCase :Union[str, Any] = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa)
__UpperCamelCase :Union[str, Any] = np.zeros([1, 0, self.n_dims] , np.floataa)
__UpperCamelCase :Union[str, Any] = torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)
for i, encoder_input_tokens in enumerate(__lowercase):
if i == 0:
__UpperCamelCase :int = torch.from_numpy(pred_mel[:1].copy()).to(
device=self.device , dtype=self.decoder.dtype)
# The first chunk has no previous context.
__UpperCamelCase :int = torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
__UpperCamelCase :Tuple = ones
__UpperCamelCase :Optional[Any] = self.scale_features(
__lowercase , output_range=[-1.0, 1.0] , clip=__lowercase)
__UpperCamelCase :int = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device) , continuous_inputs=__lowercase , continuous_mask=__lowercase , )
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
__UpperCamelCase :int = randn_tensor(
shape=encoder_continuous_inputs.shape , generator=__lowercase , device=self.device , dtype=self.decoder.dtype , )
# set step values
self.scheduler.set_timesteps(__lowercase)
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
__UpperCamelCase :Optional[int] = self.decode(
encodings_and_masks=__lowercase , input_tokens=__lowercase , noise_time=t / self.scheduler.config.num_train_timesteps , )
# Compute previous output: x_t -> x_t-1
__UpperCamelCase :int = self.scheduler.step(__lowercase , __lowercase , __lowercase , generator=__lowercase).prev_sample
__UpperCamelCase :Tuple = self.scale_to_features(__lowercase , input_range=[-1.0, 1.0])
__UpperCamelCase :List[Any] = mel[:1]
__UpperCamelCase :Optional[Any] = mel.cpu().float().numpy()
__UpperCamelCase :Any = np.concatenate([full_pred_mel, pred_mel[:1]] , axis=1)
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__lowercase , __lowercase)
logger.info('''Generated segment''' , __lowercase)
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''')
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''')
if output_type == "numpy":
__UpperCamelCase :Optional[Any] = self.melgan(input_features=full_pred_mel.astype(np.floataa))
else:
__UpperCamelCase :List[str] = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=__lowercase)
| 43
|
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self: Tuple , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: List[str]=13 , _SCREAMING_SNAKE_CASE: Tuple=7 , _SCREAMING_SNAKE_CASE: int=True , _SCREAMING_SNAKE_CASE: Optional[Any]=True , _SCREAMING_SNAKE_CASE: str=False , _SCREAMING_SNAKE_CASE: Optional[Any]=True , _SCREAMING_SNAKE_CASE: int=99 , _SCREAMING_SNAKE_CASE: int=32 , _SCREAMING_SNAKE_CASE: List[str]=5 , _SCREAMING_SNAKE_CASE: Union[str, Any]=4 , _SCREAMING_SNAKE_CASE: int=64 , _SCREAMING_SNAKE_CASE: List[str]="gelu" , _SCREAMING_SNAKE_CASE: str=0.1 , _SCREAMING_SNAKE_CASE: Any=0.1 , _SCREAMING_SNAKE_CASE: Optional[int]=512 , _SCREAMING_SNAKE_CASE: Tuple=16 , _SCREAMING_SNAKE_CASE: Any=2 , _SCREAMING_SNAKE_CASE: List[str]=0.02 , _SCREAMING_SNAKE_CASE: Tuple=3 , _SCREAMING_SNAKE_CASE: Optional[Any]=4 , _SCREAMING_SNAKE_CASE: int=None , _SCREAMING_SNAKE_CASE: int=2 , _SCREAMING_SNAKE_CASE: str=2 , _SCREAMING_SNAKE_CASE: Union[str, Any]=2 , _SCREAMING_SNAKE_CASE: List[Any]=2 , _SCREAMING_SNAKE_CASE: int=4 , _SCREAMING_SNAKE_CASE: List[str]=1 , ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : List[str] = parent
__lowerCAmelCase : Optional[Any] = batch_size
__lowerCAmelCase : Union[str, Any] = seq_length
__lowerCAmelCase : Optional[Any] = is_training
__lowerCAmelCase : Optional[int] = use_input_mask
__lowerCAmelCase : Dict = use_token_type_ids
__lowerCAmelCase : Dict = use_labels
__lowerCAmelCase : Dict = vocab_size
__lowerCAmelCase : Tuple = hidden_size
__lowerCAmelCase : List[Any] = num_hidden_layers
__lowerCAmelCase : Union[str, Any] = num_attention_heads
__lowerCAmelCase : Tuple = intermediate_size
__lowerCAmelCase : List[Any] = hidden_act
__lowerCAmelCase : Optional[Any] = hidden_dropout_prob
__lowerCAmelCase : Optional[Any] = attention_probs_dropout_prob
__lowerCAmelCase : Optional[int] = max_position_embeddings
__lowerCAmelCase : Union[str, Any] = type_vocab_size
__lowerCAmelCase : Optional[int] = type_sequence_label_size
__lowerCAmelCase : Dict = initializer_range
__lowerCAmelCase : Tuple = num_labels
__lowerCAmelCase : Optional[Any] = num_choices
__lowerCAmelCase : Union[str, Any] = scope
__lowerCAmelCase : Optional[Any] = q_groups
__lowerCAmelCase : Optional[int] = k_groups
__lowerCAmelCase : Any = v_groups
__lowerCAmelCase : int = post_attention_groups
__lowerCAmelCase : List[str] = intermediate_groups
__lowerCAmelCase : Optional[Any] = output_groups
def _SCREAMING_SNAKE_CASE ( self: Dict) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
__lowerCAmelCase : Union[str, Any] = None
if self.use_input_mask:
__lowerCAmelCase : Tuple = random_attention_mask([self.batch_size, self.seq_length])
__lowerCAmelCase : Optional[int] = None
__lowerCAmelCase : List[Any] = None
__lowerCAmelCase : str = None
if self.use_labels:
__lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
__lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
__lowerCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices)
__lowerCAmelCase : Any = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> int:
"""simple docstring"""
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Tuple) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[Any] = SqueezeBertModel(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : List[Any] = model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = model(_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Tuple) -> Dict:
"""simple docstring"""
__lowerCAmelCase : int = SqueezeBertForMaskedLM(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : Dict = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def _SCREAMING_SNAKE_CASE ( self: Optional[int] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Dict , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Union[str, Any]) -> int:
"""simple docstring"""
__lowerCAmelCase : str = SqueezeBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : Union[str, Any] = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , start_positions=_SCREAMING_SNAKE_CASE , end_positions=_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[Any] , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Tuple) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.num_labels
__lowerCAmelCase : Union[str, Any] = SqueezeBertForSequenceClassification(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : int = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[int]) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Dict = self.num_labels
__lowerCAmelCase : Optional[int] = SqueezeBertForTokenClassification(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : List[str] = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def _SCREAMING_SNAKE_CASE ( self: Optional[Any] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Optional[int] , _SCREAMING_SNAKE_CASE: Union[str, Any] , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: List[Any] , _SCREAMING_SNAKE_CASE: int) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.num_choices
__lowerCAmelCase : str = SqueezeBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
__lowerCAmelCase : int = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
__lowerCAmelCase : Union[str, Any] = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
__lowerCAmelCase : str = model(
_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def _SCREAMING_SNAKE_CASE ( self: str) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = self.prepare_config_and_inputs()
((__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase) , (__lowerCAmelCase)) : Union[str, Any] = config_and_inputs
__lowerCAmelCase : int = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
def _SCREAMING_SNAKE_CASE ( self: Dict) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Any = SqueezeBertModelTester(self)
__lowerCAmelCase : Optional[int] = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , dim=37)
def _SCREAMING_SNAKE_CASE ( self: Any) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Any:
"""simple docstring"""
__lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> int:
"""simple docstring"""
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Any) -> int:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> str:
"""simple docstring"""
__lowerCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_SCREAMING_SNAKE_CASE)
@slow
def _SCREAMING_SNAKE_CASE ( self: Any) -> Dict:
"""simple docstring"""
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase : Optional[Any] = SqueezeBertModel.from_pretrained(_SCREAMING_SNAKE_CASE)
self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
@require_sentencepiece
@require_tokenizers
@require_torch
class A__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self: int) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli")
__lowerCAmelCase : List[Any] = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]])
__lowerCAmelCase : List[Any] = model(_SCREAMING_SNAKE_CASE)[0]
__lowerCAmelCase : Any = torch.Size((1, 3))
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = torch.tensor([[0.6401, -0.0349, -0.6041]])
self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-4))
| 269
| 0
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class snake_case__ ( unittest.TestCase ):
def a__ ( self ):
__a = tempfile.mkdtemp()
__a = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
__a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
__a = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
"image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
__a = os.path.join(self.tmpdirname , lowercase_ )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(lowercase_ , lowercase_ )
def a__ ( self , **lowerCamelCase ):
return BertTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def a__ ( self , **lowerCamelCase ):
return BertTokenizerFast.from_pretrained(self.tmpdirname , **lowercase_ )
def a__ ( self , **lowerCamelCase ):
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **lowercase_ )
def a__ ( self ):
shutil.rmtree(self.tmpdirname )
def a__ ( self ):
__a = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__a = [Image.fromarray(np.moveaxis(lowercase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def a__ ( self ):
__a = self.get_tokenizer()
__a = self.get_rust_tokenizer()
__a = self.get_image_processor()
__a = AlignProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
processor_slow.save_pretrained(self.tmpdirname )
__a = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=lowercase_ )
__a = AlignProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
processor_fast.save_pretrained(self.tmpdirname )
__a = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowercase_ )
self.assertIsInstance(processor_fast.tokenizer , lowercase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowercase_ )
self.assertIsInstance(processor_fast.image_processor , lowercase_ )
def a__ ( self ):
__a = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__a = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
__a = self.get_image_processor(do_normalize=lowercase_ , padding_value=1.0 )
__a = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=lowercase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowercase_ )
def a__ ( self ):
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = AlignProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
__a = self.prepare_image_inputs()
__a = image_processor(lowercase_ , return_tensors="np" )
__a = processor(images=lowercase_ , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def a__ ( self ):
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = AlignProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
__a = "lower newer"
__a = processor(text=lowercase_ )
__a = tokenizer(lowercase_ , padding="max_length" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def a__ ( self ):
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = AlignProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
__a = "lower newer"
__a = self.prepare_image_inputs()
__a = processor(text=lowercase_ , images=lowercase_ )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(lowercase_ ):
processor()
def a__ ( self ):
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = AlignProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
__a = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__a = processor.batch_decode(lowercase_ )
__a = tokenizer.batch_decode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def a__ ( self ):
__a = self.get_image_processor()
__a = self.get_tokenizer()
__a = AlignProcessor(tokenizer=lowercase_ , image_processor=lowercase_ )
__a = "lower newer"
__a = self.prepare_image_inputs()
__a = processor(text=lowercase_ , images=lowercase_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 356
|
"""simple docstring"""
import math
def _lowerCamelCase( a ):
__a = []
__a = 2
__a = int(math.sqrt(a ) ) # Size of every segment
__a = [True] * (end + 1)
__a = []
while start <= end:
if temp[start] is True:
in_prime.append(a )
for i in range(start * start , end + 1 , a ):
__a = False
start += 1
prime += in_prime
__a = end + 1
__a = min(2 * end , a )
while low <= n:
__a = [True] * (high - low + 1)
for each in in_prime:
__a = math.floor(low / each ) * each
if t < low:
t += each
for j in range(a , high + 1 , a ):
__a = False
for j in range(len(a ) ):
if temp[j] is True:
prime.append(j + low )
__a = high + 1
__a = min(high + end , a )
return prime
print(sieve(10**6))
| 268
| 0
|
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
assert column_title.isupper()
A__ = 0
A__ = len(lowercase_ ) - 1
A__ = 0
while index >= 0:
A__ = (ord(column_title[index] ) - 64) * pow(26 , lowercase_ )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 14
|
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(lowercase_ ) - np.asarray(lowercase_ )) ** 2 ) )
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(lowercase_ , lowercase_ ) ) ** (1 / 2)
if __name__ == "__main__":
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
from timeit import timeit
print('''Without Numpy''' )
print(
timeit(
'''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' , number=10_000 , globals=globals() , ) )
print('''With Numpy''' )
print(
timeit(
'''euclidean_distance([1, 2, 3], [4, 5, 6])''' , number=10_000 , globals=globals() , ) )
benchmark()
| 14
| 1
|
import datasets
_UpperCAmelCase = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and Stoyanov, Veselin\",\n title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n in Natural Language Processing\",\n year = \"2018\",\n publisher = \"Association for Computational Linguistics\",\n location = \"Brussels, Belgium\",\n}\n"
_UpperCAmelCase = "\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"
_UpperCAmelCase = "\nComputes XNLI score which is just simple accuracy.\nArgs:\n predictions: Predicted labels.\n references: Ground truth labels.\nReturns:\n 'accuracy': accuracy\nExamples:\n\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> xnli_metric = datasets.load_metric(\"xnli\")\n >>> results = xnli_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n"
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :List[Any] , SCREAMING_SNAKE_CASE :Union[str, Any] ) -> Optional[Any]:
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class snake_case_ ( datasets.Metric ):
def UpperCAmelCase__ ( self : Optional[Any] )->List[str]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
"""references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
} ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" , )
def UpperCAmelCase__ ( self : Optional[int] , _snake_case : Optional[int] , _snake_case : str )->Optional[int]:
'''simple docstring'''
return {"accuracy": simple_accuracy(_snake_case , _snake_case )}
| 368
|
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :str ) -> bytes:
__lowerCAmelCase : List[Any] = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
__lowerCAmelCase : Dict = requests.get(base_url + url ).json()[0]["""urls"""][0]["""src"""]
return requests.get(SCREAMING_SNAKE_CASE ).content
if __name__ == "__main__":
_UpperCAmelCase = input('Enter Video/IGTV url: ').strip()
_UpperCAmelCase = f'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
with open(file_name, 'wb') as fp:
fp.write(download_video(url))
print(f'''Done. Video saved to disk as {file_name}.''')
| 232
| 0
|
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
UpperCamelCase__ = True
from torch.cuda.amp import autocast
UpperCamelCase__ = logging.getLogger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : List[Any]=None , SCREAMING_SNAKE_CASE_ : List[str]=None ):
return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE_ )
@dataclass
class a__ :
_a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
_a : Optional[str] = field(
default=snake_case__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_a : Optional[bool] = field(
default=snake_case__ , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
_a : Optional[float] = field(
default=0.1 , metadata={"""help""": """The dropout ratio for the attention probabilities."""} )
_a : Optional[float] = field(
default=0.1 , metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""} )
_a : Optional[float] = field(
default=0.1 , metadata={
"""help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
} , )
_a : Optional[float] = field(
default=0.1 , metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} , )
_a : Optional[float] = field(
default=0.05 , metadata={
"""help""": (
"""Propability of each feature vector along the time axis to be chosen as the start of the vector"""
"""span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
"""vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
)
} , )
_a : Optional[float] = field(default=0.0 , metadata={"""help""": """The LayerDrop probability."""} )
@dataclass
class a__ :
_a : Optional[str] = field(
default=snake_case__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
_a : Optional[str] = field(
default="""train+validation""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
_a : bool = field(
default=snake_case__ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
_a : Optional[int] = field(
default=snake_case__ , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
_a : Optional[int] = field(
default=snake_case__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_a : Optional[int] = field(
default=snake_case__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of validation examples to this """
"""value if set."""
)
} , )
_a : List[str] = list_field(
default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""] , metadata={"""help""": """A list of characters to remove from the transcripts."""} , )
@dataclass
class a__ :
_a : WavaVecaProcessor
_a : Union[bool, str] = True
_a : Optional[int] = None
_a : Optional[int] = None
_a : Optional[int] = None
_a : Optional[int] = None
def __call__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = [{"input_values": feature["input_values"]} for feature in features]
__lowerCAmelCase = [{"input_ids": feature["labels"]} for feature in features]
__lowerCAmelCase = self.processor.pad(
_A , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
__lowerCAmelCase = self.processor.pad(
labels=_A , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors="pt" , )
# replace padding with -100 to ignore loss correctly
__lowerCAmelCase = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1 ) , -1_0_0 )
__lowerCAmelCase = labels
return batch
class a__ ( snake_case__ ):
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
"""simple docstring"""
model.train()
__lowerCAmelCase = self._prepare_inputs(_A )
if self.use_amp:
with autocast():
__lowerCAmelCase = self.compute_loss(_A , _A )
else:
__lowerCAmelCase = self.compute_loss(_A , _A )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
__lowerCAmelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
__lowerCAmelCase = loss.sum() / (inputs["labels"] >= 0).sum()
else:
raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
__lowerCAmelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(_A ).backward()
elif self.use_apex:
with amp.scale_loss(_A , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(_A )
else:
loss.backward()
return loss.detach()
def _a ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("Training/evaluation parameters %s" , SCREAMING_SNAKE_CASE_ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
__lowerCAmelCase = datasets.load_dataset(
"common_voice" , data_args.dataset_config_name , split=data_args.train_split_name )
__lowerCAmelCase = datasets.load_dataset("common_voice" , data_args.dataset_config_name , split="test" )
# Create and save tokenizer
__lowerCAmelCase = F"""[{''.join(data_args.chars_to_ignore )}]"""
def remove_special_characters(SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = re.sub(SCREAMING_SNAKE_CASE_ , "" , batch["sentence"] ).lower() + " "
return batch
__lowerCAmelCase = train_dataset.map(SCREAMING_SNAKE_CASE_ , remove_columns=["sentence"] )
__lowerCAmelCase = eval_dataset.map(SCREAMING_SNAKE_CASE_ , remove_columns=["sentence"] )
def extract_all_chars(SCREAMING_SNAKE_CASE_ : Tuple ):
__lowerCAmelCase = " ".join(batch["text"] )
__lowerCAmelCase = list(set(SCREAMING_SNAKE_CASE_ ) )
return {"vocab": [vocab], "all_text": [all_text]}
__lowerCAmelCase = train_dataset.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , batch_size=-1 , keep_in_memory=SCREAMING_SNAKE_CASE_ , remove_columns=train_dataset.column_names , )
__lowerCAmelCase = train_dataset.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , batch_size=-1 , keep_in_memory=SCREAMING_SNAKE_CASE_ , remove_columns=eval_dataset.column_names , )
__lowerCAmelCase = list(set(vocab_train["vocab"][0] ) | set(vocab_test["vocab"][0] ) )
__lowerCAmelCase = {v: k for k, v in enumerate(SCREAMING_SNAKE_CASE_ )}
__lowerCAmelCase = vocab_dict[" "]
del vocab_dict[" "]
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
with open("vocab.json" , "w" ) as vocab_file:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = WavaVecaCTCTokenizer(
"vocab.json" , unk_token="[UNK]" , pad_token="[PAD]" , word_delimiter_token="|" , )
__lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0.0 , do_normalize=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction="mean" , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
__lowerCAmelCase = min(len(SCREAMING_SNAKE_CASE_ ) , data_args.max_train_samples )
__lowerCAmelCase = train_dataset.select(range(SCREAMING_SNAKE_CASE_ ) )
if data_args.max_val_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_val_samples ) )
__lowerCAmelCase = torchaudio.transforms.Resample(4_80_00 , 1_60_00 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(SCREAMING_SNAKE_CASE_ : str ):
__lowerCAmelCase , __lowerCAmelCase = torchaudio.load(batch["path"] )
__lowerCAmelCase = resampler(SCREAMING_SNAKE_CASE_ ).squeeze().numpy()
__lowerCAmelCase = 1_60_00
__lowerCAmelCase = batch["text"]
return batch
__lowerCAmelCase = train_dataset.map(
SCREAMING_SNAKE_CASE_ , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
__lowerCAmelCase = eval_dataset.map(
SCREAMING_SNAKE_CASE_ , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(SCREAMING_SNAKE_CASE_ : Tuple ):
# check that all files have the correct sampling rate
assert (
len(set(batch["sampling_rate"] ) ) == 1
), F"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
__lowerCAmelCase = processor(
audio=batch["speech"] , text=batch["target_text"] , sampling_rate=batch["sampling_rate"][0] )
batch.update(SCREAMING_SNAKE_CASE_ )
return batch
__lowerCAmelCase = train_dataset.map(
SCREAMING_SNAKE_CASE_ , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=SCREAMING_SNAKE_CASE_ , num_proc=data_args.preprocessing_num_workers , )
__lowerCAmelCase = eval_dataset.map(
SCREAMING_SNAKE_CASE_ , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=SCREAMING_SNAKE_CASE_ , num_proc=data_args.preprocessing_num_workers , )
# Metric
__lowerCAmelCase = datasets.load_metric("wer" )
def compute_metrics(SCREAMING_SNAKE_CASE_ : Tuple ):
__lowerCAmelCase = pred.predictions
__lowerCAmelCase = np.argmax(SCREAMING_SNAKE_CASE_ , axis=-1 )
__lowerCAmelCase = processor.tokenizer.pad_token_id
__lowerCAmelCase = processor.batch_decode(SCREAMING_SNAKE_CASE_ )
# we do not want to group tokens when computing the metrics
__lowerCAmelCase = processor.batch_decode(pred.label_ids , group_tokens=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = wer_metric.compute(predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
__lowerCAmelCase = DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# Initialize our Trainer
__lowerCAmelCase = CTCTrainer(
model=SCREAMING_SNAKE_CASE_ , data_collator=SCREAMING_SNAKE_CASE_ , args=SCREAMING_SNAKE_CASE_ , compute_metrics=SCREAMING_SNAKE_CASE_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
__lowerCAmelCase = model_args.model_name_or_path
else:
__lowerCAmelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
__lowerCAmelCase = trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE_ )
trainer.save_model()
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(SCREAMING_SNAKE_CASE_ )
)
__lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) )
trainer.log_metrics("train" , SCREAMING_SNAKE_CASE_ )
trainer.save_metrics("train" , SCREAMING_SNAKE_CASE_ )
trainer.save_state()
# Evaluation
__lowerCAmelCase = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
__lowerCAmelCase = trainer.evaluate()
__lowerCAmelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) )
trainer.log_metrics("eval" , SCREAMING_SNAKE_CASE_ )
trainer.save_metrics("eval" , SCREAMING_SNAKE_CASE_ )
return results
if __name__ == "__main__":
main()
| 92
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92
| 1
|
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : int = sys.version_info >= (3, 10)
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase=None, _UpperCAmelCase=None ) -> Any:
'''simple docstring'''
return field(default_factory=lambda: default, metadata=_UpperCAmelCase )
@dataclass
class __A :
lowerCAmelCase_ : int
lowerCAmelCase_ : float
lowerCAmelCase_ : str
lowerCAmelCase_ : bool
@dataclass
class __A :
lowerCAmelCase_ : int = 42
lowerCAmelCase_ : str = field(default="toto" , metadata={"help": "help message"} )
@dataclass
class __A :
lowerCAmelCase_ : bool = False
lowerCAmelCase_ : bool = True
lowerCAmelCase_ : Optional[bool] = None
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Optional[Any] = "titi"
lowerCAmelCase_ : Union[str, Any] = "toto"
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : str = "titi"
lowerCAmelCase_ : Union[str, Any] = "toto"
lowerCAmelCase_ : int = 42
@dataclass
class __A :
lowerCAmelCase_ : BasicEnum = "toto"
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : str = BasicEnum(self.foo )
@dataclass
class __A :
lowerCAmelCase_ : MixedTypeEnum = "toto"
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : Tuple = MixedTypeEnum(self.foo )
@dataclass
class __A :
lowerCAmelCase_ : Optional[int] = None
lowerCAmelCase_ : Optional[float] = field(default=lowerCAmelCase , metadata={"help": "help message"} )
lowerCAmelCase_ : Optional[str] = None
lowerCAmelCase_ : Optional[List[str]] = list_field(default=[] )
lowerCAmelCase_ : Optional[List[int]] = list_field(default=[] )
@dataclass
class __A :
lowerCAmelCase_ : List[int] = list_field(default=[] )
lowerCAmelCase_ : List[int] = list_field(default=[1, 2, 3] )
lowerCAmelCase_ : List[str] = list_field(default=["Hallo", "Bonjour", "Hello"] )
lowerCAmelCase_ : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class __A :
lowerCAmelCase_ : List[int] = field()
lowerCAmelCase_ : str = field()
lowerCAmelCase_ : BasicEnum = field()
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : str = BasicEnum(self.required_enum )
@dataclass
class __A :
lowerCAmelCase_ : int
lowerCAmelCase_ : "BasicEnum" = field()
lowerCAmelCase_ : "Optional[bool]" = None
lowerCAmelCase_ : "str" = field(default="toto" , metadata={"help": "help message"} )
lowerCAmelCase_ : "List[str]" = list_field(default=["Hallo", "Bonjour", "Hello"] )
if is_python_no_less_than_3_10:
@dataclass
class __A :
lowerCAmelCase_ : bool = False
lowerCAmelCase_ : bool = True
lowerCAmelCase_ : bool | None = None
@dataclass
class __A :
lowerCAmelCase_ : int | None = None
lowerCAmelCase_ : float | None = field(default=lowerCAmelCase , metadata={"help": "help message"} )
lowerCAmelCase_ : str | None = None
lowerCAmelCase_ : list[str] | None = list_field(default=[] )
lowerCAmelCase_ : list[int] | None = list_field(default=[] )
class __A ( unittest.TestCase ):
def lowercase__ ( self : Dict , UpperCAmelCase_ : argparse.ArgumentParser , UpperCAmelCase_ : argparse.ArgumentParser ):
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
lowerCAmelCase : Any = {k: v for k, v in vars(UpperCAmelCase_ ).items() if k != 'container'}
lowerCAmelCase : Tuple = {k: v for k, v in vars(UpperCAmelCase_ ).items() if k != 'container'}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , UpperCAmelCase_ ) and yy.get('choices' , UpperCAmelCase_ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](UpperCAmelCase_ ) , yy['type'](UpperCAmelCase_ ) )
del xx["type"], yy["type"]
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : Any ):
lowerCAmelCase : Optional[Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser()
expected.add_argument('--foo' , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument('--bar' , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument('--baz' , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument('--flag' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs='?' )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : Optional[Any] = ['--foo', '1', '--baz', 'quux', '--bar', '0.5']
((lowerCAmelCase) , ) : Optional[Any] = parser.parse_args_into_dataclasses(UpperCAmelCase_ , look_for_args_file=UpperCAmelCase_ )
self.assertFalse(example.flag )
def lowercase__ ( self : int ):
lowerCAmelCase : Union[str, Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : int = argparse.ArgumentParser()
expected.add_argument('--foo' , default=42 , type=UpperCAmelCase_ )
expected.add_argument('--baz' , default='toto' , type=UpperCAmelCase_ , help='help message' )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : int ):
lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
expected.add_argument('--foo' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs='?' )
expected.add_argument('--baz' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , const=UpperCAmelCase_ , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=UpperCAmelCase_ , dest='baz' )
expected.add_argument('--opt' , type=UpperCAmelCase_ , default=UpperCAmelCase_ )
lowerCAmelCase : str = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase_ )
for dataclass_type in dataclass_types:
lowerCAmelCase : Optional[Any] = HfArgumentParser(UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : List[str] = parser.parse_args([] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
lowerCAmelCase : Dict = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
lowerCAmelCase : Optional[Any] = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
lowerCAmelCase : Optional[Any] = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
lowerCAmelCase : str = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , baz=UpperCAmelCase_ , opt=UpperCAmelCase_ ) )
def lowercase__ ( self : List[str] ):
lowerCAmelCase : int = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 42] , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : List[Any] = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
lowerCAmelCase : Union[str, Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
lowerCAmelCase : Optional[int] = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
lowerCAmelCase : Dict = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
lowerCAmelCase : Dict = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
lowerCAmelCase : Any = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowercase__ ( self : Optional[Any] ):
@dataclass
class __A :
lowerCAmelCase_ : Literal["titi", "toto", 42] = "toto"
lowerCAmelCase : List[Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 42) , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : str = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
lowerCAmelCase : str = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
lowerCAmelCase : Tuple = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
def lowercase__ ( self : Optional[int] ):
lowerCAmelCase : Optional[Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : List[Any] = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=UpperCAmelCase_ )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=UpperCAmelCase_ )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=UpperCAmelCase_ )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : List[str] = parser.parse_args([] )
self.assertEqual(
UpperCAmelCase_ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
lowerCAmelCase : Union[str, Any] = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(UpperCAmelCase_ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def lowercase__ ( self : int ):
lowerCAmelCase : int = argparse.ArgumentParser()
expected.add_argument('--foo' , default=UpperCAmelCase_ , type=UpperCAmelCase_ )
expected.add_argument('--bar' , default=UpperCAmelCase_ , type=UpperCAmelCase_ , help='help message' )
expected.add_argument('--baz' , default=UpperCAmelCase_ , type=UpperCAmelCase_ )
expected.add_argument('--ces' , nargs='+' , default=[] , type=UpperCAmelCase_ )
expected.add_argument('--des' , nargs='+' , default=[] , type=UpperCAmelCase_ )
lowerCAmelCase : List[Any] = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(UpperCAmelCase_ )
for dataclass_type in dataclass_types:
lowerCAmelCase : Any = HfArgumentParser(UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : Dict = parser.parse_args([] )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=UpperCAmelCase_ , bar=UpperCAmelCase_ , baz=UpperCAmelCase_ , ces=[] , des=[] ) )
lowerCAmelCase : str = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(UpperCAmelCase_ , Namespace(foo=12 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def lowercase__ ( self : int ):
lowerCAmelCase : List[str] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument('--required_str' , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=UpperCAmelCase_ , )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : List[Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : List[str] = argparse.ArgumentParser()
expected.add_argument('--foo' , type=UpperCAmelCase_ , required=UpperCAmelCase_ )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=UpperCAmelCase_ , )
expected.add_argument('--opt' , type=UpperCAmelCase_ , default=UpperCAmelCase_ )
expected.add_argument('--baz' , default='toto' , type=UpperCAmelCase_ , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=UpperCAmelCase_ )
self.argparsersEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : Optional[Any] ):
lowerCAmelCase : Optional[Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : Any = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
lowerCAmelCase : List[Any] = parser.parse_dict(UpperCAmelCase_ )[0]
lowerCAmelCase : Tuple = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : int ):
lowerCAmelCase : Union[str, Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : str = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
'extra': 42,
}
self.assertRaises(UpperCAmelCase_ , parser.parse_dict , UpperCAmelCase_ , allow_extra_keys=UpperCAmelCase_ )
def lowercase__ ( self : Tuple ):
lowerCAmelCase : str = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : Optional[int] = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase : Any = os.path.join(UpperCAmelCase_ , 'temp_json' )
os.mkdir(UpperCAmelCase_ )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : List[Any] = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
lowerCAmelCase : int = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : Optional[int] ):
lowerCAmelCase : Union[str, Any] = HfArgumentParser(UpperCAmelCase_ )
lowerCAmelCase : int = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase : str = os.path.join(UpperCAmelCase_ , 'temp_yaml' )
os.mkdir(UpperCAmelCase_ )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCAmelCase : Optional[Any] = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
lowerCAmelCase : int = BasicExample(**UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : int ):
lowerCAmelCase : Any = HfArgumentParser(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
| 323
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class __A ( lowerCAmelCase , lowerCAmelCase ):
lowerCAmelCase_ : Optional[Any] = "dinat"
lowerCAmelCase_ : Dict = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Optional[Any]=4 , UpperCAmelCase_ : Tuple=3 , UpperCAmelCase_ : Optional[Any]=64 , UpperCAmelCase_ : List[Any]=[3, 4, 6, 5] , UpperCAmelCase_ : Dict=[2, 4, 8, 16] , UpperCAmelCase_ : Dict=7 , UpperCAmelCase_ : Dict=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] , UpperCAmelCase_ : int=3.0 , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Optional[Any]=0.0 , UpperCAmelCase_ : Optional[Any]=0.0 , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : List[str]="gelu" , UpperCAmelCase_ : List[Any]=0.02 , UpperCAmelCase_ : List[str]=1E-5 , UpperCAmelCase_ : Optional[int]=0.0 , UpperCAmelCase_ : int=None , UpperCAmelCase_ : Optional[int]=None , **UpperCAmelCase_ : Union[str, Any] , ):
super().__init__(**UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = patch_size
lowerCAmelCase : Optional[Any] = num_channels
lowerCAmelCase : str = embed_dim
lowerCAmelCase : Any = depths
lowerCAmelCase : List[Any] = len(UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = num_heads
lowerCAmelCase : Tuple = kernel_size
lowerCAmelCase : List[str] = dilations
lowerCAmelCase : Any = mlp_ratio
lowerCAmelCase : Optional[int] = qkv_bias
lowerCAmelCase : int = hidden_dropout_prob
lowerCAmelCase : str = attention_probs_dropout_prob
lowerCAmelCase : Union[str, Any] = drop_path_rate
lowerCAmelCase : Any = hidden_act
lowerCAmelCase : Union[str, Any] = layer_norm_eps
lowerCAmelCase : Optional[int] = initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase : Union[str, Any] = int(embed_dim * 2 ** (len(UpperCAmelCase_ ) - 1) )
lowerCAmelCase : int = layer_scale_init_value
lowerCAmelCase : Optional[Any] = ['stem'] + [f"stage{idx}" for idx in range(1 , len(UpperCAmelCase_ ) + 1 )]
lowerCAmelCase , lowerCAmelCase : Tuple = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase_ , out_indices=UpperCAmelCase_ , stage_names=self.stage_names )
| 323
| 1
|
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : Any = ["pixel_values"]
def __init__( self : int ,_snake_case : bool = True ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : bool = True ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : bool = True ,_snake_case : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,_snake_case : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,**_snake_case : Optional[int] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : List[str] = size if size is not None else {'''shortest_edge''': 224}
lowercase__ : Union[str, Any] = get_size_dict(_snake_case ,default_to_square=_snake_case )
lowercase__ : Dict = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : int = get_size_dict(_snake_case ,param_name='''crop_size''' )
lowercase__ : List[str] = do_resize
lowercase__ : List[str] = size
lowercase__ : List[Any] = resample
lowercase__ : Any = do_center_crop
lowercase__ : List[str] = crop_size
lowercase__ : Union[str, Any] = do_rescale
lowercase__ : List[Any] = rescale_factor
lowercase__ : Optional[int] = do_normalize
lowercase__ : Tuple = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowercase__ : List[str] = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self : List[str] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BICUBIC ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : List[Any] ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case ,default_to_square=_snake_case )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
lowercase__ : Optional[Any] = int((256 / 224) * size['''shortest_edge'''] )
lowercase__ : List[str] = get_resize_output_image_size(_snake_case ,size=_snake_case ,default_to_square=_snake_case )
lowercase__ : List[str] = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
_snake_case ,size=(size_dict['''height'''], size_dict['''width''']) ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Dict ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : List[str] ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : List[str] = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : np.ndarray ,_snake_case : Union[int, float] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Any ,) -> np.ndarray:
"""simple docstring"""
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[int] ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : ImageInput ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : PILImageResampling = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Dict[str, int]] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[float] = None ,_snake_case : Optional[bool] = None ,_snake_case : Optional[Union[float, Iterable[float]]] = None ,_snake_case : Optional[Union[float, Iterable[float]]] = None ,_snake_case : Optional[TensorType] = None ,_snake_case : ChannelDimension = ChannelDimension.FIRST ,**_snake_case : Dict ,) -> BatchFeature:
"""simple docstring"""
lowercase__ : Tuple = do_resize if do_resize is not None else self.do_resize
lowercase__ : str = resample if resample is not None else self.resample
lowercase__ : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : str = image_mean if image_mean is not None else self.image_mean
lowercase__ : Optional[int] = image_std if image_std is not None else self.image_std
lowercase__ : Optional[Any] = size if size is not None else self.size
lowercase__ : Tuple = get_size_dict(_snake_case ,default_to_square=_snake_case )
lowercase__ : Optional[Any] = crop_size if crop_size is not None else self.crop_size
lowercase__ : int = get_size_dict(_snake_case ,param_name='''crop_size''' )
lowercase__ : Union[str, Any] = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
lowercase__ : Tuple = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
lowercase__ : Any = [self.resize(_snake_case ,_snake_case ,_snake_case ) for image in images]
if do_center_crop:
lowercase__ : Tuple = [self.center_crop(_snake_case ,_snake_case ) for image in images]
if do_rescale:
lowercase__ : Dict = [self.rescale(_snake_case ,_snake_case ) for image in images]
if do_normalize:
lowercase__ : List[str] = [self.normalize(_snake_case ,_snake_case ,_snake_case ) for image in images]
lowercase__ : int = [to_channel_dimension_format(_snake_case ,_snake_case ) for image in images]
lowercase__ : Union[str, Any] = {'''pixel_values''': images}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 16
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __A :
'''simple docstring'''
def __init__( self : str ,_snake_case : List[Any] ,_snake_case : Optional[int]=3 ,_snake_case : Optional[int]=32 ,_snake_case : Union[str, Any]=3 ,_snake_case : int=10 ,_snake_case : List[str]=[10, 20, 30, 40] ,_snake_case : Any=[1, 1, 2, 1] ,_snake_case : int=True ,_snake_case : Optional[Any]=True ,_snake_case : Union[str, Any]="relu" ,_snake_case : Dict=3 ,_snake_case : Any=None ,) -> str:
"""simple docstring"""
lowercase__ : int = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Optional[Any] = image_size
lowercase__ : Optional[Any] = num_channels
lowercase__ : Optional[Any] = embeddings_size
lowercase__ : Optional[Any] = hidden_sizes
lowercase__ : str = depths
lowercase__ : Tuple = is_training
lowercase__ : List[Any] = use_labels
lowercase__ : Union[str, Any] = hidden_act
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Tuple = scope
lowercase__ : Optional[Any] = len(_snake_case )
def UpperCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowercase__ : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Tuple = None
if self.use_labels:
lowercase__ : Dict = ids_tensor([self.batch_size] ,self.num_labels )
lowercase__ : int = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return ResNetConfig(
num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,image_size=self.image_size ,)
def UpperCAmelCase ( self : List[str] ,_snake_case : Optional[int] ,_snake_case : int ,_snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ : Optional[int] = TFResNetModel(config=_snake_case )
lowercase__ : List[str] = model(_snake_case )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Optional[Any] ,_snake_case : int ,_snake_case : Any ) -> Tuple:
"""simple docstring"""
lowercase__ : Tuple = self.num_labels
lowercase__ : Union[str, Any] = TFResNetForImageClassification(_snake_case )
lowercase__ : List[str] = model(_snake_case ,labels=_snake_case )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def UpperCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
lowercase__ : Dict = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = config_and_inputs
lowercase__ : Dict = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class __A ( A_ ,A_ ,unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
lowerCAmelCase : Any = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
lowerCAmelCase : List[Any] = False
lowerCAmelCase : List[Any] = False
lowerCAmelCase : int = False
lowerCAmelCase : Union[str, Any] = False
lowerCAmelCase : List[str] = False
def UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = TFResNetModelTester(self )
lowercase__ : int = ConfigTester(self ,config_class=_snake_case ,has_text_modality=_snake_case )
def UpperCAmelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def UpperCAmelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def UpperCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
pass
def UpperCAmelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : str = model_class(_snake_case )
lowercase__ : Dict = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Optional[int] = [*signature.parameters.keys()]
lowercase__ : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,_snake_case )
def UpperCAmelCase ( self : Tuple ) -> Any:
"""simple docstring"""
lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCAmelCase ( self : Dict ) -> List[str]:
"""simple docstring"""
def check_hidden_states_output(_snake_case : Optional[int] ,_snake_case : List[str] ,_snake_case : Optional[Any] ):
lowercase__ : str = model_class(_snake_case )
lowercase__ : Union[str, Any] = model(**self._prepare_for_class(_snake_case ,_snake_case ) )
lowercase__ : List[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowercase__ : Tuple = self.model_tester.num_stages
self.assertEqual(len(_snake_case ) ,expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,)
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
lowercase__ : List[Any] = layer_type
lowercase__ : Dict = True
check_hidden_states_output(_snake_case ,_snake_case ,_snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ : Dict = True
check_hidden_states_output(_snake_case ,_snake_case ,_snake_case )
def UpperCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def UpperCAmelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : Optional[Any] = TFResNetModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def __UpperCAmelCase ( ) -> Dict:
lowercase__ : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class __A ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase ( self : str ) -> Any:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowercase__ : Tuple = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
lowercase__ : Any = self.default_image_processor
lowercase__ : int = prepare_img()
lowercase__ : Tuple = image_processor(images=_snake_case ,return_tensors='''tf''' )
# forward pass
lowercase__ : Dict = model(**_snake_case )
# verify the logits
lowercase__ : List[str] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape ,_snake_case )
lowercase__ : Any = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() ,_snake_case ,atol=1e-4 ) )
| 16
| 1
|
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = len(snake_case__ )
lowerCAmelCase__ : Dict = []
for i in range(len(snake_case__ ) - pat_len + 1 ):
lowerCAmelCase__ : str = True
for j in range(snake_case__ ):
if s[i + j] != pattern[j]:
lowerCAmelCase__ : int = False
break
if match_found:
position.append(snake_case__ )
return position
if __name__ == "__main__":
assert naive_pattern_search('''ABCDEFG''', '''DE''') == [3]
print(naive_pattern_search('''ABAAABCDBBABCDDEBCABC''', '''ABC'''))
| 365
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCamelCase = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
lowerCamelCase = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
lowerCamelCase = {f'''funnel-transformer/{name}''': 512 for name in _model_names}
lowerCamelCase = {f'''funnel-transformer/{name}''': {'''do_lower_case''': True} for name in _model_names}
class _a ( _lowercase):
_a : Tuple = VOCAB_FILES_NAMES
_a : Dict = PRETRAINED_VOCAB_FILES_MAP
_a : Dict = PRETRAINED_INIT_CONFIGURATION
_a : Union[str, Any] = FunnelTokenizer
_a : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a : int = 2
def __init__( self : List[Any] , _SCREAMING_SNAKE_CASE : str=None , _SCREAMING_SNAKE_CASE : str=None , _SCREAMING_SNAKE_CASE : Any=True , _SCREAMING_SNAKE_CASE : Any="<unk>" , _SCREAMING_SNAKE_CASE : Dict="<sep>" , _SCREAMING_SNAKE_CASE : Optional[int]="<pad>" , _SCREAMING_SNAKE_CASE : str="<cls>" , _SCREAMING_SNAKE_CASE : List[str]="<mask>" , _SCREAMING_SNAKE_CASE : Optional[int]="<s>" , _SCREAMING_SNAKE_CASE : Dict="</s>" , _SCREAMING_SNAKE_CASE : Any=True , _SCREAMING_SNAKE_CASE : Dict=True , _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : str="##" , **_SCREAMING_SNAKE_CASE : List[str] , )-> List[str]:
super().__init__(
_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , do_lower_case=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , clean_text=_SCREAMING_SNAKE_CASE , tokenize_chinese_chars=_SCREAMING_SNAKE_CASE , strip_accents=_SCREAMING_SNAKE_CASE , wordpieces_prefix=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Any = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , _SCREAMING_SNAKE_CASE ) != do_lower_case
or normalizer_state.get('''strip_accents''' , _SCREAMING_SNAKE_CASE ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , _SCREAMING_SNAKE_CASE ) != tokenize_chinese_chars
):
lowerCAmelCase__ : int = getattr(_SCREAMING_SNAKE_CASE , normalizer_state.pop('''type''' ) )
lowerCAmelCase__ : Dict = do_lower_case
lowerCAmelCase__ : str = strip_accents
lowerCAmelCase__ : Dict = tokenize_chinese_chars
lowerCAmelCase__ : str = normalizer_class(**_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[Any] = do_lower_case
def UpperCAmelCase__( self : Any , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Union[str, Any]=None )-> Optional[int]:
lowerCAmelCase__ : Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase__( self : Optional[Any] , _SCREAMING_SNAKE_CASE : List[int] , _SCREAMING_SNAKE_CASE : Optional[List[int]] = None )-> List[int]:
lowerCAmelCase__ : str = [self.sep_token_id]
lowerCAmelCase__ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__( self : str , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[str] = None )-> Tuple[str]:
lowerCAmelCase__ : Any = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE )
return tuple(_SCREAMING_SNAKE_CASE )
| 211
| 0
|
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : Union[List[PIL.Image.Image], np.ndarray]
__UpperCAmelCase : Optional[List[bool]]
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 65
|
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class UpperCamelCase_ (__A , unittest.TestCase ):
__magic_name__ = BertTokenizer
__magic_name__ = BertTokenizerFast
__magic_name__ = True
__magic_name__ = True
__magic_name__ = filter_non_english
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[Any]:
super().setUp()
UpperCAmelCase_ : Tuple = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
UpperCAmelCase_ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase_ : str ) -> Union[str, Any]:
UpperCAmelCase_ : Tuple = "UNwant\u00E9d,running"
UpperCAmelCase_ : Any = "unwanted, running"
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
UpperCAmelCase_ : Any = self.tokenizer_class(self.vocab_file )
UpperCAmelCase_ : Tuple = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(lowerCAmelCase_ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ ) , [9, 6, 7, 12, 10, 11] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
if not self.test_rust_tokenizer:
return
UpperCAmelCase_ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase_ : List[Any] = self.get_rust_tokenizer()
UpperCAmelCase_ : List[Any] = "UNwant\u00E9d,running"
UpperCAmelCase_ : Union[str, Any] = tokenizer.tokenize(lowerCAmelCase_ )
UpperCAmelCase_ : int = rust_tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Any = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : Optional[int] = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = self.get_rust_tokenizer()
UpperCAmelCase_ : Dict = tokenizer.encode(lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = rust_tokenizer.encode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
# With lower casing
UpperCAmelCase_ : Tuple = self.get_tokenizer(do_lower_case=lowerCAmelCase_ )
UpperCAmelCase_ : Optional[int] = self.get_rust_tokenizer(do_lower_case=lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = "UNwant\u00E9d,running"
UpperCAmelCase_ : List[Any] = tokenizer.tokenize(lowerCAmelCase_ )
UpperCAmelCase_ : Tuple = rust_tokenizer.tokenize(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = self.get_rust_tokenizer()
UpperCAmelCase_ : Union[str, Any] = tokenizer.encode(lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = rust_tokenizer.encode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> str:
UpperCAmelCase_ : Optional[Any] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
UpperCAmelCase_ : Optional[Any] = BasicTokenizer(do_lower_case=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Any:
UpperCAmelCase_ : List[str] = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def _SCREAMING_SNAKE_CASE ( self : int ) -> int:
UpperCAmelCase_ : Any = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[int]:
UpperCAmelCase_ : Tuple = BasicTokenizer(do_lower_case=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> str:
UpperCAmelCase_ : List[str] = BasicTokenizer(do_lower_case=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
UpperCAmelCase_ : Optional[int] = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def _SCREAMING_SNAKE_CASE ( self : str ) -> Optional[Any]:
UpperCAmelCase_ : Union[str, Any] = BasicTokenizer(do_lower_case=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
UpperCAmelCase_ : Tuple = BasicTokenizer(do_lower_case=lowerCAmelCase_ , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
UpperCAmelCase_ : Tuple = BasicTokenizer()
UpperCAmelCase_ : Dict = "a\n'll !!to?'d of, can't."
UpperCAmelCase_ : List[str] = ["a", "'", "ll", "!", "!", "to", "?", "'", "d", "of", ",", "can", "'", "t", "."]
self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_ ) , lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
UpperCAmelCase_ : int = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
UpperCAmelCase_ : Tuple = {}
for i, token in enumerate(lowerCAmelCase_ ):
UpperCAmelCase_ : Optional[int] = i
UpperCAmelCase_ : Optional[Any] = WordpieceTokenizer(vocab=lowerCAmelCase_ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Optional[int]:
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def _SCREAMING_SNAKE_CASE ( self : str ) -> Optional[Any]:
UpperCAmelCase_ : Dict = self.get_tokenizer()
UpperCAmelCase_ : List[str] = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowerCAmelCase_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(lowerCAmelCase_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Optional[Any]:
UpperCAmelCase_ : List[str] = self.tokenizer_class.from_pretrained("bert-base-uncased" )
UpperCAmelCase_ : Any = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : Tuple = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ )
UpperCAmelCase_ : Dict = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ , lowerCAmelCase_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def _SCREAMING_SNAKE_CASE ( self : Any ) -> str:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase_ : str = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
UpperCAmelCase_ : Tuple = tokenizer_r.encode_plus(
lowerCAmelCase_ , return_attention_mask=lowerCAmelCase_ , return_token_type_ids=lowerCAmelCase_ , return_offsets_mapping=lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ , )
UpperCAmelCase_ : Optional[int] = tokenizer_r.do_lower_case if hasattr(lowerCAmelCase_ , "do_lower_case" ) else False
UpperCAmelCase_ : List[Any] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase_ : List[Any] = ["的", "人", "有"]
UpperCAmelCase_ : Tuple = "".join(lowerCAmelCase_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase_ : Optional[Any] = True
UpperCAmelCase_ : Any = self.tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
UpperCAmelCase_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
UpperCAmelCase_ : Dict = tokenizer_p.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : Dict = tokenizer_r.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_ )
UpperCAmelCase_ : Any = tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = False
UpperCAmelCase_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase_ , **lowerCAmelCase_ )
UpperCAmelCase_ : int = tokenizer_r.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : List[str] = tokenizer_p.encode(lowerCAmelCase_ , add_special_tokens=lowerCAmelCase_ )
UpperCAmelCase_ : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_ )
UpperCAmelCase_ : List[str] = tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_ )
# it is expected that only the first Chinese character is not preceded by "##".
UpperCAmelCase_ : Tuple = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowerCAmelCase_ )
]
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
| 268
| 0
|
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 215
|
"""simple docstring"""
from __future__ import annotations
class __A :
"""simple docstring"""
def __init__( self , __A = 0 ) -> Dict:
a =key
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> list[str]:
assert isinstance(__A , __A ) and isinstance(__A , __A )
a =key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(__A ) ^ key ) for ch in content]
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> list[str]:
assert isinstance(__A , __A ) and isinstance(__A , __A )
a =key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(__A ) ^ key ) for ch in content]
def SCREAMING_SNAKE_CASE ( self , __A , __A = 0 ) -> str:
assert isinstance(__A , __A ) and isinstance(__A , __A )
a =key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
a =''''''
for ch in content:
ans += chr(ord(__A ) ^ key )
return ans
def SCREAMING_SNAKE_CASE ( self , __A , __A = 0 ) -> str:
assert isinstance(__A , __A ) and isinstance(__A , __A )
a =key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
a =''''''
for ch in content:
ans += chr(ord(__A ) ^ key )
return ans
def SCREAMING_SNAKE_CASE ( self , __A , __A = 0 ) -> bool:
assert isinstance(__A , __A ) and isinstance(__A , __A )
try:
with open(__A ) as fin, open('''encrypt.out''' , '''w+''' ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(__A , __A ) )
except OSError:
return False
return True
def SCREAMING_SNAKE_CASE ( self , __A , __A ) -> bool:
assert isinstance(__A , __A ) and isinstance(__A , __A )
try:
with open(__A ) as fin, open('''decrypt.out''' , '''w+''' ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(__A , __A ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 215
| 1
|
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {"""vocab_file""": """vocab.json"""}
snake_case_ = {
"""vocab_file""": {
"""mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
}
}
snake_case_ = {"""mgp-str""": 27}
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self :Dict , lowercase_ :Tuple , lowercase_ :Optional[int]="[GO]" , lowercase_ :Tuple="[GO]" , lowercase_ :Optional[Any]="[s]" , lowercase_ :List[str]="[GO]" , **lowercase_ :int ) -> List[str]:
super().__init__(
unk_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , pad_token=lowercase_ , **lowercase_ , )
with open(lowercase_ , encoding='utf-8' ) as vocab_handle:
UpperCAmelCase = json.load(lowercase_ )
UpperCAmelCase = {v: k for k, v in self.vocab.items()}
@property
def UpperCAmelCase__ ( self :List[str] ) -> Any:
return len(self.vocab )
def UpperCAmelCase__ ( self :Dict ) -> List[str]:
return dict(self.vocab , **self.added_tokens_encoder )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[Any] ) -> int:
UpperCAmelCase = []
for s in text:
char_tokens.extend(lowercase_ )
return char_tokens
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :Union[str, Any] ) -> Dict:
return self.vocab.get(lowercase_ , self.vocab.get(self.unk_token ) )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[int] ) -> Optional[Any]:
return self.decoder.get(lowercase_ )
def UpperCAmelCase__ ( self :List[str] , lowercase_ :str , lowercase_ :Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(lowercase_ ):
logger.error('Vocabulary path ({}) should be a directory'.format(lowercase_ ) )
return
UpperCAmelCase = os.path.join(
lowercase_ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
with open(lowercase_ , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=lowercase_ , ensure_ascii=lowercase_ ) + '\n' )
return (vocab_file,)
| 78
|
import math
def _SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
__UpperCamelCase : List[Any] = input("Enter message: ")
__UpperCamelCase : Optional[int] = int(input(F'Enter key [2-{len(_lowerCamelCase) - 1}]: '))
__UpperCamelCase : str = input("Encryption/Decryption [e/d]: ")
if mode.lower().startswith("e"):
__UpperCamelCase : List[str] = encrypt_message(_lowerCamelCase , _lowerCamelCase)
elif mode.lower().startswith("d"):
__UpperCamelCase : Dict = decrypt_message(_lowerCamelCase , _lowerCamelCase)
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(F'Output:\n{text + "|"}')
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : Optional[Any] = [""] * key
for col in range(_lowerCamelCase):
__UpperCamelCase : Any = col
while pointer < len(_lowerCamelCase):
cipher_text[col] += message[pointer]
pointer += key
return "".join(_lowerCamelCase)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int , _lowerCamelCase : str) -> str:
'''simple docstring'''
__UpperCamelCase : Any = math.ceil(len(_lowerCamelCase) / key)
__UpperCamelCase : Any = key
__UpperCamelCase : str = (num_cols * num_rows) - len(_lowerCamelCase)
__UpperCamelCase : Union[str, Any] = [""] * num_cols
__UpperCamelCase : Dict = 0
__UpperCamelCase : int = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
__UpperCamelCase : List[Any] = 0
row += 1
return "".join(_lowerCamelCase)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 232
| 0
|
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a =['image_processor', 'tokenizer']
__a ='OwlViTImageProcessor'
__a =('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self : Tuple , __a : List[Any]=None , __a : int=None , **__a : Any ):
_a = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __a , )
_a = kwargs.pop("feature_extractor" )
_a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__a , __a )
def __call__( self : Tuple , __a : Tuple=None , __a : Optional[Any]=None , __a : str=None , __a : Optional[Any]="max_length" , __a : Dict="np" , **__a : List[str] ):
if text is None and query_images is None and images is None:
raise ValueError(
"You have to specify at least one text or query image or image. All three cannot be none." )
if text is not None:
if isinstance(__a , __a ) or (isinstance(__a , __a ) and not isinstance(text[0] , __a )):
_a = [self.tokenizer(__a , padding=__a , return_tensors=__a , **__a )]
elif isinstance(__a , __a ) and isinstance(text[0] , __a ):
_a = []
# Maximum number of queries across batch
_a = max([len(__a ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(__a ) != max_num_queries:
_a = t + [" "] * (max_num_queries - len(__a ))
_a = self.tokenizer(__a , padding=__a , return_tensors=__a , **__a )
encodings.append(__a )
else:
raise TypeError("Input text should be a string, a list of strings or a nested list of strings" )
if return_tensors == "np":
_a = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
_a = np.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
_a = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
_a = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
_a = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 )
_a = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
_a = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 )
_a = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 )
else:
raise ValueError("Target return tensor type could not be returned" )
_a = BatchEncoding()
_a = input_ids
_a = attention_mask
if query_images is not None:
_a = BatchEncoding()
_a = self.image_processor(
__a , return_tensors=__a , **__a ).pixel_values
_a = query_pixel_values
if images is not None:
_a = self.image_processor(__a , return_tensors=__a , **__a )
if text is not None and images is not None:
_a = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
_a = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**__a ) , tensor_type=__a )
def UpperCamelCase__ ( self : List[Any] , *__a : Optional[Any] , **__a : Union[str, Any] ):
return self.image_processor.post_process(*__a , **__a )
def UpperCamelCase__ ( self : str , *__a : Tuple , **__a : str ):
return self.image_processor.post_process_object_detection(*__a , **__a )
def UpperCamelCase__ ( self : str , *__a : List[str] , **__a : Union[str, Any] ):
return self.image_processor.post_process_image_guided_detection(*__a , **__a )
def UpperCamelCase__ ( self : int , *__a : str , **__a : Optional[int] ):
return self.tokenizer.batch_decode(*__a , **__a )
def UpperCamelCase__ ( self : List[str] , *__a : Dict , **__a : Union[str, Any] ):
return self.tokenizer.decode(*__a , **__a )
@property
def UpperCamelCase__ ( self : List[Any] ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __a , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self : List[str] ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __a , )
return self.image_processor
| 346
|
'''simple docstring'''
import requests
lowerCAmelCase_ : List[Any] = 'YOUR API KEY'
def _lowerCamelCase ( lowercase : str , lowercase : str = giphy_api_key ) -> list:
_a = "+".join(query.split() )
_a = F'https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'
_a = requests.get(lowercase ).json()["data"]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('\n'.join(get_gifs('space ship')))
| 346
| 1
|
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : str=14 , lowerCamelCase_ : Optional[Any]=7 , lowerCamelCase_ : Dict=True , lowerCamelCase_ : str=True , lowerCamelCase_ : str=False , lowerCamelCase_ : Optional[int]=True , lowerCamelCase_ : int=99 , lowerCamelCase_ : List[str]=32 , lowerCamelCase_ : int=4 , lowerCamelCase_ : List[Any]=4 , lowerCamelCase_ : List[str]=4 , lowerCamelCase_ : Union[str, Any]=37 , lowerCamelCase_ : int="gelu" , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : Union[str, Any]=0.1 , lowerCamelCase_ : List[str]=5_12 , lowerCamelCase_ : Union[str, Any]=0.02 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = parent
SCREAMING_SNAKE_CASE : Optional[int] = batch_size
SCREAMING_SNAKE_CASE : Any = seq_length
SCREAMING_SNAKE_CASE : List[str] = is_training
SCREAMING_SNAKE_CASE : Optional[int] = use_input_mask
SCREAMING_SNAKE_CASE : Union[str, Any] = use_token_type_ids
SCREAMING_SNAKE_CASE : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE : str = vocab_size
SCREAMING_SNAKE_CASE : str = hidden_size
SCREAMING_SNAKE_CASE : List[Any] = rotary_dim
SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : int = intermediate_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE : List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Dict = vocab_size - 1
SCREAMING_SNAKE_CASE : str = vocab_size - 1
SCREAMING_SNAKE_CASE : List[Any] = vocab_size - 1
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : List[str] = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=lowerCamelCase_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Union[str, Any] = config_and_inputs
SCREAMING_SNAKE_CASE : Tuple = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase_ ( self : Optional[int] , lowerCamelCase_ : str , lowerCamelCase_ : Dict , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = 20
SCREAMING_SNAKE_CASE : Any = model_class_name(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = model.init_cache(input_ids.shape[0] , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
SCREAMING_SNAKE_CASE : Optional[int] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE : Any = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase_ , past_key_values=lowerCamelCase_ , position_ids=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Tuple = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE : str = model(
input_ids[:, -1:] , attention_mask=lowerCamelCase_ , past_key_values=outputs_cache.past_key_values , position_ids=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def lowerCamelCase_ ( self : List[Any] , lowerCamelCase_ : Tuple , lowerCamelCase_ : Any , lowerCamelCase_ : List[str] , lowerCamelCase_ : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = 20
SCREAMING_SNAKE_CASE : Dict = model_class_name(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
SCREAMING_SNAKE_CASE : str = model.init_cache(input_ids.shape[0] , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE : Any = model(
input_ids[:, :-1] , attention_mask=lowerCamelCase_ , past_key_values=lowerCamelCase_ , position_ids=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Tuple = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE : Dict = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=lowerCamelCase_ , position_ids=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCamelCase_ , attention_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class UpperCamelCase__ ( lowercase_ , lowercase_ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
SCREAMING_SNAKE_CASE__ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxGPTJModelTester(self )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
@tooslow
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = GPTaTokenizer.from_pretrained("""gpt2""" , pad_token="""<|endoftext|>""" , padding_side="""left""" )
SCREAMING_SNAKE_CASE : List[Any] = tokenizer(["""Hello this is a long string""", """Hey"""] , return_tensors="""np""" , padding=lowerCamelCase_ , truncation=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE : int = False
SCREAMING_SNAKE_CASE : Optional[Any] = model.config.eos_token_id
SCREAMING_SNAKE_CASE : str = jax.jit(model.generate )
SCREAMING_SNAKE_CASE : str = jit_generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , pad_token_id=tokenizer.pad_token_id ).sequences
SCREAMING_SNAKE_CASE : Tuple = tokenizer.batch_decode(lowerCamelCase_ , skip_special_tokens=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
@is_pt_flax_cross_test
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE : str = self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE : List[str] = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE : int = getattr(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE : int = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[int] = 1
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = 1
SCREAMING_SNAKE_CASE : Optional[int] = pt_model_class(lowerCamelCase_ ).eval()
SCREAMING_SNAKE_CASE : str = model_class(lowerCamelCase_ , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE : Tuple = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = fx_state
with torch.no_grad():
SCREAMING_SNAKE_CASE : Any = pt_model(**lowerCamelCase_ ).to_tuple()
SCREAMING_SNAKE_CASE : Any = fx_model(**lowerCamelCase_ ).to_tuple()
self.assertEqual(len(lowerCamelCase_ ) , len(lowerCamelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = model_class.from_pretrained(lowerCamelCase_ , from_pt=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = fx_model_loaded(**lowerCamelCase_ ).to_tuple()
self.assertEqual(
len(lowerCamelCase_ ) , len(lowerCamelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE : Dict = self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE : Dict = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE : int = getattr(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = pt_model_class(lowerCamelCase_ ).eval()
SCREAMING_SNAKE_CASE : Any = model_class(lowerCamelCase_ , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE : List[Any] = load_flax_weights_in_pytorch_model(lowerCamelCase_ , fx_model.params )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE : Union[str, Any] = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
SCREAMING_SNAKE_CASE : Dict = 1
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : Tuple = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
SCREAMING_SNAKE_CASE : List[str] = pt_model(**lowerCamelCase_ ).to_tuple()
SCREAMING_SNAKE_CASE : Optional[Any] = fx_model(**lowerCamelCase_ ).to_tuple()
self.assertEqual(len(lowerCamelCase_ ) , len(lowerCamelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = pt_model_class.from_pretrained(lowerCamelCase_ , from_flax=lowerCamelCase_ )
with torch.no_grad():
SCREAMING_SNAKE_CASE : str = pt_model_loaded(**lowerCamelCase_ ).to_tuple()
self.assertEqual(
len(lowerCamelCase_ ) , len(lowerCamelCase_ ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(lowerCamelCase_ , lowerCamelCase_ ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE : Union[str, Any] = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE : Optional[int] = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase_ )
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'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = """ylacombe/bark-small"""
SCREAMING_SNAKE_CASE : Tuple = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE : str = """en_speaker_1"""
SCREAMING_SNAKE_CASE : Optional[int] = """This is a test string"""
SCREAMING_SNAKE_CASE : Optional[int] = """speaker_embeddings_path.json"""
SCREAMING_SNAKE_CASE : List[Any] = """speaker_embeddings"""
def lowerCamelCase_ ( self : int , **lowerCamelCase_ : int ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.checkpoint , **lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : List[str] = BarkProcessor(tokenizer=lowerCamelCase_ )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE : List[Any] = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE : int = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
SCREAMING_SNAKE_CASE : List[str] = 35
SCREAMING_SNAKE_CASE : List[Any] = 2
SCREAMING_SNAKE_CASE : int = 8
SCREAMING_SNAKE_CASE : Optional[int] = {
"""semantic_prompt""": np.ones(lowerCamelCase_ ),
"""coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
"""fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
SCREAMING_SNAKE_CASE : Tuple = processor(text=self.input_string , voice_preset=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowerCamelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
SCREAMING_SNAKE_CASE : List[str] = os.path.join(self.tmpdirname , """file.npz""" )
np.savez(lowerCamelCase_ , **lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = processor(text=self.input_string , voice_preset=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowerCamelCase_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
SCREAMING_SNAKE_CASE : Optional[Any] = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE : Any = BarkProcessor(tokenizer=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = processor(text=self.input_string )
SCREAMING_SNAKE_CASE : Tuple = tokenizer(
self.input_string , padding="""max_length""" , max_length=2_56 , add_special_tokens=lowerCamelCase_ , return_attention_mask=lowerCamelCase_ , return_token_type_ids=lowerCamelCase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
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'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
SCREAMING_SNAKE_CASE_: Any =logging.get_logger(__name__)
if is_vision_available():
import PIL
class __A ( UpperCamelCase__ ):
a__ : List[str] = ["""pixel_values"""]
def __init__(self : str , __a : bool = True , __a : Dict[str, int] = None , __a : PILImageResampling = PILImageResampling.BICUBIC , __a : bool = True , __a : Dict[str, int] = None , __a : bool = True , __a : Union[int, float] = 1 / 255 , __a : bool = True , __a : Optional[Union[float, List[float]]] = None , __a : Optional[Union[float, List[float]]] = None , __a : bool = True , **__a : Tuple , ):
super().__init__(**__a )
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 224}
UpperCAmelCase_ = get_size_dict(__a , default_to_square=__a )
UpperCAmelCase_ = crop_size if crop_size is not None else {"height": 224, "width": 224}
UpperCAmelCase_ = get_size_dict(__a , default_to_square=__a , param_name="crop_size" )
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = resample
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_rescale
UpperCAmelCase_ = rescale_factor
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
UpperCAmelCase_ = image_std if image_std is not None else OPENAI_CLIP_STD
UpperCAmelCase_ = do_convert_rgb
def _lowercase (self : Dict , __a : np.ndarray , __a : Dict[str, int] , __a : PILImageResampling = PILImageResampling.BICUBIC , __a : Optional[Union[str, ChannelDimension]] = None , **__a : Tuple , ):
UpperCAmelCase_ = get_size_dict(__a , default_to_square=__a )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
UpperCAmelCase_ = get_resize_output_image_size(__a , size=size["shortest_edge"] , default_to_square=__a )
return resize(__a , size=__a , resample=__a , data_format=__a , **__a )
def _lowercase (self : Any , __a : np.ndarray , __a : Dict[str, int] , __a : Optional[Union[str, ChannelDimension]] = None , **__a : Optional[Any] , ):
UpperCAmelCase_ = get_size_dict(__a )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__a , size=(size["height"], size["width"]) , data_format=__a , **__a )
def _lowercase (self : Dict , __a : np.ndarray , __a : Union[int, float] , __a : Optional[Union[str, ChannelDimension]] = None , **__a : int , ):
return rescale(__a , scale=__a , data_format=__a , **__a )
def _lowercase (self : int , __a : np.ndarray , __a : Union[float, List[float]] , __a : Union[float, List[float]] , __a : Optional[Union[str, ChannelDimension]] = None , **__a : Tuple , ):
return normalize(__a , mean=__a , std=__a , data_format=__a , **__a )
def _lowercase (self : int , __a : ImageInput , __a : bool = None , __a : Dict[str, int] = None , __a : PILImageResampling = None , __a : bool = None , __a : int = None , __a : bool = None , __a : float = None , __a : bool = None , __a : Optional[Union[float, List[float]]] = None , __a : Optional[Union[float, List[float]]] = None , __a : bool = None , __a : Optional[Union[str, TensorType]] = None , __a : Optional[ChannelDimension] = ChannelDimension.FIRST , **__a : int , ):
UpperCAmelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ = size if size is not None else self.size
UpperCAmelCase_ = get_size_dict(__a , param_name="size" , default_to_square=__a )
UpperCAmelCase_ = resample if resample is not None else self.resample
UpperCAmelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ = get_size_dict(__a , param_name="crop_size" , default_to_square=__a )
UpperCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ = image_std if image_std is not None else self.image_std
UpperCAmelCase_ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
UpperCAmelCase_ = make_list_of_images(__a )
if not valid_images(__a ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
UpperCAmelCase_ = [convert_to_rgb(__a ) for image in images]
# All transformations expect numpy arrays.
UpperCAmelCase_ = [to_numpy_array(__a ) for image in images]
if do_resize:
UpperCAmelCase_ = [self.resize(image=__a , size=__a , resample=__a ) for image in images]
if do_center_crop:
UpperCAmelCase_ = [self.center_crop(image=__a , size=__a ) for image in images]
if do_rescale:
UpperCAmelCase_ = [self.rescale(image=__a , scale=__a ) for image in images]
if do_normalize:
UpperCAmelCase_ = [self.normalize(image=__a , mean=__a , std=__a ) for image in images]
UpperCAmelCase_ = [to_channel_dimension_format(__a , __a ) for image in images]
UpperCAmelCase_ = {"pixel_values": images}
return BatchFeature(data=__a , tensor_type=__a )
| 351
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_: int ={
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: List[str] =['MaskFormerFeatureExtractor']
SCREAMING_SNAKE_CASE_: Union[str, Any] =['MaskFormerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_: Dict =[
'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'MaskFormerForInstanceSegmentation',
'MaskFormerModel',
'MaskFormerPreTrainedModel',
]
SCREAMING_SNAKE_CASE_: List[str] =[
'MaskFormerSwinBackbone',
'MaskFormerSwinModel',
'MaskFormerSwinPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_: Dict =_LazyModule(__name__, globals()['__file__'], _import_structure)
| 106
| 0
|
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , a : Optional[int] , a : str=13 , a : str=7 , a : List[Any]=True , a : List[str]=True , a : int=True , a : Any=True , a : Tuple=99 , a : int=32 , a : Union[str, Any]=5 , a : str=4 , a : Optional[Any]=37 , a : Optional[Any]="gelu" , a : Any=0.1 , a : Optional[Any]=0.1 , a : Any=512 , a : int=16 , a : Optional[int]=2 , a : Optional[int]=0.02 , a : str=4 , ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : List[str] = batch_size
SCREAMING_SNAKE_CASE : Optional[Any] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE : Any = use_token_type_ids
SCREAMING_SNAKE_CASE : Optional[Any] = use_labels
SCREAMING_SNAKE_CASE : List[Any] = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : int = type_vocab_size
SCREAMING_SNAKE_CASE : List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : str = num_choices
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE : Optional[Any] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE : Dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =True
lowerCamelCase__ =(
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = FlaxRoFormerModelTester(self )
@slow
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE : int = model_class_name.from_pretrained("junnyu/roformer_chinese_small" , from_pt=a )
SCREAMING_SNAKE_CASE : Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
SCREAMING_SNAKE_CASE : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE : Tuple = model(a )[0]
SCREAMING_SNAKE_CASE : Optional[int] = 5_0000
SCREAMING_SNAKE_CASE : Any = (1, 6, vocab_size)
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Any = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , a , atol=1e-4 ) )
| 76
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "ctc_proj",
"mask_emb": "masked_spec_embed",
}
lowercase_ = [
"ctc_proj",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase (__A , __A , __A , __A , __A , __A):
"""simple docstring"""
for attribute in key.split('''.'''):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
_a = '''lm_head'''
_a = getattr(__A , __A)
if weight_type is not None:
_a = getattr(__A , __A).shape
else:
_a = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_a = value
elif weight_type == "weight_g":
_a = value
elif weight_type == "weight_v":
_a = value
elif weight_type == "bias":
_a = value
else:
_a = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''')
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = []
_a = fairseq_model.state_dict()
_a = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
_a = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == '''group''' , )
_a = True
else:
for key, mapped_key in MAPPING.items():
_a = '''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
_a = True
if "*" in mapped_key:
_a = name.split(__A)[0].split('''.''')[-2]
_a = mapped_key.replace('''*''' , __A)
if "weight_g" in name:
_a = '''weight_g'''
elif "weight_v" in name:
_a = '''weight_v'''
elif "bias" in name:
_a = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_a = '''weight'''
else:
_a = None
set_recursively(__A , __A , __A , __A , __A , __A)
continue
if not is_used:
unused_weights.append(__A)
logger.warning(F'''Unused weights: {unused_weights}''')
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
_a = full_name.split('''conv_layers.''')[-1]
_a = name.split('''.''')
_a = int(items[0])
_a = int(items[1])
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_a = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_a = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_a = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_a = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
else:
unused_weights.append(__A)
@torch.no_grad()
def lowerCAmelCase (__A , __A , __A=None , __A=None , __A=True):
"""simple docstring"""
if config_path is not None:
_a = UniSpeechConfig.from_pretrained(__A)
else:
_a = UniSpeechConfig()
if is_finetuned:
if dict_path:
_a = Dictionary.load_from_json(__A)
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_a = target_dict.pad_index
_a = target_dict.bos_index
_a = target_dict.eos_index
_a = len(target_dict.symbols)
_a = os.path.join(__A , '''vocab.json''')
if not os.path.isdir(__A):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__A))
return
os.makedirs(__A , exist_ok=__A)
_a = target_dict.indices
# fairseq has the <pad> and <s> switched
_a = 42
_a = 43
with open(__A , '''w''' , encoding='''utf-8''') as vocab_handle:
json.dump(__A , __A)
_a = WavaVecaPhonemeCTCTokenizer(
__A , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=__A , )
_a = True if config.feat_extract_norm == '''layer''' else False
_a = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
_a = WavaVecaProcessor(feature_extractor=__A , tokenizer=__A)
processor.save_pretrained(__A)
_a = UniSpeechForCTC(__A)
else:
_a = UniSpeechForPreTraining(__A)
if is_finetuned:
_a , _a , _a = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
else:
_a , _a , _a = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])
_a = model[0].eval()
recursively_load_weights(__A , __A , __A)
hf_unispeech.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
lowercase_ = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 211
| 0
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class lowercase ( __snake_case ):
"""simple docstring"""
_a = 'pegasus'
_a = ['past_key_values']
_a = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , UpperCamelCase_=50265 , UpperCamelCase_=1024 , UpperCamelCase_=12 , UpperCamelCase_=4096 , UpperCamelCase_=16 , UpperCamelCase_=12 , UpperCamelCase_=4096 , UpperCamelCase_=16 , UpperCamelCase_=0.0 , UpperCamelCase_=0.0 , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_="gelu" , UpperCamelCase_=1024 , UpperCamelCase_=0.1 , UpperCamelCase_=0.0 , UpperCamelCase_=0.0 , UpperCamelCase_=0.02 , UpperCamelCase_=0 , UpperCamelCase_=False , UpperCamelCase_=0 , UpperCamelCase_=1 , UpperCamelCase_=1 , **UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = vocab_size
UpperCamelCase__ :Dict = max_position_embeddings
UpperCamelCase__ :Union[str, Any] = d_model
UpperCamelCase__ :List[Any] = encoder_ffn_dim
UpperCamelCase__ :Union[str, Any] = encoder_layers
UpperCamelCase__ :int = encoder_attention_heads
UpperCamelCase__ :Optional[Any] = decoder_ffn_dim
UpperCamelCase__ :List[str] = decoder_layers
UpperCamelCase__ :List[str] = decoder_attention_heads
UpperCamelCase__ :int = dropout
UpperCamelCase__ :List[Any] = attention_dropout
UpperCamelCase__ :Any = activation_dropout
UpperCamelCase__ :Dict = activation_function
UpperCamelCase__ :Optional[int] = init_std
UpperCamelCase__ :int = encoder_layerdrop
UpperCamelCase__ :List[Any] = decoder_layerdrop
UpperCamelCase__ :List[str] = use_cache
UpperCamelCase__ :int = encoder_layers
UpperCamelCase__ :int = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=a_ , eos_token_id=a_ , is_encoder_decoder=a_ , decoder_start_token_id=a_ , forced_eos_token_id=a_ , **a_ , )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return self.d_model
| 352
|
'''simple docstring'''
from collections import Counter
from timeit import timeit
def a ( __a = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def a ( __a = "" ) -> bool:
'''simple docstring'''
if len(__a ) == 0:
return True
UpperCamelCase__ :List[Any] = input_str.replace(''' ''' , '''''' ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
UpperCamelCase__ :dict[str, int] = {}
for character in lower_case_input_str:
UpperCamelCase__ :Optional[int] = character_freq_dict.get(__a , 0 ) + 1
UpperCamelCase__ :List[str] = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def a ( __a = "" ) -> None:
'''simple docstring'''
print('''\nFor string = ''' , __a , ''':''' )
print(
'''> can_string_be_rearranged_as_palindrome_counter()''' , '''\tans =''' , can_string_be_rearranged_as_palindrome_counter(__a ) , '''\ttime =''' , timeit(
'''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)''' , setup='''import __main__ as z''' , ) , '''seconds''' , )
print(
'''> can_string_be_rearranged_as_palindrome()''' , '''\tans =''' , can_string_be_rearranged_as_palindrome(__a ) , '''\ttime =''' , timeit(
'''z.can_string_be_rearranged_as_palindrome(z.check_str)''' , setup='''import __main__ as z''' , ) , '''seconds''' , )
if __name__ == "__main__":
__snake_case = input(
'''Enter string to determine if it can be rearranged as a palindrome or not: '''
).strip()
benchmark(check_str)
__snake_case = can_string_be_rearranged_as_palindrome_counter(check_str)
print(F"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")
| 219
| 0
|
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = False )-> list[float]:
'''simple docstring'''
if radian_mode:
return [magnitude * cos(lowerCAmelCase_ ), magnitude * sin(lowerCAmelCase_ )]
return [magnitude * cos(radians(lowerCAmelCase_ ) ), magnitude * sin(radians(lowerCAmelCase_ ) )]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 10**-1 )-> bool:
'''simple docstring'''
_UpperCAmelCase : NDArray[floataa] = cross(lowerCAmelCase_ , lowerCAmelCase_ )
_UpperCAmelCase : float = sum(lowerCAmelCase_ )
return abs(lowerCAmelCase_ ) < eps
if __name__ == "__main__":
# Test to check if it works
A_ : str = array(
[
polar_force(718.4, 1_8_0 - 3_0),
polar_force(879.54, 4_5),
polar_force(1_0_0, -9_0),
]
)
A_ : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
A_ : List[str] = array(
[
polar_force(3_0 * 9.81, 1_5),
polar_force(2_1_5, 1_8_0 - 4_5),
polar_force(2_6_4, 9_0 - 3_0),
]
)
A_ : Tuple = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
A_ : Dict = array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])
A_ : Union[str, Any] = array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod()
| 215
|
'''simple docstring'''
from __future__ import annotations
import queue
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ) -> str:
_UpperCAmelCase : Optional[Any] = data
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Union[str, Any] = None
def snake_case_ ( )-> TreeNode:
'''simple docstring'''
print("""\n********Press N to stop entering at any point of time********\n""" )
_UpperCAmelCase : Any = input("""Enter the value of the root node: """ ).strip().lower()
_UpperCAmelCase : queue.Queue = queue.Queue()
_UpperCAmelCase : List[str] = TreeNode(int(lowerCAmelCase_ ) )
q.put(lowerCAmelCase_ )
while not q.empty():
_UpperCAmelCase : str = q.get()
_UpperCAmelCase : Any = F'''Enter the left node of {node_found.data}: '''
_UpperCAmelCase : Union[str, Any] = input(lowerCAmelCase_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_UpperCAmelCase : List[str] = TreeNode(int(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[int] = left_node
q.put(lowerCAmelCase_ )
_UpperCAmelCase : Dict = F'''Enter the right node of {node_found.data}: '''
_UpperCAmelCase : Tuple = input(lowerCAmelCase_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_UpperCAmelCase : Any = TreeNode(int(lowerCAmelCase_ ) )
_UpperCAmelCase : Optional[Any] = right_node
q.put(lowerCAmelCase_ )
raise
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
_UpperCAmelCase : queue.Queue = queue.Queue()
q.put(lowerCAmelCase_ )
while not q.empty():
_UpperCAmelCase : Dict = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
_UpperCAmelCase : queue.Queue = queue.Queue()
q.put(lowerCAmelCase_ )
while not q.empty():
_UpperCAmelCase : Optional[int] = []
while not q.empty():
_UpperCAmelCase : Optional[int] = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(lowerCAmelCase_ )
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
_UpperCAmelCase : list[TreeNode] = []
_UpperCAmelCase : Optional[Any] = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(lowerCAmelCase_ )
_UpperCAmelCase : Union[str, Any] = n.left
# end of while means current node doesn't have left child
_UpperCAmelCase : int = stack.pop()
# start to traverse its right child
_UpperCAmelCase : Any = n.right
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
_UpperCAmelCase : list[TreeNode] = []
_UpperCAmelCase : Optional[Any] = node
while n or stack:
while n:
stack.append(lowerCAmelCase_ )
_UpperCAmelCase : Tuple = n.left
_UpperCAmelCase : Union[str, Any] = stack.pop()
print(n.data , end=""",""" )
_UpperCAmelCase : Any = n.right
def snake_case_ ( lowerCAmelCase_ )-> None:
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or not node:
return
_UpperCAmelCase ,_UpperCAmelCase : str = [], []
_UpperCAmelCase : Dict = node
stacka.append(lowerCAmelCase_ )
while stacka: # to find the reversed order of post order, store it in stack2
_UpperCAmelCase : Optional[int] = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(lowerCAmelCase_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def snake_case_ ( lowerCAmelCase_ = "" , lowerCAmelCase_=50 , lowerCAmelCase_="*" )-> str:
'''simple docstring'''
if not s:
return "\n" + width * char
_UpperCAmelCase ,_UpperCAmelCase : Optional[Any] = divmod(width - len(lowerCAmelCase_ ) - 2 , 2 )
return F'''{left * char} {s} {(left + extra) * char}'''
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("""Binary Tree Traversals"""))
A_ : TreeNode = build_tree()
print(prompt("""Pre Order Traversal"""))
pre_order(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal"""))
in_order(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal"""))
post_order(node)
print(prompt() + """\n""")
print(prompt("""Level Order Traversal"""))
level_order(node)
print(prompt() + """\n""")
print(prompt("""Actual Level Order Traversal"""))
level_order_actual(node)
print("""*""" * 5_0 + """\n""")
print(prompt("""Pre Order Traversal - Iteration Version"""))
pre_order_iter(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal - Iteration Version"""))
in_order_iter(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal - Iteration Version"""))
post_order_iter(node)
print(prompt())
| 215
| 1
|
'''simple docstring'''
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase_ ( A , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase_ = GPTaTokenizer
lowerCamelCase_ = GPTaTokenizerFast
lowerCamelCase_ = True
lowerCamelCase_ = {'''add_prefix_space''': True}
lowerCamelCase_ = False
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_SCREAMING_SNAKE_CASE = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
_SCREAMING_SNAKE_CASE = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
_SCREAMING_SNAKE_CASE = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_SCREAMING_SNAKE_CASE = {"unk_token": "<unk>"}
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(__lowerCamelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__lowerCamelCase ) )
def lowerCAmelCase_ ( self : Any , **__lowerCamelCase : Any ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def lowerCAmelCase_ ( self : Optional[Any] , **__lowerCamelCase : List[str] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **__lowerCamelCase )
def lowerCAmelCase_ ( self : Any , __lowerCamelCase : int ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = "lower newer"
_SCREAMING_SNAKE_CASE = "lower newer"
return input_text, output_text
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_SCREAMING_SNAKE_CASE = "lower newer"
_SCREAMING_SNAKE_CASE = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(__lowerCamelCase , add_prefix_space=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
_SCREAMING_SNAKE_CASE = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(add_prefix_space=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = "lower newer"
# Testing tokenization
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(__lowerCamelCase , add_prefix_space=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing conversion to ids without special tokens
_SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase , add_prefix_space=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing conversion to ids with special tokens
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(add_prefix_space=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_prefix_space=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase , __lowerCamelCase )
# Testing the unknown token
_SCREAMING_SNAKE_CASE = tokens + [rust_tokenizer.unk_token]
_SCREAMING_SNAKE_CASE = [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , __lowerCamelCase )
def lowerCAmelCase_ ( self : str , *__lowerCamelCase : Optional[int] , **__lowerCamelCase : List[Any] ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Dict , __lowerCamelCase : int=1_5 ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
# Simple input
_SCREAMING_SNAKE_CASE = "This is a simple input"
_SCREAMING_SNAKE_CASE = ["This is a simple input 1", "This is a simple input 2"]
_SCREAMING_SNAKE_CASE = ("This is a simple input", "This is a pair")
_SCREAMING_SNAKE_CASE = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Simple input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(__lowerCamelCase , tokenizer_r.encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" )
# Pair input
self.assertRaises(
__lowerCamelCase , tokenizer_r.batch_encode_plus , __lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , )
def lowerCAmelCase_ ( self : List[str] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token="<pad>" )
# Simple input
_SCREAMING_SNAKE_CASE = "This is a simple input"
_SCREAMING_SNAKE_CASE = ["This is a simple input looooooooong", "This is a simple input"]
_SCREAMING_SNAKE_CASE = ("This is a simple input", "This is a pair")
_SCREAMING_SNAKE_CASE = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
_SCREAMING_SNAKE_CASE = tokenizer.pad_token_id
_SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , padding="max_length" , max_length=3_0 , return_tensors="np" )
_SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , padding=__lowerCamelCase , truncate=__lowerCamelCase , return_tensors="np" )
_SCREAMING_SNAKE_CASE = tokenizer(*__lowerCamelCase , padding="max_length" , max_length=6_0 , return_tensors="np" )
_SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase , padding=__lowerCamelCase , truncate=__lowerCamelCase , return_tensors="np" )
# s
# test single string max_length padding
self.assertEqual(out_s["input_ids"].shape[-1] , 3_0 )
self.assertTrue(pad_token_id in out_s["input_ids"] )
self.assertTrue(0 in out_s["attention_mask"] )
# s2
# test automatic padding
self.assertEqual(out_sa["input_ids"].shape[-1] , 3_3 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa["input_ids"][0] )
self.assertFalse(0 in out_sa["attention_mask"][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa["input_ids"][1] )
self.assertTrue(0 in out_sa["attention_mask"][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p["input_ids"].shape[-1] , 6_0 )
self.assertTrue(pad_token_id in out_p["input_ids"] )
self.assertTrue(0 in out_p["attention_mask"] )
# p2
# test automatic padding pair
self.assertEqual(out_pa["input_ids"].shape[-1] , 5_2 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa["input_ids"][0] )
self.assertFalse(0 in out_pa["attention_mask"][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa["input_ids"][1] )
self.assertTrue(0 in out_pa["attention_mask"][1] )
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = "$$$"
_SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=__lowerCamelCase , add_bos_token=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = "This is a simple input"
_SCREAMING_SNAKE_CASE = ["This is a simple input 1", "This is a simple input 2"]
_SCREAMING_SNAKE_CASE = tokenizer.bos_token_id
_SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = tokenizer(__lowerCamelCase )
self.assertEqual(out_s.input_ids[0] , __lowerCamelCase )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_SCREAMING_SNAKE_CASE = tokenizer.decode(out_s.input_ids )
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __lowerCamelCase )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
pass
def lowerCAmelCase_ ( self : Optional[int] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [self.get_tokenizer(do_lower_case=__lowerCamelCase , add_bos_token=__lowerCamelCase )]
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
_SCREAMING_SNAKE_CASE = "Encode this."
_SCREAMING_SNAKE_CASE = "This one too please."
_SCREAMING_SNAKE_CASE = tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
encoded_sequence += tokenizer.encode(__lowerCamelCase , add_special_tokens=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = tokenizer.encode_plus(
__lowerCamelCase , __lowerCamelCase , add_special_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = encoded_sequence_dict["input_ids"]
_SCREAMING_SNAKE_CASE = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
_SCREAMING_SNAKE_CASE = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowerCamelCase )
]
_SCREAMING_SNAKE_CASE = [x for x in filtered_sequence if x is not None]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
@require_tokenizers
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = "A photo of a cat"
_SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
self.assertEqual(__lowerCamelCase , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
tokenizer.save_pretrained("test_opt" )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("./test_opt" )
_SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
self.assertEqual(__lowerCamelCase , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/opt-350m" , use_slow=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = "A photo of a cat"
_SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
# Same as above
self.assertEqual(__lowerCamelCase , [2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
@unittest.skip("This test is failing because of a bug in the fast tokenizer" )
def lowerCAmelCase_ ( self : Tuple ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/opt-350m" , from_slow=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = "bos"
_SCREAMING_SNAKE_CASE = tokenizer.get_vocab()["bos"]
_SCREAMING_SNAKE_CASE = "A photo of a cat"
_SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
# We changed the bos token
self.assertEqual(__lowerCamelCase , [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
tokenizer.save_pretrained("./tok" )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("./tok" )
self.assertTrue(tokenizer.is_fast )
_SCREAMING_SNAKE_CASE = tokenizer.encode(
__lowerCamelCase , )
self.assertEqual(__lowerCamelCase , [3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8] )
| 360
|
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}
class lowercase_ ( A ):
"""simple docstring"""
lowerCamelCase_ = '''efficientnet'''
def __init__( self : Optional[Any] , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 6_0_0 , __lowerCamelCase : float = 2.0 , __lowerCamelCase : float = 3.1 , __lowerCamelCase : int = 8 , __lowerCamelCase : List[int] = [3, 3, 5, 3, 5, 5, 3] , __lowerCamelCase : List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , __lowerCamelCase : List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , __lowerCamelCase : List[int] = [] , __lowerCamelCase : List[int] = [1, 2, 2, 2, 1, 2, 1] , __lowerCamelCase : List[int] = [1, 2, 2, 3, 3, 4, 1] , __lowerCamelCase : List[int] = [1, 6, 6, 6, 6, 6, 6] , __lowerCamelCase : float = 0.2_5 , __lowerCamelCase : str = "swish" , __lowerCamelCase : int = 2_5_6_0 , __lowerCamelCase : str = "mean" , __lowerCamelCase : float = 0.0_2 , __lowerCamelCase : float = 0.0_0_1 , __lowerCamelCase : float = 0.9_9 , __lowerCamelCase : float = 0.5 , __lowerCamelCase : float = 0.2 , **__lowerCamelCase : Tuple , ):
"""simple docstring"""
super().__init__(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = width_coefficient
_SCREAMING_SNAKE_CASE = depth_coefficient
_SCREAMING_SNAKE_CASE = depth_divisor
_SCREAMING_SNAKE_CASE = kernel_sizes
_SCREAMING_SNAKE_CASE = in_channels
_SCREAMING_SNAKE_CASE = out_channels
_SCREAMING_SNAKE_CASE = depthwise_padding
_SCREAMING_SNAKE_CASE = strides
_SCREAMING_SNAKE_CASE = num_block_repeats
_SCREAMING_SNAKE_CASE = expand_ratios
_SCREAMING_SNAKE_CASE = squeeze_expansion_ratio
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dim
_SCREAMING_SNAKE_CASE = pooling_type
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = batch_norm_eps
_SCREAMING_SNAKE_CASE = batch_norm_momentum
_SCREAMING_SNAKE_CASE = dropout_rate
_SCREAMING_SNAKE_CASE = drop_connect_rate
_SCREAMING_SNAKE_CASE = sum(__lowerCamelCase ) * 4
class lowercase_ ( A ):
"""simple docstring"""
lowerCamelCase_ = version.parse('''1.11''' )
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
return 1e-5
| 111
| 0
|
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : Dict = ["""image_processor""", """tokenizer"""]
lowerCAmelCase_ : Union[str, Any] = """OwlViTImageProcessor"""
lowerCAmelCase_ : Union[str, Any] = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self : List[Any] , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : Optional[Any]=None , **_UpperCAmelCase : List[str] ):
"""simple docstring"""
UpperCAmelCase__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _UpperCAmelCase , )
UpperCAmelCase__ = kwargs.pop("""feature_extractor""" )
UpperCAmelCase__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(_UpperCAmelCase , _UpperCAmelCase )
def __call__( self : List[Any] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : Any="max_length" , _UpperCAmelCase : Dict="np" , **_UpperCAmelCase : List[Any] ):
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
"""You have to specify at least one text or query image or image. All three cannot be none.""" )
if text is not None:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ) or (isinstance(_UpperCAmelCase , _UpperCAmelCase ) and not isinstance(text[0] , _UpperCAmelCase )):
UpperCAmelCase__ = [self.tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )]
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(text[0] , _UpperCAmelCase ):
UpperCAmelCase__ = []
# Maximum number of queries across batch
UpperCAmelCase__ = max([len(_UpperCAmelCase ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(_UpperCAmelCase ) != max_num_queries:
UpperCAmelCase__ = t + [""" """] * (max_num_queries - len(_UpperCAmelCase ))
UpperCAmelCase__ = self.tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )
encodings.append(_UpperCAmelCase )
else:
raise TypeError("""Input text should be a string, a list of strings or a nested list of strings""" )
if return_tensors == "np":
UpperCAmelCase__ = np.concatenate([encoding["""input_ids"""] for encoding in encodings] , axis=0 )
UpperCAmelCase__ = np.concatenate([encoding["""attention_mask"""] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
UpperCAmelCase__ = jnp.concatenate([encoding["""input_ids"""] for encoding in encodings] , axis=0 )
UpperCAmelCase__ = jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
UpperCAmelCase__ = torch.cat([encoding["""input_ids"""] for encoding in encodings] , dim=0 )
UpperCAmelCase__ = torch.cat([encoding["""attention_mask"""] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
UpperCAmelCase__ = tf.stack([encoding["""input_ids"""] for encoding in encodings] , axis=0 )
UpperCAmelCase__ = tf.stack([encoding["""attention_mask"""] for encoding in encodings] , axis=0 )
else:
raise ValueError("""Target return tensor type could not be returned""" )
UpperCAmelCase__ = BatchEncoding()
UpperCAmelCase__ = input_ids
UpperCAmelCase__ = attention_mask
if query_images is not None:
UpperCAmelCase__ = BatchEncoding()
UpperCAmelCase__ = self.image_processor(
_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase ).pixel_values
UpperCAmelCase__ = query_pixel_values
if images is not None:
UpperCAmelCase__ = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )
if text is not None and images is not None:
UpperCAmelCase__ = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
UpperCAmelCase__ = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**_UpperCAmelCase ) , tensor_type=_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Dict , *_UpperCAmelCase : List[str] , **_UpperCAmelCase : Any ):
"""simple docstring"""
return self.image_processor.post_process(*_UpperCAmelCase , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Any , *_UpperCAmelCase : Tuple , **_UpperCAmelCase : str ):
"""simple docstring"""
return self.image_processor.post_process_object_detection(*_UpperCAmelCase , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , *_UpperCAmelCase : Optional[Any] , **_UpperCAmelCase : Dict ):
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*_UpperCAmelCase , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Any , *_UpperCAmelCase : str , **_UpperCAmelCase : Dict ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , *_UpperCAmelCase : str , **_UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase )
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _UpperCAmelCase , )
return self.image_processor_class
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _UpperCAmelCase , )
return self.image_processor
| 346
|
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _UpperCamelCase ( ):
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
UpperCAmelCase__ = """__test_patch_submodule_mock__"""
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def _UpperCamelCase ( ):
'''simple docstring'''
assert _test_patching.open is open
UpperCAmelCase__ = """__test_patch_submodule_builtin_mock__"""
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_missing_mock__"""
with patch_submodule(_test_patching , """pandas.read_csv""" , SCREAMING_SNAKE_CASE__ ):
pass
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_missing_builtin_mock__"""
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ) is None
with patch_submodule(_test_patching , """len""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.len is mock
assert _test_patching.len is len
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_start_and_stop_mock__"""
UpperCAmelCase__ = patch_submodule(_test_patching , """open""" , SCREAMING_SNAKE_CASE__ )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def _UpperCamelCase ( ):
'''simple docstring'''
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
UpperCAmelCase__ = """__test_patch_submodule_successive_join__"""
UpperCAmelCase__ = """__test_patch_submodule_successive_dirname__"""
UpperCAmelCase__ = """__test_patch_submodule_successive_rename__"""
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , """os.rename""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.path.join""" , SCREAMING_SNAKE_CASE__ ):
with patch_submodule(_test_patching , """os.path.dirname""" , SCREAMING_SNAKE_CASE__ ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = """__test_patch_submodule_doesnt_exist_mock__"""
with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ):
pass
with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , SCREAMING_SNAKE_CASE__ ):
pass
| 346
| 1
|
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase_ = 2
lowercase_ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__lowerCAmelCase )
if n > 1:
factors.append(__lowerCAmelCase )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313
|
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__lowerCAmelCase ):
lowercase_ = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(__lowerCAmelCase )[1] in (".py", ".ipynb"):
yield os.path.join(__lowerCAmelCase , __lowerCAmelCase ).lstrip("""./""" )
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
return F'''{i * " "}*''' if i else "\n##"
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase_ = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(__lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
print(F'''{md_prefix(__lowerCAmelCase )} {new_part.replace("_" , " " ).title()}''' )
return new_path
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = "." ) -> None:
'''simple docstring'''
lowercase_ = """"""
for filepath in sorted(good_file_paths(__lowerCAmelCase ) ):
lowercase_ , lowercase_ = os.path.split(__lowerCAmelCase )
if filepath != old_path:
lowercase_ = print_path(__lowerCAmelCase , __lowerCAmelCase )
lowercase_ = (filepath.count(os.sep ) + 1) if filepath else 0
lowercase_ = F'''{filepath}/{filename}'''.replace(""" """ , """%20""" )
lowercase_ = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'''{md_prefix(__lowerCAmelCase )} [{filename}]({url})''' )
if __name__ == "__main__":
print_directory_md(".")
| 313
| 1
|
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a_ (a_ ):
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ = None , ):
super().__init__()
self.register_modules(transformer=lowercase_ , vae=lowercase_ , scheduler=lowercase_ )
# create a imagenet -> id dictionary for easier use
_lowerCAmelCase : int = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(""",""" ):
_lowerCAmelCase : Union[str, Any] = int(lowercase_ )
_lowerCAmelCase : int = dict(sorted(self.labels.items() ) )
def __UpperCamelCase ( self , snake_case_ ):
if not isinstance(lowercase_ , lowercase_ ):
_lowerCAmelCase : Any = list(lowercase_ )
for l in label:
if l not in self.labels:
raise ValueError(
f'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self , snake_case_ , snake_case_ = 4.0 , snake_case_ = None , snake_case_ = 5_0 , snake_case_ = "pil" , snake_case_ = True , ):
_lowerCAmelCase : Optional[Any] = len(lowercase_ )
_lowerCAmelCase : Any = self.transformer.config.sample_size
_lowerCAmelCase : int = self.transformer.config.in_channels
_lowerCAmelCase : List[str] = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowercase_ , device=self.device , dtype=self.transformer.dtype , )
_lowerCAmelCase : Any = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
_lowerCAmelCase : List[Any] = torch.tensor(lowercase_ , device=self.device ).reshape(-1 )
_lowerCAmelCase : Dict = torch.tensor([1_0_0_0] * batch_size , device=self.device )
_lowerCAmelCase : List[Any] = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowercase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
_lowerCAmelCase : List[Any] = latent_model_input[: len(lowercase_ ) // 2]
_lowerCAmelCase : Optional[Any] = torch.cat([half, half] , dim=0 )
_lowerCAmelCase : List[str] = self.scheduler.scale_model_input(lowercase_ , lowercase_ )
_lowerCAmelCase : Optional[int] = t
if not torch.is_tensor(lowercase_ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
_lowerCAmelCase : Any = latent_model_input.device.type == '''mps'''
if isinstance(lowercase_ , lowercase_ ):
_lowerCAmelCase : Dict = torch.floataa if is_mps else torch.floataa
else:
_lowerCAmelCase : List[str] = torch.intaa if is_mps else torch.intaa
_lowerCAmelCase : List[Any] = torch.tensor([timesteps] , dtype=lowercase_ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
_lowerCAmelCase : List[Any] = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
_lowerCAmelCase : List[str] = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
_lowerCAmelCase : Dict = self.transformer(
lowercase_ , timestep=lowercase_ , class_labels=lowercase_ ).sample
# perform guidance
if guidance_scale > 1:
_lowerCAmelCase : Tuple = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
_lowerCAmelCase : Any = torch.split(lowercase_ , len(lowercase_ ) // 2 , dim=0 )
_lowerCAmelCase : Optional[Any] = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
_lowerCAmelCase : Optional[int] = torch.cat([half_eps, half_eps] , dim=0 )
_lowerCAmelCase : Union[str, Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
_lowerCAmelCase : Any = torch.split(lowercase_ , lowercase_ , dim=1 )
else:
_lowerCAmelCase : int = noise_pred
# compute previous image: x_t -> x_t-1
_lowerCAmelCase : int = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ ).prev_sample
if guidance_scale > 1:
_lowerCAmelCase : int = latent_model_input.chunk(2 , dim=0 )
else:
_lowerCAmelCase : Optional[int] = latent_model_input
_lowerCAmelCase : List[Any] = 1 / self.vae.config.scaling_factor * latents
_lowerCAmelCase : List[Any] = self.vae.decode(lowercase_ ).sample
_lowerCAmelCase : Tuple = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_lowerCAmelCase : Tuple = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_lowerCAmelCase : List[str] = self.numpy_to_pil(lowercase_ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowercase_ )
| 309
|
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar('''T''')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
lowercase__ = 42 # Cache store of keys
lowercase__ = 42 # References of the keys in cache
lowercase__ = 10 # Maximum capacity of cache
def __init__( self : Dict ,lowercase_ : int ):
lowerCAmelCase__ : str = deque()
lowerCAmelCase__ : Any = set()
if not n:
lowerCAmelCase__ : Optional[Any] = sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
lowerCAmelCase__ : int = n
def __lowerCAmelCase ( self : str ,lowercase_ : T ):
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
lowerCAmelCase__ : Any = self.dq_store.pop()
self.key_reference.remove(lowercase_ )
else:
self.dq_store.remove(lowercase_ )
self.dq_store.appendleft(lowercase_ )
self.key_reference.add(lowercase_ )
def __lowerCAmelCase ( self : int ):
for k in self.dq_store:
print(lowercase_ )
def __repr__( self : Tuple ):
return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}'
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : LRUCache[str | int] = LRUCache(4)
lru_cache.refer('''A''')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('''A''')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 106
| 0
|
import copy
import re
class lowercase_ :
A__ : Optional[Any] = """hp"""
A__ : Union[str, Any] = {}
A__ : Optional[int] = None
@classmethod
def lowerCamelCase_ ( cls , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = prefix
UpperCamelCase_ = defaults
cls.build_naming_info()
@staticmethod
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
if len(__UpperCamelCase ) == 0:
return ""
UpperCamelCase_ = None
if any(char.isdigit() for char in word ):
raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(__UpperCamelCase ) + 1 ):
UpperCamelCase_ = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
UpperCamelCase_ = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(__UpperCamelCase ):
UpperCamelCase_ = """"""
while integer != 0:
UpperCamelCase_ = chr(ord("""A""" ) + integer % 1_0 ) + s
integer //= 1_0
return s
UpperCamelCase_ = 0
while True:
UpperCamelCase_ = word + """#""" + int_to_alphabetic(__UpperCamelCase )
if sword in info["reverse_short_word"]:
continue
else:
UpperCamelCase_ = sword
break
UpperCamelCase_ = short_word
UpperCamelCase_ = word
return short_word
@staticmethod
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = param_name.split("""_""" )
UpperCamelCase_ = [TrialShortNamer.shortname_for_word(__UpperCamelCase , __UpperCamelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
UpperCamelCase_ = ["""""", """_"""]
for separator in separators:
UpperCamelCase_ = separator.join(__UpperCamelCase )
if shortname not in info["reverse_short_param"]:
UpperCamelCase_ = shortname
UpperCamelCase_ = param_name
return shortname
return param_name
@staticmethod
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = TrialShortNamer.shortname_for_key(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase_ = short_name
UpperCamelCase_ = param_name
@classmethod
def lowerCamelCase_ ( cls ):
"""simple docstring"""
if cls.NAMING_INFO is not None:
return
UpperCamelCase_ = {
"""short_word""": {},
"""reverse_short_word""": {},
"""short_param""": {},
"""reverse_short_param""": {},
}
UpperCamelCase_ = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(__UpperCamelCase , __UpperCamelCase )
UpperCamelCase_ = info
@classmethod
def lowerCamelCase_ ( cls , __UpperCamelCase ):
"""simple docstring"""
cls.build_naming_info()
assert cls.PREFIX is not None
UpperCamelCase_ = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(f'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
UpperCamelCase_ = cls.NAMING_INFO["""short_param"""][k]
if isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCamelCase_ = 1 if v else 0
UpperCamelCase_ = """""" if isinstance(__UpperCamelCase , (int, float) ) else """-"""
UpperCamelCase_ = f'''{key}{sep}{v}'''
name.append(__UpperCamelCase )
return "_".join(__UpperCamelCase )
@classmethod
def lowerCamelCase_ ( cls , __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
UpperCamelCase_ = []
else:
UpperCamelCase_ = repr.split("""_""" )
UpperCamelCase_ = {}
for value in values:
if "-" in value:
UpperCamelCase_ , UpperCamelCase_ = value.split("""-""" )
else:
UpperCamelCase_ = re.sub("""[0-9.]""" , """""" , __UpperCamelCase )
UpperCamelCase_ = float(re.sub("""[^0-9.]""" , """""" , __UpperCamelCase ) )
UpperCamelCase_ = cls.NAMING_INFO["""reverse_short_param"""][p_k]
UpperCamelCase_ = p_v
for k in cls.DEFAULTS:
if k not in parameters:
UpperCamelCase_ = cls.DEFAULTS[k]
return parameters
| 261
|
from __future__ import annotations
def lowerCamelCase__ ( a__ : list[list[int]] ) -> int:
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(a__ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(a__ ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 261
| 1
|
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase_ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) == 0
assert and_gate(1 , 1 ) == 1
if __name__ == "__main__":
test_and_gate()
print(and_gate(1, 0))
print(and_gate(0, 0))
print(and_gate(0, 1))
print(and_gate(1, 1))
| 19
|
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCamelCase : str = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_sentencepiece_available():
import sentencepiece as sp
__lowerCamelCase : Any = 5
__lowerCamelCase : Dict = 10
@require_sentencepiece
@require_tokenizers
class __snake_case ( lowerCamelCase_ , unittest.TestCase ):
lowerCAmelCase_ = SpeechaTextTokenizer
lowerCAmelCase_ = False
lowerCAmelCase_ = True
def __a ( self : Tuple ):
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE__ = sp.SentencePieceProcessor()
spm_model.Load(_lowercase )
SCREAMING_SNAKE_CASE__ = ["""<s>""", """<pad>""", """</s>""", """<unk>"""]
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(_lowercase ) )]
SCREAMING_SNAKE_CASE__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
SCREAMING_SNAKE_CASE__ = Path(self.tmpdirname )
save_json(_lowercase , save_dir / VOCAB_FILES_NAMES["""vocab_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(_lowercase , save_dir / VOCAB_FILES_NAMES["""spm_file"""] )
SCREAMING_SNAKE_CASE__ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __a ( self : Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """<pad>"""
SCREAMING_SNAKE_CASE__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase )
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(_lowercase ) , 10_01 )
def __a ( self : List[Any] ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_01 )
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowercase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowercase ) , [2_89, 50, 14, 1_74, 3_86] , )
SCREAMING_SNAKE_CASE__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowercase , [SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """."""] , )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_tokens_to_ids(_lowercase )
self.assertListEqual(_lowercase , [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
SCREAMING_SNAKE_CASE__ = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertListEqual(
_lowercase , [SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""] , )
@slow
def __a ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {"""input_ids""": [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowercase , model_name="""facebook/s2t-small-mustc-en-de-st""" , revision="""a14f04cf0776c02f62a8cb800cf7909e15ea23ad""" , )
@require_sentencepiece
class __snake_case ( unittest.TestCase ):
lowerCAmelCase_ = "valhalla/s2t_mustc_multilinguial_medium"
lowerCAmelCase_ = "C'est trop cool"
lowerCAmelCase_ = "Esto es genial"
@classmethod
def __a ( cls : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def __a ( self : Dict ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id["""pt"""] , 4 )
self.assertEqual(self.tokenizer.lang_code_to_id["""ru"""] , 6 )
self.assertEqual(self.tokenizer.lang_code_to_id["""it"""] , 9 )
self.assertEqual(self.tokenizer.lang_code_to_id["""de"""] , 11 )
def __a ( self : Union[str, Any] ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size , 1_00_00 )
def __a ( self : int ):
"""simple docstring"""
self.assertIn(_lowercase , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE__ = [ES_CODE, 4, 16_01, 47, 76_47, 2]
SCREAMING_SNAKE_CASE__ = self.tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
SCREAMING_SNAKE_CASE__ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertNotIn(self.tokenizer.eos_token , _lowercase )
def __a ( self : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """fr"""
SCREAMING_SNAKE_CASE__ = self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0] , _lowercase )
self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id )
def __a ( self : Optional[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """fr"""
self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] )
SCREAMING_SNAKE_CASE__ = """es"""
self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
| 219
| 0
|
from collections.abc import Callable
import numpy as np
def UpperCamelCase (lowercase_: Callable , lowercase_: float , lowercase_: float , lowercase_: float , lowercase_: float ) -> np.array:
A__ : List[str] = int(np.ceil((x_end - xa) / step_size ) )
A__ : Union[str, Any] = np.zeros((n + 1,) )
A__ : Dict = ya
A__ : Optional[int] = xa
for k in range(lowercase_ ):
A__ : Tuple = y[k] + step_size * ode_func(lowercase_ , y[k] )
A__ : Tuple = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 141
|
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def UpperCamelCase (lowercase_: int ) -> str:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class _a (__magic_name__ ):
'''simple docstring'''
@staticmethod
def __A ( A__ ):
A__ : Any = parser.add_parser("""download""" )
download_parser.add_argument(
"""--cache-dir""" , type=A__ , default=A__ , help="""Path to location to store the models""" )
download_parser.add_argument(
"""--force""" , action="""store_true""" , help="""Force the model to be download even if already in cache-dir""" )
download_parser.add_argument(
"""--trust-remote-code""" , action="""store_true""" , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine""" , )
download_parser.add_argument("""model""" , type=A__ , help="""Name of the model to download""" )
download_parser.set_defaults(func=A__ )
def __init__( self , A__ , A__ , A__ , A__ ):
A__ : Union[str, Any] = model
A__ : Dict = cache
A__ : str = force
A__ : Tuple = trust_remote_code
def __A ( self ):
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 141
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 244
|
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : str = Dict[str, Any]
__UpperCAmelCase : int = List[Prediction]
@add_end_docstrings(__lowerCamelCase )
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self : int , *A : Optional[int] , **A : Optional[int] ):
super().__init__(*A , **A )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , """vision""" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def UpperCAmelCase__ ( self : List[str] , **A : Tuple ):
__snake_case: List[str] = {}
if "threshold" in kwargs:
__snake_case: Optional[Any] = kwargs["""threshold"""]
return {}, {}, postprocess_kwargs
def __call__( self : int , *A : Optional[Any] , **A : Tuple ):
return super().__call__(*A , **A )
def UpperCAmelCase__ ( self : Optional[int] , A : str ):
__snake_case: Optional[Any] = load_image(A )
__snake_case: Dict = torch.IntTensor([[image.height, image.width]] )
__snake_case: str = self.image_processor(images=[image] , return_tensors="""pt""" )
if self.tokenizer is not None:
__snake_case: Optional[Any] = self.tokenizer(text=inputs["""words"""] , boxes=inputs["""boxes"""] , return_tensors="""pt""" )
__snake_case: Any = target_size
return inputs
def UpperCAmelCase__ ( self : Optional[int] , A : Dict ):
__snake_case: int = model_inputs.pop("""target_size""" )
__snake_case: int = self.model(**A )
__snake_case: Any = outputs.__class__({"""target_size""": target_size, **outputs} )
if self.tokenizer is not None:
__snake_case: Optional[int] = model_inputs["""bbox"""]
return model_outputs
def UpperCAmelCase__ ( self : List[Any] , A : Optional[int] , A : Union[str, Any]=0.9 ):
__snake_case: Optional[Any] = model_outputs["""target_size"""]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
__snake_case , __snake_case: Union[str, Any] = target_size[0].tolist()
def unnormalize(A : Tuple ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1_000),
(height * bbox[1] / 1_000),
(width * bbox[2] / 1_000),
(height * bbox[3] / 1_000),
] ) )
__snake_case , __snake_case: Optional[int] = model_outputs["""logits"""].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
__snake_case: List[Any] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__snake_case: int = [unnormalize(A ) for bbox in model_outputs["""bbox"""].squeeze(0 )]
__snake_case: int = ["""score""", """label""", """box"""]
__snake_case: List[Any] = [dict(zip(A , A ) ) for vals in zip(scores.tolist() , A , A ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__snake_case: Tuple = self.image_processor.post_process_object_detection(A , A , A )
__snake_case: Optional[Any] = raw_annotations[0]
__snake_case: int = raw_annotation["""scores"""]
__snake_case: int = raw_annotation["""labels"""]
__snake_case: Optional[Any] = raw_annotation["""boxes"""]
__snake_case: Union[str, Any] = scores.tolist()
__snake_case: List[str] = [self.model.config.idalabel[label.item()] for label in labels]
__snake_case: List[str] = [self._get_bounding_box(A ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__snake_case: List[Any] = ["""score""", """label""", """box"""]
__snake_case: Dict = [
dict(zip(A , A ) )
for vals in zip(raw_annotation["""scores"""] , raw_annotation["""labels"""] , raw_annotation["""boxes"""] )
]
return annotation
def UpperCAmelCase__ ( self : Optional[Any] , A : "torch.Tensor" ):
if self.framework != "pt":
raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""" )
__snake_case , __snake_case , __snake_case , __snake_case: Union[str, Any] = box.int().tolist()
__snake_case: Optional[Any] = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 111
| 0
|
'''simple docstring'''
import argparse
import datetime
def _snake_case ( A ) -> str:
lowerCAmelCase__ = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday""",
}
lowerCAmelCase__ = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(lowercase__ ) < 11:
raise ValueError('''Must be 10 characters long''' )
# Get month
lowerCAmelCase__ = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('''Month must be between 1 - 12''' )
lowerCAmelCase__ = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get day
lowerCAmelCase__ = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('''Date must be between 1 - 31''' )
# Get second separator
lowerCAmelCase__ = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get year
lowerCAmelCase__ = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
'''Year out of range. There has to be some sort of limit...right?''' )
# Get datetime obj for validation
lowerCAmelCase__ = datetime.date(int(lowercase__ ) , int(lowercase__ ) , int(lowercase__ ) )
# Start math
if m <= 2:
lowerCAmelCase__ = y - 1
lowerCAmelCase__ = m + 12
# maths var
lowerCAmelCase__ = int(str(lowercase__ )[:2] )
lowerCAmelCase__ = int(str(lowercase__ )[2:] )
lowerCAmelCase__ = int(2.6 * m - 5.39 )
lowerCAmelCase__ = int(c / 4 )
lowerCAmelCase__ = int(k / 4 )
lowerCAmelCase__ = int(d + k )
lowerCAmelCase__ = int(t + u + v + x )
lowerCAmelCase__ = int(z - (2 * c) )
lowerCAmelCase__ = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('''The date was evaluated incorrectly. Contact developer.''' )
# Response
lowerCAmelCase__ = F"""Your date {date_input}, is a {days[str(lowercase__ )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase = argparse.ArgumentParser(
description=(
'''Find out what day of the week nearly any date is or was. Enter '''
'''date as a string in the mm-dd-yyyy or mm/dd/yyyy format'''
)
)
parser.add_argument(
'''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)'''
)
__UpperCAmelCase = parser.parse_args()
zeller(args.date_input)
| 356
|
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = [
'''word_embeddings_layernorm.weight''',
'''word_embeddings_layernorm.bias''',
'''input_layernorm.weight''',
'''input_layernorm.bias''',
'''post_attention_layernorm.weight''',
'''post_attention_layernorm.bias''',
'''self_attention.dense.bias''',
'''mlp.dense_4h_to_h.bias''',
'''ln_f.weight''',
'''ln_f.bias''',
]
__UpperCAmelCase = [
'''mlp.dense_4h_to_h.weight''',
'''self_attention.dense.weight''',
]
def _snake_case ( A , A ) -> Optional[Any]:
lowerCAmelCase__ = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
lowerCAmelCase__ = int(re.match(R'''.*layer_(\d*).*''' , A )[1] )
layer_number -= 3
return F"""h.{layer_number}.""" + key
def _snake_case ( A ) -> Optional[int]:
if dtype == torch.bool:
return 1 / 8
lowerCAmelCase__ = re.search(R'''[^\d](\d+)$''' , str(A ) )
if bit_search is None:
raise ValueError(F"""`dtype` is not a valid dtype: {dtype}.""" )
lowerCAmelCase__ = int(bit_search.groups()[0] )
return bit_size // 8
def _snake_case ( A , A , A , A , A ) -> Dict:
# Construct model
if bloom_config_file == "":
lowerCAmelCase__ = BloomConfig()
else:
lowerCAmelCase__ = BloomConfig.from_json_file(A )
if shard_model:
lowerCAmelCase__ = os.listdir(A )
lowerCAmelCase__ = sorted(filter(lambda A : s.startswith('''layer''' ) and "model_00" in s , A ) )
lowerCAmelCase__ = {'''weight_map''': {}, '''metadata''': {}}
lowerCAmelCase__ = 0
lowerCAmelCase__ = None
lowerCAmelCase__ = BloomConfig()
for j, file in enumerate(A ):
print('''Processing file: {}'''.format(A ) )
lowerCAmelCase__ = None
for i in range(A ):
# load all TP files
lowerCAmelCase__ = file.replace('''model_00''' , F"""model_0{i}""" )
lowerCAmelCase__ = torch.load(os.path.join(A , A ) , map_location='''cpu''' )
# Rename keys in the transformers names
lowerCAmelCase__ = list(temp.keys() )
for key in keys:
lowerCAmelCase__ = temp.pop(A )
if tensors is None:
lowerCAmelCase__ = temp
else:
for key in tensors.keys():
if any(key.endswith(A ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
lowerCAmelCase__ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
lowerCAmelCase__ = torch.cat([tensors[key], temp[key]] , dim=A )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(A ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
lowerCAmelCase__ = tensors[key] / pretraining_tp
torch.save(
A , os.path.join(
A , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(A ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
lowerCAmelCase__ = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
lowerCAmelCase__ = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(A ) ).zfill(5 ) )
lowerCAmelCase__ = BloomConfig()
lowerCAmelCase__ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
lowerCAmelCase__ = total_size
with open(A , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
with open(os.path.join(A , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f:
lowerCAmelCase__ = json.dumps(A , indent=2 , sort_keys=A ) + '''\n'''
f.write(A )
else:
lowerCAmelCase__ = BloomModel(A )
lowerCAmelCase__ = os.listdir(A )
lowerCAmelCase__ = sorted(filter(lambda A : s.startswith('''layer''' ) and "model_00" in s , A ) )
lowerCAmelCase__ = None
for i, file in enumerate(A ):
lowerCAmelCase__ = None
for i in range(A ):
# load all TP files
lowerCAmelCase__ = file.replace('''model_00''' , F"""model_0{i}""" )
lowerCAmelCase__ = torch.load(os.path.join(A , A ) , map_location='''cpu''' )
# Rename keys in the transformers names
lowerCAmelCase__ = list(temp.keys() )
for key in keys:
lowerCAmelCase__ = temp.pop(A )
if tensors is None:
lowerCAmelCase__ = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(A ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
lowerCAmelCase__ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
lowerCAmelCase__ = torch.cat([tensors[key], temp[key]] , dim=A )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(A ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
lowerCAmelCase__ = tensors[key] / pretraining_tp
lowerCAmelCase__ = model.load_state_dict(A , strict=A )
assert not other_keys.unexpected_keys, F"""The keys {other_keys.unexpected_keys} are unexpected"""
if missing_keys is None:
lowerCAmelCase__ = set(other_keys.missing_keys )
else:
lowerCAmelCase__ = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, F"""The keys {missing_keys} are missing"""
# Save pytorch-model
os.makedirs(A , exist_ok=A )
lowerCAmelCase__ = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
lowerCAmelCase__ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}""" )
if config.torch_dtype is not None:
lowerCAmelCase__ = model.to(config.torch_dtype )
torch.save(model.state_dict() , A )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(A , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bloom_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path to the Megatron-LM checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--bloom_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--shard_model''',
action='''store_true''',
help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
)
parser.add_argument(
'''--pretraining_tp''',
default=4,
type=int,
help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
)
__UpperCAmelCase = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 228
| 0
|
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Dict = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
SCREAMING_SNAKE_CASE : List[Any] = Vector()
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Optional[int] = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_lowerCamelCase ) , '''(0,0,0,0,0,1)''' )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Tuple = Vector([1, 2, 3, 4] )
self.assertEqual(len(_lowerCamelCase ) , 4 )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Dict = Vector([1, 2] )
SCREAMING_SNAKE_CASE : str = Vector([1, 2, 3, 4, 5] )
SCREAMING_SNAKE_CASE : List[Any] = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
SCREAMING_SNAKE_CASE : Tuple = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.2_3_6 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.4_1_6 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.6_1_6 , 3 )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Dict = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE : List[str] = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : List[str] = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE : List[Any] = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : int = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE : Optional[int] = Vector([2, -1, 4] ) # for test of dot product
SCREAMING_SNAKE_CASE : str = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '''(3.0,6.0,9.0)''' )
self.assertEqual((a * b) , 0 )
def __lowerCAmelCase ( self ) ->None:
self.assertEqual(str(zero_vector(10 ) ).count('''0''' ) , 10 )
def __lowerCAmelCase ( self ) ->None:
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '''(0,1,0)''' )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : int = Vector([1, 2, 3] )
SCREAMING_SNAKE_CASE : Union[str, Any] = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _lowerCamelCase , _lowerCamelCase ) ) , '''(3,4,7)''' )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Tuple = Vector([1, 0, 0, 0, 0, 0] )
SCREAMING_SNAKE_CASE : str = x.copy()
self.assertEqual(str(_lowerCamelCase ) , str(_lowerCamelCase ) )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Dict = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_lowerCamelCase ) , '''(0,1,0)''' )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : int = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n''' , str(_lowerCamelCase ) )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Any = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE : Optional[int] = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_lowerCamelCase , _lowerCamelCase ) )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : List[str] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE : str = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_lowerCamelCase , _lowerCamelCase ) )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Tuple = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : int = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
SCREAMING_SNAKE_CASE : Optional[Any] = Vector([1, 2, 3] )
self.assertEqual('''(14,32,50)''' , str(a * x ) )
self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n''' , str(a * 2 ) )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n''' , str(_lowerCamelCase ) )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : List[str] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.0_1 )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : str = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE : List[Any] = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n''' , str(a + b ) )
def __lowerCAmelCase ( self ) ->None:
SCREAMING_SNAKE_CASE : Union[str, Any] = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
SCREAMING_SNAKE_CASE : int = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n''' , str(a - b ) )
def __lowerCAmelCase ( self ) ->None:
self.assertEqual(
'''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n''' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 313
|
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = tweepy.OAuthHandler(a__ , a__ )
auth.set_access_token(a__ , a__ )
SCREAMING_SNAKE_CASE : List[str] = tweepy.API(a__ )
# initialize a list to hold all the tweepy Tweets
SCREAMING_SNAKE_CASE : Any = []
# make initial request for most recent tweets (200 is the maximum allowed count)
SCREAMING_SNAKE_CASE : List[Any] = api.user_timeline(screen_name=a__ , count=200 )
# save most recent tweets
alltweets.extend(a__ )
# save the id of the oldest tweet less one
SCREAMING_SNAKE_CASE : Tuple = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(a__ ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
SCREAMING_SNAKE_CASE : Any = api.user_timeline(
screen_name=a__ , count=200 , max_id=a__ )
# save most recent tweets
alltweets.extend(a__ )
# update the id of the oldest tweet less one
SCREAMING_SNAKE_CASE : Dict = alltweets[-1].id - 1
print(F"""...{len(a__ )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
SCREAMING_SNAKE_CASE : Optional[Any] = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , '''w''' ) as f:
SCREAMING_SNAKE_CASE : List[Any] = csv.writer(a__ )
writer.writerow(['''id''', '''created_at''', '''text'''] )
writer.writerows(a__ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 313
| 1
|
from __future__ import annotations
from typing import Any
class __lowerCAmelCase :
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ = 0 ) -> None:
'''simple docstring'''
snake_case_ : Tuple = row, column
snake_case_ : Optional[int] = [[default_value for c in range(__magic_name__ )] for r in range(__magic_name__ )]
def __str__(self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = F'''Matrix consist of {self.row} rows and {self.column} columns\n'''
# Make string identifier
snake_case_ : Dict = 0
for row_vector in self.array:
for obj in row_vector:
snake_case_ : Tuple = max(__magic_name__ , len(str(__magic_name__ ) ) )
snake_case_ : Dict = F'''%{max_element_length}s'''
# Make string and return
def single_line(__magic_name__ ) -> str:
nonlocal string_format_identifier
snake_case_ : Any = '''['''
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(__magic_name__ ) for row_vector in self.array )
return s
def __repr__(self ) -> str:
'''simple docstring'''
return str(self )
def lowerCamelCase (self , __magic_name__ ) -> bool:
'''simple docstring'''
if not (isinstance(__magic_name__ , (list, tuple) ) and len(__magic_name__ ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__(self , __magic_name__ ) -> Any:
'''simple docstring'''
assert self.validate_indicies(__magic_name__ )
return self.array[loc[0]][loc[1]]
def __setitem__(self , __magic_name__ , __magic_name__ ) -> None:
'''simple docstring'''
assert self.validate_indicies(__magic_name__ )
snake_case_ : List[str] = value
def __add__(self , __magic_name__ ) -> Matrix:
'''simple docstring'''
assert isinstance(__magic_name__ , __magic_name__ )
assert self.row == another.row and self.column == another.column
# Add
snake_case_ : List[str] = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
snake_case_ : str = self[r, c] + another[r, c]
return result
def __neg__(self ) -> Matrix:
'''simple docstring'''
snake_case_ : Optional[Any] = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
snake_case_ : int = -self[r, c]
return result
def __sub__(self , __magic_name__ ) -> Matrix:
'''simple docstring'''
return self + (-another)
def __mul__(self , __magic_name__ ) -> Matrix:
'''simple docstring'''
if isinstance(__magic_name__ , (int, float) ): # Scalar multiplication
snake_case_ : Tuple = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
snake_case_ : Dict = self[r, c] * another
return result
elif isinstance(__magic_name__ , __magic_name__ ): # Matrix multiplication
assert self.column == another.row
snake_case_ : str = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
snake_case_ : str = F'''Unsupported type given for another ({type(__magic_name__ )})'''
raise TypeError(__magic_name__ )
def lowerCamelCase (self ) -> Matrix:
'''simple docstring'''
snake_case_ : Tuple = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
snake_case_ : Tuple = self[r, c]
return result
def lowerCamelCase (self , __magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
assert isinstance(__magic_name__ , __magic_name__ ) and isinstance(__magic_name__ , __magic_name__ )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
snake_case_ : str = v.transpose()
snake_case_ : Dict = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
snake_case_ : Optional[int] = Matrix(3 , 3 , 0 )
for i in range(3 ):
snake_case_ : Any = 1
print(f'''a^(-1) is {ainv}''' )
# u, v
snake_case_ : List[str] = Matrix(3 , 1 , 0 )
snake_case_ : Optional[Any] = 1, 2, -3
snake_case_ : Union[str, Any] = Matrix(3 , 1 , 0 )
snake_case_ : Tuple = 4, -2, 5
print(f'''u is {u}''' )
print(f'''v is {v}''' )
print(f'''uv^T is {u * v.transpose()}''' )
# Sherman Morrison
print(f'''(a + uv^T)^(-1) is {ainv.sherman_morrison(_UpperCamelCase , _UpperCamelCase )}''' )
def lowerCamelCase_ ( ) -> None:
"""simple docstring"""
import doctest
doctest.testmod()
testa()
| 355
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''XGLMTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = ['''XGLMTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XGLMForCausalLM''',
'''XGLMModel''',
'''XGLMPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''FlaxXGLMForCausalLM''',
'''FlaxXGLMModel''',
'''FlaxXGLMPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ = [
'''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXGLMForCausalLM''',
'''TFXGLMModel''',
'''TFXGLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 279
| 0
|
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class snake_case__ :
_snake_case : float
_snake_case : TreeNode | None = None
_snake_case : TreeNode | None = None
def _lowerCamelCase( a ):
# Validation
def is_valid_tree(a ) -> bool:
if node is None:
return True
if not isinstance(a , a ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(a ):
raise ValueError(
"Each node should be type of TreeNode and data should be float." )
def is_binary_search_tree_recursive_check(
a , a , a ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , a , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , a )
)
return is_binary_search_tree_recursive_check(a , -float("inf" ) , float("inf" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 261
|
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case__ ( snake_case_, snake_case_ ):
@register_to_config
def __init__( self , lowerCamelCase = 768 , ):
super().__init__()
__a = nn.Parameter(torch.zeros(1 , lowerCamelCase ) )
__a = nn.Parameter(torch.ones(1 , lowerCamelCase ) )
def a__ ( self , lowerCamelCase = None , lowerCamelCase = None , ):
__a = nn.Parameter(self.mean.to(lowerCamelCase ).to(lowerCamelCase ) )
__a = nn.Parameter(self.std.to(lowerCamelCase ).to(lowerCamelCase ) )
return self
def a__ ( self , lowerCamelCase ):
__a = (embeds - self.mean) * 1.0 / self.std
return embeds
def a__ ( self , lowerCamelCase ):
__a = (embeds * self.std) + self.mean
return embeds
| 261
| 1
|
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
UpperCAmelCase = threading.Lock()
UpperCAmelCase = None
UpperCAmelCase = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
UpperCAmelCase = logging.WARNING
UpperCAmelCase = True
def lowerCamelCase () -> Tuple:
lowercase :int = os.getenv('''TRANSFORMERS_VERBOSITY''' , a_)
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
F"""has to be one of: { ', '.join(log_levels.keys()) }""")
return _default_log_level
def lowerCamelCase () -> str:
return __name__.split('''.''')[0]
def lowerCamelCase () -> logging.Logger:
return logging.getLogger(_get_library_name())
def lowerCamelCase () -> None:
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
lowercase :str = logging.StreamHandler() # Set sys.stderr as stream.
lowercase :Tuple = sys.stderr.flush
# Apply our default configuration to the library root logger.
lowercase :Union[str, Any] = _get_library_root_logger()
library_root_logger.addHandler(_default_handler)
library_root_logger.setLevel(_get_default_logging_level())
lowercase :List[str] = False
def lowerCamelCase () -> None:
global _default_handler
with _lock:
if not _default_handler:
return
lowercase :Optional[int] = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler)
library_root_logger.setLevel(logging.NOTSET)
lowercase :str = None
def lowerCamelCase () -> Optional[int]:
return log_levels
def lowerCamelCase (a_ :Optional[str] = None) -> logging.Logger:
if name is None:
lowercase :Optional[Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(a_)
def lowerCamelCase () -> int:
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def lowerCamelCase (a_ :int) -> None:
_configure_library_root_logger()
_get_library_root_logger().setLevel(a_)
def lowerCamelCase () -> Any:
return set_verbosity(a_)
def lowerCamelCase () -> Optional[int]:
return set_verbosity(a_)
def lowerCamelCase () -> int:
return set_verbosity(a_)
def lowerCamelCase () -> Optional[Any]:
return set_verbosity(a_)
def lowerCamelCase () -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler)
def lowerCamelCase () -> None:
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler)
def lowerCamelCase (a_ :logging.Handler) -> None:
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(a_)
def lowerCamelCase (a_ :logging.Handler) -> None:
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(a_)
def lowerCamelCase () -> None:
_configure_library_root_logger()
lowercase :List[Any] = False
def lowerCamelCase () -> None:
_configure_library_root_logger()
lowercase :int = True
def lowerCamelCase () -> None:
lowercase :Union[str, Any] = _get_library_root_logger().handlers
for handler in handlers:
lowercase :str = logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s''')
handler.setFormatter(a_)
def lowerCamelCase () -> None:
lowercase :Union[str, Any] = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(a_)
def lowerCamelCase (self :Tuple , *a_ :List[Any] , **a_ :Dict) -> Union[str, Any]:
lowercase :Tuple = os.getenv('''TRANSFORMERS_NO_ADVISORY_WARNINGS''' , a_)
if no_advisory_warnings:
return
self.warning(*a_ , **a_)
UpperCAmelCase = warning_advice
@functools.lru_cache(a_)
def lowerCamelCase (self :List[Any] , *a_ :Optional[int] , **a_ :str) -> str:
self.warning(*a_ , **a_)
UpperCAmelCase = warning_once
class __magic_name__ :
def __init__( self : Any , *snake_case__ : Tuple , **snake_case__ : int ): # pylint: disable=unused-argument
'''simple docstring'''
lowercase :str = args[0] if args else None
def __iter__( self : Optional[Any] ):
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self : Union[str, Any] , snake_case__ : Optional[int] ):
'''simple docstring'''
def empty_fn(*snake_case__ : Any , **snake_case__ : Union[str, Any] ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self : Union[str, Any] ):
'''simple docstring'''
return self
def __exit__( self : Dict , snake_case__ : Union[str, Any] , snake_case__ : List[str] , snake_case__ : List[str] ):
'''simple docstring'''
return
class __magic_name__ :
def __call__( self : Union[str, Any] , *snake_case__ : List[str] , **snake_case__ : Any ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm(*snake_case__ , **snake_case__ )
else:
return EmptyTqdm(*snake_case__ , **snake_case__ )
def __snake_case ( self : Optional[Any] , *snake_case__ : Optional[Any] , **snake_case__ : List[Any] ):
'''simple docstring'''
lowercase :Optional[int] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*snake_case__ , **snake_case__ )
def __snake_case ( self : Optional[int] ):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
UpperCAmelCase = _tqdm_cls()
def lowerCamelCase () -> bool:
global _tqdm_active
return bool(_tqdm_active)
def lowerCamelCase () -> Union[str, Any]:
global _tqdm_active
lowercase :str = True
hf_hub_utils.enable_progress_bars()
def lowerCamelCase () -> str:
global _tqdm_active
lowercase :Union[str, Any] = False
hf_hub_utils.disable_progress_bars()
| 350
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __magic_name__ ( __UpperCAmelCase ):
__A : UNetaDModel
__A : ScoreSdeVeScheduler
def __init__( self : List[Any] , snake_case__ : UNetaDModel , snake_case__ : ScoreSdeVeScheduler ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=snake_case__ , scheduler=snake_case__ )
@torch.no_grad()
def __call__( self : Optional[Any] , snake_case__ : int = 1 , snake_case__ : int = 2_0_0_0 , snake_case__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , snake_case__ : Optional[str] = "pil" , snake_case__ : bool = True , **snake_case__ : Optional[Any] , ):
'''simple docstring'''
lowercase :List[str] = self.unet.config.sample_size
lowercase :Optional[int] = (batch_size, 3, img_size, img_size)
lowercase :Optional[Any] = self.unet
lowercase :Dict = randn_tensor(snake_case__ , generator=snake_case__ ) * self.scheduler.init_noise_sigma
lowercase :Any = sample.to(self.device )
self.scheduler.set_timesteps(snake_case__ )
self.scheduler.set_sigmas(snake_case__ )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
lowercase :Union[str, Any] = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
lowercase :Union[str, Any] = self.unet(snake_case__ , snake_case__ ).sample
lowercase :Union[str, Any] = self.scheduler.step_correct(snake_case__ , snake_case__ , generator=snake_case__ ).prev_sample
# prediction step
lowercase :List[str] = model(snake_case__ , snake_case__ ).sample
lowercase :Optional[int] = self.scheduler.step_pred(snake_case__ , snake_case__ , snake_case__ , generator=snake_case__ )
lowercase , lowercase :Optional[int] = output.prev_sample, output.prev_sample_mean
lowercase :List[Any] = sample_mean.clamp(0 , 1 )
lowercase :Optional[Any] = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase :Dict = self.numpy_to_pil(snake_case__ )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=snake_case__ )
| 172
| 0
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCAmelCase ( A , unittest.TestCase ):
lowerCAmelCase_ = KandinskyVaaImgaImgPipeline
lowerCAmelCase_ = ["image_embeds", "negative_image_embeds", "image"]
lowerCAmelCase_ = [
"image_embeds",
"negative_image_embeds",
"image",
]
lowerCAmelCase_ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowerCAmelCase_ = False
@property
def snake_case ( self : List[str] ):
"""simple docstring"""
return 32
@property
def snake_case ( self : Any ):
"""simple docstring"""
return 32
@property
def snake_case ( self : List[str] ):
"""simple docstring"""
return self.time_input_dim
@property
def snake_case ( self : str ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def snake_case ( self : Union[str, Any] ):
"""simple docstring"""
return 100
@property
def snake_case ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__lowercase ={
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__lowercase =UNetaDConditionModel(**__lowercase )
return model
@property
def snake_case ( self : Any ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def snake_case ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__lowercase =VQModel(**self.dummy_movq_kwargs )
return model
def snake_case ( self : Tuple ):
"""simple docstring"""
__lowercase =self.dummy_unet
__lowercase =self.dummy_movq
__lowercase ={
'num_train_timesteps': 1000,
'beta_schedule': 'linear',
'beta_start': 0.0_0_0_8_5,
'beta_end': 0.0_1_2,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
__lowercase =DDIMScheduler(**__lowercase )
__lowercase ={
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def snake_case ( self : Optional[int] , __lowercase : Optional[int] , __lowercase : int=0 ):
"""simple docstring"""
__lowercase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowercase ) ).to(__lowercase )
__lowercase =floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowercase )
# create init_image
__lowercase =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
__lowercase =image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase =Image.fromarray(np.uinta(__lowercase ) ).convert('RGB' ).resize((256, 256) )
if str(__lowercase ).startswith('mps' ):
__lowercase =torch.manual_seed(__lowercase )
else:
__lowercase =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
__lowercase ={
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def snake_case ( self : List[str] ):
"""simple docstring"""
__lowercase ='cpu'
__lowercase =self.get_dummy_components()
__lowercase =self.pipeline_class(**__lowercase )
__lowercase =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
__lowercase =pipe(**self.get_dummy_inputs(__lowercase ) )
__lowercase =output.images
__lowercase =pipe(
**self.get_dummy_inputs(__lowercase ) , return_dict=__lowercase , )[0]
__lowercase =image[0, -3:, -3:, -1]
__lowercase =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowercase =np.array(
[0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self : Optional[int] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self : Any ):
"""simple docstring"""
__lowercase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_img2img_frog.npy' )
__lowercase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__lowercase ='A red cartoon frog, 4k'
__lowercase =KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(__lowercase )
__lowercase =KandinskyVaaImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder' , torch_dtype=torch.floataa )
__lowercase =pipeline.to(__lowercase )
pipeline.set_progress_bar_config(disable=__lowercase )
__lowercase =torch.Generator(device='cpu' ).manual_seed(0 )
__lowercase , __lowercase =pipe_prior(
__lowercase , generator=__lowercase , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__lowercase =pipeline(
image=__lowercase , image_embeds=__lowercase , negative_image_embeds=__lowercase , generator=__lowercase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='np' , )
__lowercase =output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 141
|
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __UpperCamelCase ( lowercase__ : Optional[int], lowercase__ : str, lowercase__ : int ):
'''simple docstring'''
__lowercase =OmegaConf.load(lowercase__ )
__lowercase =torch.load(lowercase__, map_location='cpu' )['model']
__lowercase =list(state_dict.keys() )
# extract state_dict for VQVAE
__lowercase ={}
__lowercase ='first_stage_model.'
for key in keys:
if key.startswith(lowercase__ ):
__lowercase =state_dict[key]
# extract state_dict for UNetLDM
__lowercase ={}
__lowercase ='model.diffusion_model.'
for key in keys:
if key.startswith(lowercase__ ):
__lowercase =state_dict[key]
__lowercase =config.model.params.first_stage_config.params
__lowercase =config.model.params.unet_config.params
__lowercase =VQModel(**lowercase__ ).eval()
vqvae.load_state_dict(lowercase__ )
__lowercase =UNetLDMModel(**lowercase__ ).eval()
unet.load_state_dict(lowercase__ )
__lowercase =DDIMScheduler(
timesteps=config.model.params.timesteps, beta_schedule='scaled_linear', beta_start=config.model.params.linear_start, beta_end=config.model.params.linear_end, clip_sample=lowercase__, )
__lowercase =LDMPipeline(lowercase__, lowercase__, lowercase__ )
pipeline.save_pretrained(lowercase__ )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
UpperCAmelCase = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 141
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
lowerCAmelCase__ :Optional[Any] = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
lowerCAmelCase__ :Optional[Any] = TaTokenizerFast
lowerCAmelCase__ :Any = {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ :List[str] = [
'''MT5EncoderModel''',
'''MT5ForConditionalGeneration''',
'''MT5ForQuestionAnswering''',
'''MT5Model''',
'''MT5PreTrainedModel''',
'''MT5Stack''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ :Any = ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ :Optional[int] = ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
lowerCAmelCase__ :Dict = _LazyModule(
__name__,
globals()['''__file__'''],
_import_structure,
extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
module_spec=__spec__,
)
| 364
|
from __future__ import annotations
from PIL import Image
# Define glider example
lowerCAmelCase__ :str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
lowerCAmelCase__ :Any = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def lowerCAmelCase__ ( a__: list[list[int]] ) -> list[list[int]]:
'''simple docstring'''
_UpperCAmelCase = []
for i in range(len(a__ ) ):
_UpperCAmelCase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
_UpperCAmelCase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(a__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(a__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(a__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
_UpperCAmelCase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(a__ )
return next_generation
def lowerCAmelCase__ ( a__: list[list[int]] , a__: int ) -> list[Image.Image]:
'''simple docstring'''
_UpperCAmelCase = []
for _ in range(a__ ):
# Create output image
_UpperCAmelCase = Image.new('RGB' , (len(cells[0] ), len(a__ )) )
_UpperCAmelCase = img.load()
# Save cells to image
for x in range(len(a__ ) ):
for y in range(len(cells[0] ) ):
_UpperCAmelCase = 2_5_5 - cells[y][x] * 2_5_5
_UpperCAmelCase = (colour, colour, colour)
# Save image
images.append(a__ )
_UpperCAmelCase = new_generation(a__ )
return images
if __name__ == "__main__":
lowerCAmelCase__ :Tuple = generate_images(GLIDER, 1_6)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 185
| 0
|
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class SCREAMING_SNAKE_CASE( datasets.BeamBasedBuilder ):
"""simple docstring"""
def A ( self : Dict ) -> Tuple:
return datasets.DatasetInfo(
features=datasets.Features({'''content''': datasets.Value('''string''' )} ) , supervised_keys=__snake_case , )
def A ( self : Union[str, Any] , __snake_case : List[str] , __snake_case : str ) -> Optional[Any]:
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_dummy_examples()} )]
def A ( self : Optional[Any] , __snake_case : Optional[Any] , __snake_case : Optional[Any] ) -> Optional[Any]:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__snake_case )
class SCREAMING_SNAKE_CASE( datasets.BeamBasedBuilder ):
"""simple docstring"""
def A ( self : Tuple ) -> Union[str, Any]:
return datasets.DatasetInfo(
features=datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) , supervised_keys=__snake_case , )
def A ( self : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] ) -> Tuple:
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''examples''': get_test_nested_examples()} )
]
def A ( self : Tuple , __snake_case : Tuple , __snake_case : str ) -> str:
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(__snake_case )
def snake_case_ ( ) -> Optional[Any]:
return [(i, {"content": content}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
def snake_case_ ( ) -> Union[str, Any]:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['''foo''', '''bar''', '''foobar'''] )]
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
@require_beam
def A ( self : Tuple ) -> Dict:
UpperCAmelCase : Dict = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : str = DummyBeamDataset(cache_dir=__snake_case , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__snake_case , builder.name , '''default''' , '''0.0.0''' , F"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
UpperCAmelCase : str = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __snake_case )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __snake_case )
self.assertDictEqual(dset['''train'''][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__snake_case , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def A ( self : List[Any] ) -> List[Any]:
import apache_beam as beam
UpperCAmelCase : Tuple = beam.io.parquetio.WriteToParquet
UpperCAmelCase : List[str] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : Any = DummyBeamDataset(cache_dir=__snake_case , beam_runner='''DirectRunner''' )
with patch('''apache_beam.io.parquetio.WriteToParquet''' ) as write_parquet_mock:
UpperCAmelCase : str = partial(__snake_case , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
__snake_case , builder.name , '''default''' , '''0.0.0''' , F"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
__snake_case , builder.name , '''default''' , '''0.0.0''' , F"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'''content''': datasets.Value('''string''' )} ) )
UpperCAmelCase : Any = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __snake_case )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __snake_case )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['''train''']['''content'''] ) , sorted(['''foo''', '''bar''', '''foobar'''] ) )
self.assertTrue(
os.path.exists(os.path.join(__snake_case , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
@require_beam
def A ( self : Optional[Any] ) -> List[str]:
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : Any = DummyBeamDataset(cache_dir=__snake_case )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def A ( self : Optional[Any] ) -> Optional[Any]:
UpperCAmelCase : List[str] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase : List[str] = NestedBeamDataset(cache_dir=__snake_case , beam_runner='''DirectRunner''' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(__snake_case , builder.name , '''default''' , '''0.0.0''' , F"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'''a''': datasets.Sequence({'''b''': datasets.Value('''string''' )} )} ) )
UpperCAmelCase : List[Any] = builder.as_dataset()
self.assertEqual(dset['''train'''].num_rows , __snake_case )
self.assertEqual(dset['''train'''].info.splits['''train'''].num_examples , __snake_case )
self.assertDictEqual(dset['''train'''][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['''train'''][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(__snake_case , builder.name , '''default''' , '''0.0.0''' , '''dataset_info.json''' ) ) )
del dset
| 23
|
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class __lowerCAmelCase :
UpperCamelCase__ = LEDConfig
UpperCamelCase__ = {}
UpperCamelCase__ = '''gelu'''
def __init__( self :Optional[int] , __magic_name__ :Dict , __magic_name__ :List[str]=13 , __magic_name__ :Union[str, Any]=7 , __magic_name__ :str=True , __magic_name__ :Union[str, Any]=False , __magic_name__ :Union[str, Any]=99 , __magic_name__ :List[Any]=32 , __magic_name__ :str=2 , __magic_name__ :List[str]=4 , __magic_name__ :str=37 , __magic_name__ :Any=0.1 , __magic_name__ :Dict=0.1 , __magic_name__ :List[str]=20 , __magic_name__ :Union[str, Any]=2 , __magic_name__ :List[Any]=1 , __magic_name__ :Optional[int]=0 , __magic_name__ :Optional[int]=4 , ):
'''simple docstring'''
a = parent
a = batch_size
a = seq_length
a = is_training
a = use_labels
a = vocab_size
a = hidden_size
a = num_hidden_layers
a = num_attention_heads
a = intermediate_size
a = hidden_dropout_prob
a = attention_probs_dropout_prob
a = max_position_embeddings
a = eos_token_id
a = pad_token_id
a = bos_token_id
a = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
a = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
a = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
a = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
a = tf.concat([input_ids, eos_tensor] , axis=1 )
a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
a = prepare_led_inputs_dict(__magic_name__ , __magic_name__ , __magic_name__ )
a = tf.concat(
[tf.zeros_like(__magic_name__ )[:, :-1], tf.ones_like(__magic_name__ )[:, -1:]] , axis=-1 , )
a = global_attention_mask
return config, inputs_dict
def lowerCamelCase__ ( self :Union[str, Any] , __magic_name__ :Optional[int] , __magic_name__ :List[Any] ):
'''simple docstring'''
a = TFLEDModel(config=__magic_name__ ).get_decoder()
a = inputs_dict["""input_ids"""]
a = input_ids[:1, :]
a = inputs_dict["""attention_mask"""][:1, :]
a = 1
# first forward pass
a = model(__magic_name__ , attention_mask=__magic_name__ , use_cache=__magic_name__ )
a , a = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
a = ids_tensor((self.batch_size, 3) , config.vocab_size )
a = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
a = tf.concat([input_ids, next_tokens] , axis=-1 )
a = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
a = model(__magic_name__ , attention_mask=__magic_name__ )[0]
a = model(__magic_name__ , attention_mask=__magic_name__ , past_key_values=__magic_name__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
a = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
a = output_from_no_past[:, -3:, random_slice_idx]
a = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__magic_name__ , __magic_name__ , rtol=1E-3 )
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , __lowerCamelCase=None , ) -> List[str]:
if attention_mask is None:
a = tf.cast(tf.math.not_equal(__lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
a = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
a = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
a = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class __lowerCAmelCase ( __magic_name__ , __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
UpperCamelCase__ = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
UpperCamelCase__ = (
{
'''conversational''': TFLEDForConditionalGeneration,
'''feature-extraction''': TFLEDModel,
'''summarization''': TFLEDForConditionalGeneration,
'''text2text-generation''': TFLEDForConditionalGeneration,
'''translation''': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
UpperCamelCase__ = True
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = TFLEDModelTester(self )
a = ConfigTester(self , config_class=__magic_name__ )
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__ )
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
a , a = self.model_tester.prepare_config_and_inputs_for_common()
a = tf.zeros_like(inputs_dict["""attention_mask"""] )
a = 2
a = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["""global_attention_mask"""] , )
a = True
a = self.model_tester.seq_length
a = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(__magic_name__ :int ):
a = outputs.decoder_attentions
self.assertEqual(len(__magic_name__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(__magic_name__ :Any ):
a = [t.numpy() for t in outputs.encoder_attentions]
a = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(__magic_name__ ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(__magic_name__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
a = True
a = False
a = False
a = model_class(__magic_name__ )
a = model(self._prepare_for_class(__magic_name__ , __magic_name__ ) )
a = len(__magic_name__ )
self.assertEqual(config.output_hidden_states , __magic_name__ )
check_encoder_attentions_output(__magic_name__ )
if self.is_encoder_decoder:
a = model_class(__magic_name__ )
a = model(self._prepare_for_class(__magic_name__ , __magic_name__ ) )
self.assertEqual(config.output_hidden_states , __magic_name__ )
check_decoder_attentions_output(__magic_name__ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
a = True
a = model_class(__magic_name__ )
a = model(self._prepare_for_class(__magic_name__ , __magic_name__ ) )
self.assertEqual(config.output_hidden_states , __magic_name__ )
check_encoder_attentions_output(__magic_name__ )
# Check attention is always last and order is fine
a = True
a = True
a = model_class(__magic_name__ )
a = model(self._prepare_for_class(__magic_name__ , __magic_name__ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__magic_name__ ) )
self.assertEqual(model.config.output_hidden_states , __magic_name__ )
check_encoder_attentions_output(__magic_name__ )
@unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing.""" )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
pass
def __A ( __lowerCamelCase ) -> int:
return tf.constant(__lowerCamelCase , dtype=tf.intaa )
__UpperCamelCase : int = 1E-4
@slow
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" ).led
# change to intended input here
a = _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
a = _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
a = prepare_led_inputs_dict(model.config , __magic_name__ , __magic_name__ )
a = model(**__magic_name__ )[0]
a = (1, 1024, 768)
self.assertEqual(output.shape , __magic_name__ )
# change to expected output here
a = tf.convert_to_tensor(
[[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]] , )
tf.debugging.assert_near(output[:, :3, :3] , __magic_name__ , atol=1E-3 )
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
a = TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" )
# change to intended input here
a = _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
a = _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
a = prepare_led_inputs_dict(model.config , __magic_name__ , __magic_name__ )
a = model(**__magic_name__ )[0]
a = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape , __magic_name__ )
# change to expected output here
a = tf.convert_to_tensor(
[[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]] , )
tf.debugging.assert_near(output[:, :3, :3] , __magic_name__ , atol=1E-3 , rtol=1E-3 )
| 228
| 0
|
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class __UpperCAmelCase ( lowercase__ ):
def __init__( self : Tuple, __A : str="", __A : str="train" ):
assert os.path.isdir(lowercase_ )
UpperCAmelCase : str = []
UpperCAmelCase : int = os.listdir(lowercase_ )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
UpperCAmelCase : Union[str, Any] = os.path.join(lowercase_, lowercase_ )
if not os.path.isfile(lowercase_ ):
continue
self.documents.append(lowercase_ )
def __len__( self : Dict ):
return len(self.documents )
def __getitem__( self : Tuple, __A : Union[str, Any] ):
UpperCAmelCase : Optional[Any] = self.documents[idx]
UpperCAmelCase : Dict = document_path.split('''/''' )[-1]
with open(lowercase_, encoding='''utf-8''' ) as source:
UpperCAmelCase : Union[str, Any] = source.read()
UpperCAmelCase : Any = process_story(lowercase_ )
return document_name, story_lines, summary_lines
def a__ ( UpperCAmelCase : Optional[Any] ) -> Optional[int]:
UpperCAmelCase : Dict = list(filter(lambda UpperCAmelCase : len(__lowerCamelCase ) != 0 , [line.strip() for line in raw_story.split('''\n''' )] ) )
# for some unknown reason some lines miss a period, add it
UpperCAmelCase : List[Any] = [_add_missing_period(__lowerCamelCase ) for line in nonempty_lines]
# gather article lines
UpperCAmelCase : Dict = []
UpperCAmelCase : Optional[int] = deque(__lowerCamelCase )
while True:
try:
UpperCAmelCase : List[str] = lines.popleft()
if element.startswith('''@highlight''' ):
break
story_lines.append(__lowerCamelCase )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
UpperCAmelCase : int = list(filter(lambda UpperCAmelCase : not t.startswith('''@highlight''' ) , __lowerCamelCase ) )
return story_lines, summary_lines
def a__ ( UpperCAmelCase : Any ) -> Union[str, Any]:
UpperCAmelCase : List[Any] = [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
if line.startswith('''@highlight''' ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] ) -> List[Any]:
if len(__lowerCamelCase ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(__lowerCamelCase )) )
return sequence
def a__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Any ) -> Dict:
UpperCAmelCase : int = torch.ones_like(__lowerCamelCase )
UpperCAmelCase : Optional[Any] = sequence == pad_token_id
UpperCAmelCase : Any = 0
return mask
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : str ) -> Tuple:
UpperCAmelCase : int = [tokenizer.encode(__lowerCamelCase ) for line in story_lines]
UpperCAmelCase : Optional[Any] = [token for sentence in story_lines_token_ids for token in sentence]
UpperCAmelCase : Optional[Any] = [tokenizer.encode(__lowerCamelCase ) for line in summary_lines]
UpperCAmelCase : str = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] ) -> str:
UpperCAmelCase : Tuple = []
for sequence in batch:
UpperCAmelCase : Dict = -1
UpperCAmelCase : Any = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(__lowerCamelCase )
return torch.tensor(__lowerCamelCase )
| 351
|
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
_lowerCamelCase : List[str] = True
from torch.cuda.amp import autocast
_lowerCamelCase : Any = logging.getLogger(__name__)
@dataclass
class __UpperCAmelCase :
UpperCamelCase = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Whether to log verbose messages or not."""} , )
UpperCamelCase = field(
default=2.0 , metadata={"""help""": """Maximum temperature for gumbel softmax."""} )
UpperCamelCase = field(
default=0.5 , metadata={"""help""": """Minimum temperature for gumbel softmax."""} )
UpperCamelCase = field(
default=0.9_9_9_9_9_5 , metadata={"""help""": """Decay of gumbel temperature during training."""} )
def a__ ( UpperCAmelCase : ModelArguments , UpperCAmelCase : TrainingArguments ) -> Any:
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
UpperCAmelCase : Any = logging.WARNING
if model_args.verbose_logging:
UpperCAmelCase : Any = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
UpperCAmelCase : Any = logging.INFO
logger.setLevel(UpperCAmelCase )
@dataclass
class __UpperCAmelCase :
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
UpperCamelCase = field(
default="""train""" , metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
} , )
UpperCamelCase = field(
default="""validation""" , metadata={
"""help""": (
"""The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
)
} , )
UpperCamelCase = field(
default="""file""" , metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""} , )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
UpperCamelCase = field(
default=1 , metadata={
"""help""": """The percentage of the train set used as validation set in case there's no validation split"""
} , )
UpperCamelCase = field(
default=lowerCamelCase__ , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
UpperCamelCase = field(
default=2_0.0 , metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""} )
@dataclass
class __UpperCAmelCase :
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = "longest"
UpperCamelCase = None
UpperCamelCase = None
def __call__( self : int, __A : List[Dict[str, Union[List[int], torch.Tensor]]] ):
# reformat list to dict and set to pytorch format
UpperCAmelCase : List[Any] = self.feature_extractor.pad(
__A, max_length=self.max_length, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors='''pt''', )
UpperCAmelCase : int = self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1] )
UpperCAmelCase : Tuple = batch['''input_values'''].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
UpperCAmelCase : Tuple = self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1 ) ).to(
torch.long )
UpperCAmelCase : Dict = torch.zeros(
(batch_size, mask_indices_seq_length), dtype=torch.long, device=batch['''input_values'''].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
UpperCAmelCase : Tuple = 1
UpperCAmelCase : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
UpperCAmelCase : Dict = _compute_mask_indices(
(batch_size, mask_indices_seq_length), self.model.config.mask_time_prob, self.model.config.mask_time_length, attention_mask=__A, min_masks=2, )
return batch
class __UpperCAmelCase ( lowerCamelCase__ ):
def __init__( self : Union[str, Any], *__A : int, __A : Dict=1, __A : Any=0, __A : Optional[Any]=1.0, **__A : Any ):
super().__init__(*__A, **__A )
UpperCAmelCase : Any = 0
UpperCAmelCase : Any = max_gumbel_temp
UpperCAmelCase : Optional[Any] = min_gumbel_temp
UpperCAmelCase : str = gumbel_temp_decay
def __magic_name__ ( self : Dict, __A : nn.Module, __A : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
UpperCAmelCase : List[Any] = self._prepare_inputs(__A )
if self.use_amp:
with autocast():
UpperCAmelCase : Optional[Any] = self.compute_loss(__A, __A )
else:
UpperCAmelCase : Optional[int] = self.compute_loss(__A, __A )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
UpperCAmelCase : Optional[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
UpperCAmelCase : str = loss.sum() / (inputs['''mask_time_indices''']).sum()
else:
raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']''' )
if self.args.gradient_accumulation_steps > 1:
UpperCAmelCase : Any = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(__A ).backward()
elif self.use_apex:
with amp.scale_loss(__A, self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(__A )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp ) )
return loss.detach()
def a__ ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCAmelCase : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = parser.parse_args_into_dataclasses()
configure_logger(UpperCAmelCase , UpperCAmelCase )
# Downloading and loading a dataset from the hub.
UpperCAmelCase : int = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
UpperCAmelCase : Union[str, Any] = DatasetDict()
UpperCAmelCase : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]''' , cache_dir=model_args.cache_dir , )
UpperCAmelCase : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]''' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
UpperCAmelCase : Optional[Any] = DatasetDict()
UpperCAmelCase : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split='''validation''' , cache_dir=model_args.cache_dir , )
UpperCAmelCase : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f'''{data_args.train_split_name}''' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
UpperCAmelCase : Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=UpperCAmelCase )
def prepare_dataset(UpperCAmelCase : Dict ):
# check that all files have the correct sampling rate
UpperCAmelCase , UpperCAmelCase : Optional[Any] = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
UpperCAmelCase : str = datasets.map(
UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets['''train'''].column_names )
# filter audio files that are too long
UpperCAmelCase : int = vectorized_datasets.filter(
lambda UpperCAmelCase : len(data['''speech'''] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(UpperCAmelCase : Dict ):
return feature_extractor(batch['''speech'''] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
UpperCAmelCase : Any = vectorized_datasets.map(
UpperCAmelCase , batched=UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets['''train'''].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
UpperCAmelCase : Optional[int] = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
'''PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'''
''' ``config.feat_extract_norm=\'layer\'''' )
UpperCAmelCase : Any = WavaVecaForPreTraining(UpperCAmelCase )
UpperCAmelCase : int = DataCollatorForWavaVecaPretraining(model=UpperCAmelCase , feature_extractor=UpperCAmelCase )
UpperCAmelCase : Any = WavaVecaPreTrainer(
model=UpperCAmelCase , data_collator=UpperCAmelCase , args=UpperCAmelCase , train_dataset=vectorized_datasets['''train'''] , eval_dataset=vectorized_datasets['''validation'''] , tokenizer=UpperCAmelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 99
| 0
|
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''spiece.model'''}
__snake_case = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__snake_case = {
'''AI-Sweden/gpt-sw3-126m''': 20_48,
'''AI-Sweden/gpt-sw3-350m''': 20_48,
'''AI-Sweden/gpt-sw3-1.6b''': 20_48,
'''AI-Sweden/gpt-sw3-6.7b''': 20_48,
'''AI-Sweden/gpt-sw3-20b''': 20_48,
}
class __snake_case ( _a ):
__lowerCamelCase : List[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Tuple = ['''input_ids''', '''attention_mask''']
def __init__( self , snake_case__ , snake_case__=False , snake_case__=False , snake_case__=False , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__ = None , **snake_case__ , ) -> None:
'''simple docstring'''
UpperCAmelCase : List[str] ={} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase : Union[str, Any] =kwargs.get('''name_or_path''' )
if name_or_path is None:
logger.warning(
'''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
''' you are testing the model, this can safely be ignored''' )
UpperCAmelCase : Dict ='''None'''
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase : str ='''<|endoftext|>''' if eos_token is None else eos_token
UpperCAmelCase : str ='''<unk>''' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase : Dict =unk_token if pad_token is None else pad_token
UpperCAmelCase : List[str] =eos_token if bos_token is None else bos_token
else:
UpperCAmelCase : Tuple ='''<pad>''' if pad_token is None else pad_token
UpperCAmelCase : Optional[int] ='''<s>''' if bos_token is None else bos_token
super().__init__(
do_lower_case=snake_case__ , remove_space=snake_case__ , keep_accents=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , pad_token=snake_case__ , sp_model_kwargs=self.sp_model_kwargs , **snake_case__ , )
UpperCAmelCase : Dict =do_lower_case
UpperCAmelCase : Optional[Any] =remove_space
UpperCAmelCase : Optional[Any] =keep_accents
UpperCAmelCase : Tuple =vocab_file
UpperCAmelCase : Dict =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case__ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase : List[Any] ={''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''''', ''''''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase : str =re.compile(
f'''[{''.join(map(snake_case__ , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8203] ) )}]''' )
def __getstate__( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[str] =self.__dict__.copy()
UpperCAmelCase : Tuple =None
return state
def __setstate__( self , snake_case__ ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
UpperCAmelCase : Tuple ={}
UpperCAmelCase : Any =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
return len(self.sp_model )
def UpperCAmelCase__ ( self , snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : Dict =self.non_printing_characters_re.sub('''''' , snake_case__ )
# Normalize whitespaces
UpperCAmelCase : Optional[int] =''''''.join([char if char not in self.whitespaces else ''' ''' for char in text] )
# NFC Unicode normalization
UpperCAmelCase : Union[str, Any] =unicodedata.normalize('''NFC''' , snake_case__ )
return text
def UpperCAmelCase__ ( self , snake_case__ , **snake_case__ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Dict =self.preprocess_text(snake_case__ )
return self.sp_model.encode(snake_case__ , out_type=snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> int:
'''simple docstring'''
return self.sp_model.PieceToId(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> str:
'''simple docstring'''
return self.sp_model.IdToPiece(snake_case__ )
@staticmethod
def UpperCAmelCase__ ( snake_case__ ) -> str:
'''simple docstring'''
return out_string
def UpperCAmelCase__ ( self , snake_case__ ) -> str:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =[]
UpperCAmelCase : Dict =''''''
UpperCAmelCase : int =False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(snake_case__ ) + token
UpperCAmelCase : Dict =True
UpperCAmelCase : List[Any] =[]
else:
current_sub_tokens.append(snake_case__ )
UpperCAmelCase : Dict =False
out_string += self.sp_model.decode(snake_case__ )
return out_string
def UpperCAmelCase__ ( self ) -> Dict[str, int]:
'''simple docstring'''
UpperCAmelCase : str ={self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(snake_case__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase : List[str] =os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case__ )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case__ , '''wb''' ) as fi:
UpperCAmelCase : Union[str, Any] =self.sp_model.serialized_model_proto()
fi.write(snake_case__ )
return (out_vocab_file,)
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
'''simple docstring'''
if isinstance(snake_case__ , snake_case__ ):
UpperCAmelCase : List[str] =self.preprocess_text(snake_case__ )
UpperCAmelCase : List[str] =self.sp_model.encode(snake_case__ )
else:
UpperCAmelCase : Dict =[self.preprocess_text(snake_case__ ) for t in text]
UpperCAmelCase : Union[str, Any] =self.sp_model.encode(snake_case__ )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase : List[Any] =torch.tensor(snake_case__ )
return token_ids
def UpperCAmelCase__ ( self , snake_case__ ) -> str:
'''simple docstring'''
return self.sp_model.decode(snake_case__ )
def UpperCAmelCase__ ( self , snake_case__ ) -> List[int]:
'''simple docstring'''
UpperCAmelCase : Dict =[f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
UpperCAmelCase : Any =(
f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(snake_case__ ) + f'''{self.bos_token}Bot:'''
)
return self.encode(text=snake_case__ )
| 348
|
lowerCAmelCase_ = {
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
"electronvolt": 1.602_176_634e-19,
"britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
"footpound": 1.355_818,
}
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
snake_case_ : str = (
f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
f'''Valid values are: {", ".join(_UpperCamelCase )}'''
)
raise ValueError(_UpperCamelCase )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 279
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase :str = logging.get_logger(__name__)
_lowerCAmelCase :Optional[int] = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''big_bird'''
def __init__( self , A=5_0_3_5_8 , A=7_6_8 , A=1_2 , A=1_2 , A=3_0_7_2 , A="gelu_new" , A=0.1 , A=0.1 , A=4_0_9_6 , A=2 , A=0.02 , A=1E-12 , A=True , A=0 , A=1 , A=2 , A=6_6 , A="block_sparse" , A=True , A=False , A=6_4 , A=3 , A=None , **A , ) -> int:
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , sep_token_id=A , **A , )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : Optional[Any] = max_position_embeddings
_UpperCAmelCase : Union[str, Any] = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : Any = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : str = hidden_dropout_prob
_UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : List[Any] = type_vocab_size
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Tuple = use_cache
_UpperCAmelCase : Optional[int] = rescale_embeddings
_UpperCAmelCase : List[str] = attention_type
_UpperCAmelCase : str = use_bias
_UpperCAmelCase : Optional[int] = block_size
_UpperCAmelCase : Tuple = num_random_blocks
_UpperCAmelCase : str = classifier_dropout
class _UpperCAmelCase ( a ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Union[str, Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_UpperCAmelCase : Any = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 68
|
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( a ):
'''simple docstring'''
a__ ='''WhisperFeatureExtractor'''
a__ ='''WhisperTokenizer'''
def __init__( self , A , A ) -> Any:
super().__init__(A , A )
_UpperCAmelCase : int = self.feature_extractor
_UpperCAmelCase : List[str] = False
def __lowerCAmelCase ( self , A=None , A=None , A=True ) -> Optional[int]:
return self.tokenizer.get_decoder_prompt_ids(task=A , language=A , no_timestamps=A )
def __call__( self , *A , **A ) -> Tuple:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*A , **A )
_UpperCAmelCase : str = kwargs.pop('''audio''' , A )
_UpperCAmelCase : Dict = kwargs.pop('''sampling_rate''' , A )
_UpperCAmelCase : Dict = kwargs.pop('''text''' , A )
if len(A ) > 0:
_UpperCAmelCase : List[Any] = args[0]
_UpperCAmelCase : Union[str, Any] = args[1:]
if audio is None and text is None:
raise ValueError('''You need to specify either an `audio` or `text` input to process.''' )
if audio is not None:
_UpperCAmelCase : Optional[Any] = self.feature_extractor(A , *A , sampling_rate=A , **A )
if text is not None:
_UpperCAmelCase : Any = self.tokenizer(A , **A )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_UpperCAmelCase : int = encodings['''input_ids''']
return inputs
def __lowerCAmelCase ( self , *A , **A ) -> Optional[Any]:
return self.tokenizer.batch_decode(*A , **A )
def __lowerCAmelCase ( self , *A , **A ) -> Any:
return self.tokenizer.decode(*A , **A )
def __lowerCAmelCase ( self , A , A="np" ) -> Any:
return self.tokenizer.get_prompt_ids(A , return_tensors=A )
| 68
| 1
|
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class lowercase :
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=4 , snake_case="gelu" , snake_case=0.0 , snake_case=0.1 , snake_case=True , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
snake_case_ = use_labels
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = intermediate_multiple_size
snake_case_ = hidden_act
snake_case_ = hidden_dropout
snake_case_ = attention_dropout
snake_case_ = weight_tying
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def a ( self ):
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = self.get_config()
return config, input_ids, input_mask, token_labels
def a ( self ):
return GPTNeoXJapaneseConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_A , initializer_range=self.initializer_range , )
def a ( self ):
snake_case_ = self.prepare_config_and_inputs()
snake_case_ = True
return config, input_ids, input_mask, token_labels
def a ( self , snake_case , snake_case , snake_case ):
snake_case_ = GPTNeoXJapaneseModel(config=_A )
model.to(_A )
model.eval()
snake_case_ = model(_A , attention_mask=_A )
snake_case_ = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a ( self , snake_case , snake_case , snake_case ):
snake_case_ = True
snake_case_ = GPTNeoXJapaneseModel(_A )
model.to(_A )
model.eval()
snake_case_ = model(_A , attention_mask=_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a ( self , snake_case , snake_case , snake_case , snake_case ):
snake_case_ = GPTNeoXJapaneseForCausalLM(config=_A )
model.to(_A )
model.eval()
snake_case_ = model(_A , attention_mask=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a ( self , snake_case , snake_case , snake_case ):
snake_case_ = True
snake_case_ = GPTNeoXJapaneseForCausalLM(config=_A )
model.to(_A )
model.eval()
# first forward pass
snake_case_ = model(_A , attention_mask=_A , use_cache=_A )
snake_case_ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case_ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 )
snake_case_ = torch.cat([input_mask, next_mask] , dim=-1 )
snake_case_ = model(_A , attention_mask=_A , output_hidden_states=_A )
snake_case_ = output_from_no_past['hidden_states'][0]
snake_case_ = model(
_A , attention_mask=_A , past_key_values=_A , output_hidden_states=_A , )['hidden_states'][0]
# select random slice
snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
snake_case_ = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_A , _A , atol=1e-3 ) )
def a ( self ):
snake_case_ = self.prepare_config_and_inputs()
snake_case_ = config_and_inputs
snake_case_ = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase ( lowercase_ , lowercase_ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : Any = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : Any = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : int = (
{"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : List[Any] = False
__SCREAMING_SNAKE_CASE : Union[str, Any] = False
__SCREAMING_SNAKE_CASE : List[str] = False
__SCREAMING_SNAKE_CASE : Optional[int] = False
def a ( self ):
snake_case_ = GPTNeoXJapaneseModelTester(self )
snake_case_ = ConfigTester(self , config_class=_A , hidden_size=37 )
def a ( self ):
self.config_tester.run_common_tests()
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(_A , _A , _A )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(_A , _A , _A )
def a ( self ):
# This regression test was failing with PyTorch < 1.3
snake_case_ = self.model_tester.prepare_config_and_inputs_for_decoder()
snake_case_ = None
self.model_tester.create_and_check_model_as_decoder(_A , _A , _A )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(_A , _A , _A )
def a ( self ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*_A )
@slow
def a ( self ):
snake_case_ = 'abeja/gpt-neox-japanese-2.7b'
snake_case_ = ['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']
snake_case_ = [
'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
'100年後に必要とされる会社は、「人」が中心の会社です。',
'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
'国境の長いトンネルを抜けると、そこは雪国だった。',
'美味しい日本食といえば、やっぱりお寿司ですよね。',
]
snake_case_ = GPTNeoXJapaneseTokenizer.from_pretrained(_A )
snake_case_ = GPTNeoXJapaneseForCausalLM.from_pretrained(_A )
snake_case_ = []
for prompt in prompts:
snake_case_ = tokenizer(_A , return_tensors='pt' ).input_ids
snake_case_ = model.generate(_A , max_length=50 )
snake_case_ = tokenizer.batch_decode(_A , skip_special_tokens=_A )
predicted_outputs += generated_string
self.assertListEqual(_A , _A )
| 285
|
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class UpperCamelCase ( nn.Module ):
UpperCAmelCase : int
UpperCAmelCase : jnp.dtype = jnp.floataa
def _lowercase (self : Any) -> Optional[int]:
__snake_case : str = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__(self : Any , _A : Any) -> str:
__snake_case , __snake_case , __snake_case , __snake_case : Union[str, Any] = hidden_states.shape
__snake_case : Union[str, Any] = jax.image.resize(
_A , shape=(batch, height * 2, width * 2, channels) , method='nearest' , )
__snake_case : List[Any] = self.conv(_A)
return hidden_states
class UpperCamelCase ( nn.Module ):
UpperCAmelCase : int
UpperCAmelCase : jnp.dtype = jnp.floataa
def _lowercase (self : Optional[Any]) -> List[Any]:
__snake_case : Dict = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__(self : int , _A : str) -> Any:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
__snake_case : Union[str, Any] = self.conv(_A)
return hidden_states
class UpperCamelCase ( nn.Module ):
UpperCAmelCase : int
UpperCAmelCase : int = None
UpperCAmelCase : float = 0.0
UpperCAmelCase : bool = None
UpperCAmelCase : jnp.dtype = jnp.floataa
def _lowercase (self : List[str]) -> Dict:
__snake_case : str = self.in_channels if self.out_channels is None else self.out_channels
__snake_case : Optional[int] = nn.GroupNorm(num_groups=32 , epsilon=1E-5)
__snake_case : str = nn.Conv(
_A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
__snake_case : Optional[int] = nn.Dense(_A , dtype=self.dtype)
__snake_case : int = nn.GroupNorm(num_groups=32 , epsilon=1E-5)
__snake_case : str = nn.Dropout(self.dropout_prob)
__snake_case : Dict = nn.Conv(
_A , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
__snake_case : List[str] = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
__snake_case : Optional[Any] = None
if use_nin_shortcut:
__snake_case : List[str] = nn.Conv(
_A , kernel_size=(1, 1) , strides=(1, 1) , padding='VALID' , dtype=self.dtype , )
def __call__(self : List[Any] , _A : Union[str, Any] , _A : str , _A : int=True) -> Any:
__snake_case : List[Any] = hidden_states
__snake_case : Optional[Any] = self.norma(_A)
__snake_case : int = nn.swish(_A)
__snake_case : Optional[int] = self.conva(_A)
__snake_case : Dict = self.time_emb_proj(nn.swish(_A))
__snake_case : List[str] = jnp.expand_dims(jnp.expand_dims(_A , 1) , 1)
__snake_case : Any = hidden_states + temb
__snake_case : Tuple = self.norma(_A)
__snake_case : Dict = nn.swish(_A)
__snake_case : Union[str, Any] = self.dropout(_A , _A)
__snake_case : Union[str, Any] = self.conva(_A)
if self.conv_shortcut is not None:
__snake_case : List[Any] = self.conv_shortcut(_A)
return hidden_states + residual
| 172
| 0
|
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging
a__: Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
def __init__( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
super().__init__()
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''' )
self.register_modules(
speech_model=__lowerCamelCase,speech_processor=__lowerCamelCase,vae=__lowerCamelCase,text_encoder=__lowerCamelCase,tokenizer=__lowerCamelCase,unet=__lowerCamelCase,scheduler=__lowerCamelCase,feature_extractor=__lowerCamelCase,)
def UpperCamelCase ( self,__lowerCamelCase = "auto" ):
if slice_size == "auto":
A__ = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__lowerCamelCase )
def UpperCamelCase ( self ):
self.enable_attention_slicing(__lowerCamelCase )
@torch.no_grad()
def __call__( self,__lowerCamelCase,__lowerCamelCase=1_6000,__lowerCamelCase = 512,__lowerCamelCase = 512,__lowerCamelCase = 50,__lowerCamelCase = 7.5,__lowerCamelCase = None,__lowerCamelCase = 1,__lowerCamelCase = 0.0,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = "pil",__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = 1,**__lowerCamelCase,):
A__ = self.speech_processor.feature_extractor(
__lowerCamelCase,return_tensors='''pt''',sampling_rate=__lowerCamelCase ).input_features.to(self.device )
A__ = self.speech_model.generate(__lowerCamelCase,max_length=48_0000 )
A__ = self.speech_processor.tokenizer.batch_decode(__lowerCamelCase,skip_special_tokens=__lowerCamelCase,normalize=__lowerCamelCase )[
0
]
if isinstance(__lowerCamelCase,__lowerCamelCase ):
A__ = 1
elif isinstance(__lowerCamelCase,__lowerCamelCase ):
A__ = len(__lowerCamelCase )
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase )}" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__lowerCamelCase,__lowerCamelCase ) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(__lowerCamelCase )}." )
# get prompt text embeddings
A__ = self.tokenizer(
__lowerCamelCase,padding='''max_length''',max_length=self.tokenizer.model_max_length,return_tensors='''pt''',)
A__ = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
A__ = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
f" {self.tokenizer.model_max_length} tokens: {removed_text}" )
A__ = text_input_ids[:, : self.tokenizer.model_max_length]
A__ = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
A__ , A__ , A__ = text_embeddings.shape
A__ = text_embeddings.repeat(1,__lowerCamelCase,1 )
A__ = text_embeddings.view(bs_embed * num_images_per_prompt,__lowerCamelCase,-1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
A__ = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
A__ = 42
if negative_prompt is None:
A__ = [''''''] * batch_size
elif type(__lowerCamelCase ) is not type(__lowerCamelCase ):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCamelCase )} !="
f" {type(__lowerCamelCase )}." )
elif isinstance(__lowerCamelCase,__lowerCamelCase ):
A__ = [negative_prompt]
elif batch_size != len(__lowerCamelCase ):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(__lowerCamelCase )}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
''' the batch size of `prompt`.''' )
else:
A__ = negative_prompt
A__ = text_input_ids.shape[-1]
A__ = self.tokenizer(
__lowerCamelCase,padding='''max_length''',max_length=__lowerCamelCase,truncation=__lowerCamelCase,return_tensors='''pt''',)
A__ = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
A__ = uncond_embeddings.shape[1]
A__ = uncond_embeddings.repeat(1,__lowerCamelCase,1 )
A__ = uncond_embeddings.view(batch_size * num_images_per_prompt,__lowerCamelCase,-1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
A__ = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
A__ = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
A__ = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
A__ = torch.randn(__lowerCamelCase,generator=__lowerCamelCase,device='''cpu''',dtype=__lowerCamelCase ).to(
self.device )
else:
A__ = torch.randn(__lowerCamelCase,generator=__lowerCamelCase,device=self.device,dtype=__lowerCamelCase )
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
A__ = latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__lowerCamelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
A__ = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
A__ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
A__ = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
A__ = {}
if accepts_eta:
A__ = eta
for i, t in enumerate(self.progress_bar(__lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
A__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
A__ = self.scheduler.scale_model_input(__lowerCamelCase,__lowerCamelCase )
# predict the noise residual
A__ = self.unet(__lowerCamelCase,__lowerCamelCase,encoder_hidden_states=__lowerCamelCase ).sample
# perform guidance
if do_classifier_free_guidance:
A__ , A__ = noise_pred.chunk(2 )
A__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
A__ = self.scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
A__ = 1 / 0.18215 * latents
A__ = self.vae.decode(__lowerCamelCase ).sample
A__ = (image / 2 + 0.5).clamp(0,1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
A__ = image.cpu().permute(0,2,3,1 ).float().numpy()
if output_type == "pil":
A__ = self.numpy_to_pil(__lowerCamelCase )
if not return_dict:
return image
return StableDiffusionPipelineOutput(images=__lowerCamelCase,nsfw_content_detected=__lowerCamelCase )
| 39
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 39
| 1
|
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( __a ) -> Union[str, Any]:
"""simple docstring"""
def is_in_circle(__a , __a ) -> bool:
lowerCamelCase__: Tuple =sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
lowerCamelCase__: List[Any] =mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(__a ) )
# The ratio of the area for circle to square is pi/4.
lowerCamelCase__: Dict =proportion * 4
print(F"""The estimated value of pi is {pi_estimate}""" )
print(F"""The numpy value of pi is {pi}""" )
print(F"""The total error is {abs(pi - pi_estimate )}""" )
def lowerCAmelCase_ ( __a , __a , __a = 0.0 , __a = 1.0 , ) -> float:
"""simple docstring"""
return mean(
function_to_integrate(uniform(__a , __a ) ) for _ in range(__a ) ) * (max_value - min_value)
def lowerCAmelCase_ ( __a , __a = 0.0 , __a = 1.0 ) -> None:
"""simple docstring"""
def identity_function(__a ) -> float:
return x
lowerCamelCase__: Dict =area_under_curve_estimator(
__a , __a , __a , __a )
lowerCamelCase__: Union[str, Any] =(max_value * max_value - min_value * min_value) / 2
print("******************" )
print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(F"""Estimated value is {estimated_value}""" )
print(F"""Expected value is {expected_value}""" )
print(F"""Total error is {abs(estimated_value - expected_value )}""" )
print("******************" )
def lowerCAmelCase_ ( __a ) -> None:
"""simple docstring"""
def function_to_integrate(__a ) -> float:
return sqrt(4.0 - x * x )
lowerCamelCase__: List[Any] =area_under_curve_estimator(
__a , __a , 0.0 , 2.0 )
print("******************" )
print("Estimating pi using area_under_curve_estimator" )
print(F"""Estimated value is {estimated_value}""" )
print(F"""Expected value is {pi}""" )
print(F"""Total error is {abs(estimated_value - pi )}""" )
print("******************" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A__ : List[str] = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : str = ["""BeitFeatureExtractor"""]
A__ : List[str] = ["""BeitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
"""BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BeitForImageClassification""",
"""BeitForMaskedImageModeling""",
"""BeitForSemanticSegmentation""",
"""BeitModel""",
"""BeitPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = [
"""FlaxBeitForImageClassification""",
"""FlaxBeitForMaskedImageModeling""",
"""FlaxBeitModel""",
"""FlaxBeitPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
A__ : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 185
| 0
|
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
__snake_case :Tuple = logging.getLogger(__name__)
require_version('''pytorch_lightning>=1.0.4''')
__snake_case :Optional[int] = {
'''base''': AutoModel,
'''sequence-classification''': AutoModelForSequenceClassification,
'''question-answering''': AutoModelForQuestionAnswering,
'''pretraining''': AutoModelForPreTraining,
'''token-classification''': AutoModelForTokenClassification,
'''language-modeling''': AutoModelWithLMHead,
'''summarization''': AutoModelForSeqaSeqLM,
'''translation''': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
__snake_case :Dict = {
'''linear''': get_linear_schedule_with_warmup,
'''cosine''': get_cosine_schedule_with_warmup,
'''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
'''polynomial''': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
__snake_case :Optional[Any] = sorted(arg_to_scheduler.keys())
__snake_case :Tuple = '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class _A ( pl.LightningModule ):
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : argparse.Namespace , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : List[Any]="base" , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : List[str]=None , **__SCREAMING_SNAKE_CASE : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(__SCREAMING_SNAKE_CASE)
__a = 0
__a = Path(self.hparams.output_dir)
__a = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
__a = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({'''num_labels''': num_labels} if num_labels is not None else {}) , cache_dir=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
else:
__a = config
__a = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
assert hasattr(self.config , __SCREAMING_SNAKE_CASE), F'model config doesn\'t have a `{p}` attribute'
setattr(self.config , __SCREAMING_SNAKE_CASE , getattr(self.hparams , __SCREAMING_SNAKE_CASE))
if tokenizer is None:
__a = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=__SCREAMING_SNAKE_CASE , )
else:
__a = tokenizer
__a = MODEL_MODES[mode]
if model is None:
__a = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path) , config=self.config , cache_dir=__SCREAMING_SNAKE_CASE , )
else:
__a = model
def _lowerCamelCase ( self : Optional[int] , *__SCREAMING_SNAKE_CASE : str , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
__a = self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = arg_to_scheduler[self.hparams.lr_scheduler]
__a = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps())
__a = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a = self.model
__a = ['''bias''', '''LayerNorm.weight''']
__a = [
{
'''params''': [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
], # check this named paramters
'''weight_decay''': self.hparams.weight_decay,
},
{
'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
'''weight_decay''': 0.0,
},
]
if self.hparams.adafactor:
__a = Adafactor(
__SCREAMING_SNAKE_CASE , lr=self.hparams.learning_rate , scale_parameter=__SCREAMING_SNAKE_CASE , relative_step=__SCREAMING_SNAKE_CASE)
else:
__a = AdamW(
__SCREAMING_SNAKE_CASE , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon)
__a = optimizer
__a = self.get_lr_scheduler()
return [optimizer], [scheduler]
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any]):
'''simple docstring'''
return self.validation_step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : List[Any]):
'''simple docstring'''
return self.validation_end(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Any):
'''simple docstring'''
__a = max(1 , self.hparams.gpus) # TODO: consider num_tpu_cores
__a = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def _lowerCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : List[str]):
'''simple docstring'''
if stage == "test":
__a = len(self.test_dataloader().dataset)
else:
__a = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=__SCREAMING_SNAKE_CASE)
__a = len(self.train_dataloader().dataset)
def _lowerCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : bool = False):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''')
def _lowerCamelCase ( self : str):
'''simple docstring'''
return self.train_loader
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any]):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
__SCREAMING_SNAKE_CASE , list(filter(__SCREAMING_SNAKE_CASE , self.hparams.model_name_or_path.split('''/'''))).pop() , str(self.hparams.max_seq_length) , ) , )
@pl.utilities.rank_zero_only
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : Dict[str, Any]):
'''simple docstring'''
__a = self.output_dir.joinpath('''best_tfmr''')
__a = self.step_count
self.model.save_pretrained(__SCREAMING_SNAKE_CASE)
self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
@staticmethod
def _lowerCamelCase ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any]):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=__SCREAMING_SNAKE_CASE , help='''Pretrained config name or path if not the same as model_name''')
parser.add_argument(
'''--tokenizer_name''' , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(__SCREAMING_SNAKE_CASE).parent / '''test_run''' / '''cache''') , type=__SCREAMING_SNAKE_CASE , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=__SCREAMING_SNAKE_CASE , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=__SCREAMING_SNAKE_CASE , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=__SCREAMING_SNAKE_CASE , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=__SCREAMING_SNAKE_CASE , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=__SCREAMING_SNAKE_CASE , help='''The initial learning rate for Adam.''')
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=__SCREAMING_SNAKE_CASE , metavar=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=__SCREAMING_SNAKE_CASE , help='''Weight decay if we apply some.''')
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=__SCREAMING_SNAKE_CASE , help='''Epsilon for Adam optimizer.''')
parser.add_argument('''--warmup_steps''' , default=0 , type=__SCREAMING_SNAKE_CASE , help='''Linear warmup over warmup_steps.''')
parser.add_argument('''--num_workers''' , default=4 , type=__SCREAMING_SNAKE_CASE , help='''kwarg passed to DataLoader''')
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=__SCREAMING_SNAKE_CASE)
parser.add_argument('''--train_batch_size''' , default=32 , type=__SCREAMING_SNAKE_CASE)
parser.add_argument('''--eval_batch_size''' , default=32 , type=__SCREAMING_SNAKE_CASE)
parser.add_argument('''--adafactor''' , action='''store_true''')
class _A ( pl.Callback ):
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Dict):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class _A ( pl.Callback ):
def _lowerCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(__SCREAMING_SNAKE_CASE)
class _A ( pl.Callback ):
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
__a = trainer.lr_schedulers[0]['''scheduler''']
__a = {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr())}
pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : pl.Trainer , __SCREAMING_SNAKE_CASE : pl.LightningModule):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''')
__a = trainer.callback_metrics
# Log results
for key in sorted(__SCREAMING_SNAKE_CASE):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE , str(metrics[key])))
def _lowerCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : pl.Trainer , __SCREAMING_SNAKE_CASE : pl.LightningModule):
'''simple docstring'''
rank_zero_info('''***** Test results *****''')
__a = trainer.callback_metrics
# Log and save results to file
__a = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''')
with open(__SCREAMING_SNAKE_CASE , '''w''') as writer:
for key in sorted(__SCREAMING_SNAKE_CASE):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE , str(metrics[key])))
writer.write('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE , str(metrics[key])))
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_UpperCAmelCase ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_UpperCAmelCase , help='''The output directory where the model predictions and checkpoints will be written.''' , )
parser.add_argument(
'''--fp16''' , action='''store_true''' , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''' , )
parser.add_argument(
'''--fp16_opt_level''' , type=_UpperCAmelCase , default='''O2''' , help=(
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
'''See details at https://nvidia.github.io/apex/amp.html'''
) , )
parser.add_argument('''--n_tpu_cores''' , dest='''tpu_cores''' , type=_UpperCAmelCase )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_UpperCAmelCase , help='''Max gradient norm''' )
parser.add_argument('''--do_train''' , action='''store_true''' , help='''Whether to run training.''' )
parser.add_argument('''--do_predict''' , action='''store_true''' , help='''Whether to run predictions on the test set.''' )
parser.add_argument(
'''--gradient_accumulation_steps''' , dest='''accumulate_grad_batches''' , type=_UpperCAmelCase , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_UpperCAmelCase , default=42 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_UpperCAmelCase ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_UpperCAmelCase , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=True , _UpperCAmelCase=[] , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase , ):
pl.seed_everything(args.seed )
# init model
__a = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_UpperCAmelCase )
# add custom checkpoints
if checkpoint_callback is None:
__a = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix='''checkpoint''' , monitor='''val_loss''' , mode='''min''' , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_UpperCAmelCase )
if logging_callback is None:
__a = LoggingCallback()
__a = {}
if args.fpaa:
__a = 16
if args.gpus > 1:
__a = '''auto'''
__a = '''ddp'''
__a = args.accumulate_grad_batches
__a = None
__a = '''auto'''
__a = pl.Trainer.from_argparse_args(
_UpperCAmelCase , weights_summary=_UpperCAmelCase , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_UpperCAmelCase , val_check_interval=1 , num_sanity_val_steps=2 , **_UpperCAmelCase , )
if args.do_train:
trainer.fit(_UpperCAmelCase )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 131
|
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
__snake_case :Tuple = logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase )
class _A ( __UpperCAmelCase ):
def __init__( self : Optional[int] , **__SCREAMING_SNAKE_CASE : Optional[Any]):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
if self.framework == "tf":
raise ValueError(F'The {self.__class__} is only available in PyTorch.')
requires_backends(self , '''vision''')
self.check_model_type(__SCREAMING_SNAKE_CASE)
def __call__( self : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, "Image.Image", List[Dict[str, Any]]] , __SCREAMING_SNAKE_CASE : Union[str, List[str]] = None , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ):
'''simple docstring'''
if "text_queries" in kwargs:
__a = kwargs.pop('''text_queries''')
if isinstance(__SCREAMING_SNAKE_CASE , (str, Image.Image)):
__a = {'''image''': image, '''candidate_labels''': candidate_labels}
else:
__a = image
__a = super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
return results
def _lowerCamelCase ( self : str , **__SCREAMING_SNAKE_CASE : Union[str, Any]):
'''simple docstring'''
__a = {}
if "threshold" in kwargs:
__a = kwargs['''threshold''']
if "top_k" in kwargs:
__a = kwargs['''top_k''']
return {}, {}, postprocess_params
def _lowerCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
__a = load_image(inputs['''image'''])
__a = inputs['''candidate_labels''']
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
__a = candidate_labels.split(''',''')
__a = torch.tensor([[image.height, image.width]] , dtype=torch.intaa)
for i, candidate_label in enumerate(__SCREAMING_SNAKE_CASE):
__a = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
__a = self.image_processor(__SCREAMING_SNAKE_CASE , return_tensors=self.framework)
yield {
"is_last": i == len(__SCREAMING_SNAKE_CASE) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
__a = model_inputs.pop('''target_size''')
__a = model_inputs.pop('''candidate_label''')
__a = model_inputs.pop('''is_last''')
__a = self.model(**__SCREAMING_SNAKE_CASE)
__a = {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
return model_outputs
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple=0.1 , __SCREAMING_SNAKE_CASE : Union[str, Any]=None):
'''simple docstring'''
__a = []
for model_output in model_outputs:
__a = model_output['''candidate_label''']
__a = BaseModelOutput(__SCREAMING_SNAKE_CASE)
__a = self.image_processor.post_process_object_detection(
outputs=__SCREAMING_SNAKE_CASE , threshold=__SCREAMING_SNAKE_CASE , target_sizes=model_output['''target_size'''])[0]
for index in outputs["scores"].nonzero():
__a = outputs['''scores'''][index].item()
__a = self._get_bounding_box(outputs['''boxes'''][index][0])
__a = {'''score''': score, '''label''': label, '''box''': box}
results.append(__SCREAMING_SNAKE_CASE)
__a = sorted(__SCREAMING_SNAKE_CASE , key=lambda __SCREAMING_SNAKE_CASE: x["score"] , reverse=__SCREAMING_SNAKE_CASE)
if top_k:
__a = results[:top_k]
return results
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : "torch.Tensor"):
'''simple docstring'''
if self.framework != "pt":
raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.''')
__a , __a , __a , __a = box.int().tolist()
__a = {
'''xmin''': xmin,
'''ymin''': ymin,
'''xmax''': xmax,
'''ymax''': ymax,
}
return bbox
| 131
| 1
|
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
SCREAMING_SNAKE_CASE : int = 16
SCREAMING_SNAKE_CASE : str = 32
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ = 16 ) -> Dict:
_lowercase : Optional[int] = AutoTokenizer.from_pretrained('bert-base-cased' )
_lowercase : str = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowerCamelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_lowercase : Union[str, Any] = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=lowerCamelCase_ , max_length=lowerCamelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_lowercase : str = datasets.map(
lowerCamelCase_ , batched=lowerCamelCase_ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowercase : str = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCamelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_lowercase : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_lowercase : Union[str, Any] = 16
elif accelerator.mixed_precision != "no":
_lowercase : List[str] = 8
else:
_lowercase : List[Any] = None
return tokenizer.pad(
lowerCamelCase_ , padding='longest' , max_length=lowerCamelCase_ , pad_to_multiple_of=lowerCamelCase_ , return_tensors='pt' , )
# Instantiate dataloaders.
_lowercase : Any = DataLoader(
tokenized_datasets['train'] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
_lowercase : Optional[Any] = DataLoader(
tokenized_datasets['validation'] , shuffle=lowerCamelCase_ , collate_fn=lowerCamelCase_ , batch_size=lowerCamelCase_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
SCREAMING_SNAKE_CASE : Tuple = mocked_dataloaders # noqa: F811
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , lowerCamelCase_ ) == "1":
_lowercase : int = 2
# Initialize accelerator
_lowercase : Dict = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowercase : str = config['lr']
_lowercase : Any = int(config['num_epochs'] )
_lowercase : int = int(config['seed'] )
_lowercase : Dict = int(config['batch_size'] )
_lowercase : int = evaluate.load('glue' , 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCamelCase_ )
def inner_training_loop(lowerCamelCase_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCamelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowercase : List[str] = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=lowerCamelCase_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_lowercase : List[Any] = model.to(accelerator.device )
# Instantiate optimizer
_lowercase : Dict = AdamW(params=model.parameters() , lr=lowerCamelCase_ )
_lowercase , _lowercase : Tuple = get_dataloaders(lowerCamelCase_ , lowerCamelCase_ )
# Instantiate scheduler
_lowercase : int = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase_ , num_warmup_steps=100 , num_training_steps=(len(lowerCamelCase_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase : List[str] = accelerator.prepare(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# Now we train the model
for epoch in range(lowerCamelCase_ ):
model.train()
for step, batch in enumerate(lowerCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_lowercase : str = model(**lowerCamelCase_ )
_lowercase : List[str] = outputs.loss
accelerator.backward(lowerCamelCase_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCamelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowercase : List[str] = model(**lowerCamelCase_ )
_lowercase : List[Any] = outputs.logits.argmax(dim=-1 )
_lowercase , _lowercase : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCamelCase_ , references=lowerCamelCase_ , )
_lowercase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , lowerCamelCase_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def UpperCamelCase_( ) -> List[Any]:
_lowercase : Tuple = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=lowerCamelCase_ , default=lowerCamelCase_ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
_lowercase : Union[str, Any] = parser.parse_args()
_lowercase : Optional[int] = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCamelCase_ , lowerCamelCase_ )
if __name__ == "__main__":
main()
| 21
|
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self , lowercase) -> Dict:
'''simple docstring'''
for model_result in results.values():
for batch_size, sequence_length in zip(model_result['bs'] , model_result['ss']):
a__ : int = model_result['result'][batch_size][sequence_length]
self.assertIsNotNone(lowercase)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : Tuple = 'sshleifer/tiny-gpt2'
a__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowercase , multi_process=lowercase , )
a__ : List[Any] = TensorFlowBenchmark(lowercase)
a__ : Any = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : Optional[Any] = 'sgugger/tiny-distilbert-classification'
a__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , only_pretrain_model=lowercase , )
a__ : str = TensorFlowBenchmark(lowercase)
a__ : int = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> List[str]:
'''simple docstring'''
a__ : Union[str, Any] = 'sshleifer/tiny-gpt2'
a__ : Dict = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : List[str] = TensorFlowBenchmark(lowercase)
a__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Optional[int]:
'''simple docstring'''
a__ : str = 'sshleifer/tiny-gpt2'
a__ : str = AutoConfig.from_pretrained(lowercase)
a__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , eager_mode=lowercase , multi_process=lowercase , )
a__ : Optional[Any] = TensorFlowBenchmark(lowercase , [config])
a__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Dict = 'sshleifer/tiny-gpt2'
a__ : Union[str, Any] = AutoConfig.from_pretrained(lowercase)
a__ : Optional[int] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : str = TensorFlowBenchmark(lowercase , [config])
a__ : List[str] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : Optional[int] = 'sshleifer/tiny-gpt2'
a__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : Tuple = TensorFlowBenchmark(lowercase)
a__ : str = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Optional[Any] = 'sshleifer/tiny-gpt2'
a__ : List[Any] = AutoConfig.from_pretrained(lowercase)
a__ : List[Any] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : Optional[Any] = TensorFlowBenchmark(lowercase , [config])
a__ : List[Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def __lowercase ( self) -> int:
'''simple docstring'''
a__ : Optional[int] = 'patrickvonplaten/t5-tiny-random'
a__ : Optional[int] = AutoConfig.from_pretrained(lowercase)
a__ : Any = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=lowercase , )
a__ : List[Any] = TensorFlowBenchmark(lowercase , configs=[config])
a__ : Union[str, Any] = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU')) == 0 , 'Cannot do xla on CPU.')
def __lowercase ( self) -> List[Any]:
'''simple docstring'''
a__ : Optional[int] = 'sshleifer/tiny-gpt2'
a__ : str = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , training=lowercase , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , use_xla=lowercase , multi_process=lowercase , )
a__ : int = TensorFlowBenchmark(lowercase)
a__ : Tuple = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def __lowercase ( self) -> Dict:
'''simple docstring'''
a__ : str = 'sshleifer/tiny-gpt2'
with tempfile.TemporaryDirectory() as tmp_dir:
a__ : int = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowercase , save_to_csv=lowercase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(lowercase , 'inf_time.csv') , inference_memory_csv_file=os.path.join(lowercase , 'inf_mem.csv') , env_info_csv_file=os.path.join(lowercase , 'env.csv') , multi_process=lowercase , )
a__ : List[str] = TensorFlowBenchmark(lowercase)
benchmark.run()
self.assertTrue(Path(os.path.join(lowercase , 'inf_time.csv')).exists())
self.assertTrue(Path(os.path.join(lowercase , 'inf_mem.csv')).exists())
self.assertTrue(Path(os.path.join(lowercase , 'env.csv')).exists())
def __lowercase ( self) -> str:
'''simple docstring'''
a__ : Union[str, Any] = 'sshleifer/tiny-gpt2'
def _check_summary_is_not_empty(lowercase):
self.assertTrue(hasattr(lowercase , 'sequential'))
self.assertTrue(hasattr(lowercase , 'cumulative'))
self.assertTrue(hasattr(lowercase , 'current'))
self.assertTrue(hasattr(lowercase , 'total'))
with tempfile.TemporaryDirectory() as tmp_dir:
a__ : List[str] = TensorFlowBenchmarkArguments(
models=[MODEL_ID] , inference=lowercase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(lowercase , 'log.txt') , log_print=lowercase , trace_memory_line_by_line=lowercase , eager_mode=lowercase , multi_process=lowercase , )
a__ : List[str] = TensorFlowBenchmark(lowercase)
a__ : Any = benchmark.run()
_check_summary_is_not_empty(result.inference_summary)
self.assertTrue(Path(os.path.join(lowercase , 'log.txt')).exists())
| 99
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST",
"PegasusXForConditionalGeneration",
"PegasusXModel",
"PegasusXPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 2
|
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
"--original_config_file",
default=None,
type=str,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--num_in_channels",
default=None,
type=int,
help="The number of input channels. If `None` number of input channels will be automatically inferred.",
)
parser.add_argument(
"--scheduler_type",
default="pndm",
type=str,
help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
)
parser.add_argument(
"--pipeline_type",
default=None,
type=str,
help=(
"The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
". If `None` pipeline will be automatically inferred."
),
)
parser.add_argument(
"--image_size",
default=None,
type=int,
help=(
"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
" Base. Use 768 for Stable Diffusion v2."
),
)
parser.add_argument(
"--prediction_type",
default=None,
type=str,
help=(
"The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
" Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
),
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument(
"--upcast_attention",
action="store_true",
help=(
"Whether the attention computation should always be upcasted. This is necessary when running stable"
" diffusion 2.1."
),
)
parser.add_argument(
"--from_safetensors",
action="store_true",
help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
)
parser.add_argument(
"--to_safetensors",
action="store_true",
help="Whether to store pipeline in safetensors format or not.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
parser.add_argument(
"--stable_unclip",
type=str,
default=None,
required=False,
help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
)
parser.add_argument(
"--stable_unclip_prior",
type=str,
default=None,
required=False,
help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
)
parser.add_argument(
"--clip_stats_path",
type=str,
help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
required=False,
)
parser.add_argument(
"--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
)
parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
parser.add_argument(
"--vae_path",
type=str,
default=None,
required=False,
help="Set to a path, hub id to an already converted vae to not convert it again.",
)
__A = parser.parse_args()
__A = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 2
| 1
|
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
lowerCAmelCase__ = random.Random()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: Optional[int]=1.0 , SCREAMING_SNAKE_CASE_: Tuple=None , SCREAMING_SNAKE_CASE_: Dict=None ) -> List[Any]:
'''simple docstring'''
if rng is None:
A__ = global_rng
A__ = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class a__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowercase , lowercase=7 , lowercase=400 , lowercase=2000 , lowercase=1 , lowercase=0.0 , lowercase=16000 , lowercase=True , lowercase=80 , lowercase=16 , lowercase=64 , lowercase="hann_window" , lowercase=80 , lowercase=7600 , lowercase=1e-10 , lowercase=True , ) -> Union[str, Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = min_seq_length
A__ = max_seq_length
A__ = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
A__ = feature_size
A__ = padding_value
A__ = sampling_rate
A__ = do_normalize
A__ = num_mel_bins
A__ = hop_length
A__ = win_length
A__ = win_function
A__ = fmin
A__ = fmax
A__ = mel_floor
A__ = return_attention_mask
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def UpperCamelCase ( self , lowercase=False , lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
def _flatten(lowercase ):
return list(itertools.chain(*lowercase ) )
if equal_length:
A__ = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
A__ = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
A__ = [np.asarray(lowercase ) for x in speech_inputs]
return speech_inputs
def UpperCamelCase ( self , lowercase=False , lowercase=False ) -> Union[str, Any]:
'''simple docstring'''
if equal_length:
A__ = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
A__ = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
A__ = [np.asarray(lowercase ) for x in speech_inputs]
return speech_inputs
@require_torch
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaFeatureExtractor
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = SpeechTaFeatureExtractionTester(self )
def UpperCamelCase ( self , lowercase ) -> Dict:
'''simple docstring'''
self.assertTrue(np.all(np.mean(lowercase , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowercase , axis=0 ) - 1 ) < 1e-3 ) )
def UpperCamelCase ( self ) -> Optional[Any]:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = [np.asarray(lowercase ) for speech_input in speech_inputs]
# Test not batched input
A__ = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
A__ = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
# Test batched
A__ = feat_extract(lowercase , return_tensors="np" ).input_values
A__ = feat_extract(lowercase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase ):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = ["longest", "max_length", "do_not_pad"]
A__ = [None, 1600, None]
for max_length, padding in zip(lowercase , lowercase ):
A__ = feat_extract(lowercase , padding=lowercase , max_length=lowercase , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = range(800 , 1400 , 200 )
A__ = [floats_list((1, x) )[0] for x in lengths]
A__ = ["longest", "max_length", "do_not_pad"]
A__ = [None, 1600, None]
for max_length, padding in zip(lowercase , lowercase ):
A__ = feat_extract(lowercase , max_length=lowercase , padding=lowercase )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = feat_extract(
lowercase , truncation=lowercase , max_length=1000 , padding="max_length" , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def UpperCamelCase ( self ) -> str:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = feat_extract(
lowercase , truncation=lowercase , max_length=1000 , padding="longest" , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = feat_extract(
lowercase , truncation=lowercase , max_length=2000 , padding="longest" , return_tensors="np" )
A__ = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
A__ = np.random.rand(100 ).astype(np.floataa )
A__ = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
A__ = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
A__ = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
A__ = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
A__ = [np.asarray(lowercase ) for speech_input in speech_inputs]
# Test feature size
A__ = feature_extractor(audio_target=lowercase , padding=lowercase , return_tensors="np" ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
A__ = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_values
A__ = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
# Test batched
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase ):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
A__ = [floats_list((1, x) )[0] for x in (800, 800, 800)]
A__ = np.asarray(lowercase )
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
A__ = feature_extractor(lowercase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowercase , lowercase ):
self.assertTrue(np.allclose(lowercase , lowercase , atol=1e-3 ) )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = self.feature_extraction_class(**self.feat_extract_dict )
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(lowercase ) == len(lowercase ) for x, y in zip(lowercase , processed_features[input_name] ) ) )
A__ = self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase )
A__ = BatchFeature({input_name: speech_inputs} , tensor_type="np" )
A__ = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
A__ = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
A__ = self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase )
A__ = self.feature_extraction_class(**self.feat_extract_dict )
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} , tensor_type="pt" )
A__ = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
A__ = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.feature_extraction_class(**self.feat_extract_dict )
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
A__ = feat_extract.num_mel_bins # hack!
A__ = feat_extract.pad(lowercase , padding="longest" , return_tensors="np" )[input_name]
A__ = feat_extract.pad(lowercase , padding="longest" , return_tensors="pt" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.feat_extract_dict
A__ = True
A__ = self.feature_extraction_class(**lowercase )
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = [len(lowercase ) for x in speech_inputs]
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
A__ = feat_extract.num_mel_bins # hack!
A__ = feat_extract.pad(lowercase , padding="longest" , return_tensors="np" )
self.assertIn("attention_mask" , lowercase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , lowercase )
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = self.feat_extract_dict
A__ = True
A__ = self.feature_extraction_class(**lowercase )
A__ = self.feat_extract_tester.prepare_inputs_for_target()
A__ = [len(lowercase ) for x in speech_inputs]
A__ = feat_extract.model_input_names[0]
A__ = BatchFeature({input_name: speech_inputs} )
A__ = min(lowercase )
A__ = feat_extract.num_mel_bins # hack!
A__ = feat_extract.pad(
lowercase , padding="max_length" , max_length=lowercase , truncation=lowercase , return_tensors="np" )
self.assertIn("attention_mask" , lowercase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def UpperCamelCase ( self , lowercase ) -> Tuple:
'''simple docstring'''
from datasets import load_dataset
A__ = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
A__ = ds.sort("id" ).select(range(lowercase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
A__ = torch.tensor(
[2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03] )
# fmt: on
A__ = self._load_datasamples(1 )
A__ = SpeechTaFeatureExtractor()
A__ = feature_extractor(lowercase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 93680) )
self.assertTrue(torch.allclose(input_values[0, :30] , lowercase , atol=1e-6 ) )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
A__ = self._load_datasamples(1 )
A__ = SpeechTaFeatureExtractor()
A__ = feature_extractor(audio_target=lowercase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , lowercase , atol=1e-4 ) )
| 68
|
def lowerCAmelCase__ ( ) -> Any:
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simple docstring'''
A__ = 1
A__ = 2
while i * i <= n:
A__ = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowerCAmelCase__ ( ) -> Dict:
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(SCREAMING_SNAKE_CASE_ ) > 5_0_0 )
if __name__ == "__main__":
print(solution())
| 68
| 1
|
'''simple docstring'''
lowerCamelCase_ = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def __lowercase ( __lowercase ) -> str:
'''simple docstring'''
assert type(__lowercase ) in (int, float) and decimal == int(__lowercase )
_A = int(__lowercase )
_A = ""
_A = False
if decimal < 0:
_A = True
decimal *= -1
while decimal > 0:
_A , _A = divmod(__lowercase , 16 )
_A = values[remainder] + hexadecimal
_A = "0x" + hexadecimal
if negative:
_A = "-" + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 360
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = '''camembert'''
def __init__( self : Union[str, Any] , __UpperCAmelCase : int=30522 , __UpperCAmelCase : List[str]=768 , __UpperCAmelCase : Optional[Any]=12 , __UpperCAmelCase : Optional[Any]=12 , __UpperCAmelCase : Dict=3072 , __UpperCAmelCase : Any="gelu" , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : List[str]=0.1 , __UpperCAmelCase : List[Any]=512 , __UpperCAmelCase : Tuple=2 , __UpperCAmelCase : List[str]=0.02 , __UpperCAmelCase : List[str]=1E-12 , __UpperCAmelCase : Any=1 , __UpperCAmelCase : Optional[Any]=0 , __UpperCAmelCase : str=2 , __UpperCAmelCase : int="absolute" , __UpperCAmelCase : Any=True , __UpperCAmelCase : int=None , **__UpperCAmelCase : List[Any] , ):
'''simple docstring'''
super().__init__(pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase )
_A = vocab_size
_A = hidden_size
_A = num_hidden_layers
_A = num_attention_heads
_A = hidden_act
_A = intermediate_size
_A = hidden_dropout_prob
_A = attention_probs_dropout_prob
_A = max_position_embeddings
_A = type_vocab_size
_A = initializer_range
_A = layer_norm_eps
_A = position_embedding_type
_A = use_cache
_A = classifier_dropout
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
@property
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
if self.task == "multiple-choice":
_A = {0: "batch", 1: "choice", 2: "sequence"}
else:
_A = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 174
| 0
|
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( snake_case__ , snake_case__ , unittest.TestCase):
"""simple docstring"""
UpperCamelCase__ = StableDiffusionDiffEditPipeline
UpperCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
UpperCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
UpperCamelCase__ = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase__ = frozenset([])
def UpperCamelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase , )
_UpperCAmelCase = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' , clip_sample=UpperCAmelCase , set_alpha_to_one=UpperCAmelCase , )
_UpperCAmelCase = DDIMInverseScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' , clip_sample=UpperCAmelCase , set_alpha_to_zero=UpperCAmelCase , )
torch.manual_seed(0 )
_UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
_UpperCAmelCase = CLIPTextModel(UpperCAmelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'inverse_scheduler': inverse_scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase=0 ):
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 16, 16) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
_UpperCAmelCase = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
if str(UpperCAmelCase ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(UpperCAmelCase )
else:
_UpperCAmelCase = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
_UpperCAmelCase = {
'prompt': 'a dog and a newt',
'mask_image': mask,
'image_latents': latents,
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase=0 ):
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(UpperCAmelCase ) ).convert('RGB' )
if str(UpperCAmelCase ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(UpperCAmelCase )
else:
_UpperCAmelCase = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
_UpperCAmelCase = {
'image': image,
'source_prompt': 'a cat and a frog',
'target_prompt': 'a dog and a newt',
'generator': generator,
'num_inference_steps': 2,
'num_maps_per_mask': 2,
'mask_encode_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase=0 ):
"""simple docstring"""
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(UpperCAmelCase ) ).convert('RGB' )
if str(UpperCAmelCase ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(UpperCAmelCase )
else:
_UpperCAmelCase = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
_UpperCAmelCase = {
'image': image,
'prompt': 'a cat and a frog',
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'decode_latents': True,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase ( self ):
"""simple docstring"""
if not hasattr(self.pipeline_class , '_optional_components' ):
return
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
_UpperCAmelCase = self.get_dummy_inputs(UpperCAmelCase )
_UpperCAmelCase = pipe(**UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCAmelCase )
_UpperCAmelCase = self.pipeline_class.from_pretrained(UpperCAmelCase )
pipe_loaded.to(UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCAmelCase , UpperCAmelCase ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
_UpperCAmelCase = self.get_dummy_inputs(UpperCAmelCase )
_UpperCAmelCase = pipe_loaded(**UpperCAmelCase )[0]
_UpperCAmelCase = np.abs(output - output_loaded ).max()
self.assertLess(UpperCAmelCase , 1e-4 )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = self.get_dummy_mask_inputs(UpperCAmelCase )
_UpperCAmelCase = pipe.generate_mask(**UpperCAmelCase )
_UpperCAmelCase = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
_UpperCAmelCase = np.array([0] * 9 )
_UpperCAmelCase = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = self.get_dummy_inversion_inputs(UpperCAmelCase )
_UpperCAmelCase = pipe.invert(**UpperCAmelCase ).images
_UpperCAmelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
_UpperCAmelCase = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99] , )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1e-3 )
def UpperCamelCase ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = {'beta_start': 0.0_00_85, 'beta_end': 0.0_12, 'beta_schedule': 'scaled_linear'}
_UpperCAmelCase = DPMSolverMultistepScheduler(**UpperCAmelCase )
_UpperCAmelCase = DPMSolverMultistepInverseScheduler(**UpperCAmelCase )
_UpperCAmelCase = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = self.get_dummy_inversion_inputs(UpperCAmelCase )
_UpperCAmelCase = pipe.invert(**UpperCAmelCase ).images
_UpperCAmelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
_UpperCAmelCase = np.array(
[0.51_50, 0.51_34, 0.50_43, 0.53_76, 0.46_94, 0.5_10_50, 0.50_15, 0.44_07, 0.47_99] , )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1e-3 )
@require_torch_gpu
@slow
class __lowerCamelCase ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def UpperCamelCase ( cls ):
"""simple docstring"""
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png' )
_UpperCAmelCase = raw_image.convert('RGB' ).resize((768, 768) )
_UpperCAmelCase = raw_image
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=UpperCAmelCase , torch_dtype=torch.floataa )
_UpperCAmelCase = DDIMScheduler.from_config(pipe.scheduler.config )
_UpperCAmelCase = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = 'a bowl of fruit'
_UpperCAmelCase = 'a bowl of pears'
_UpperCAmelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCAmelCase , target_prompt=UpperCAmelCase , generator=UpperCAmelCase , )
_UpperCAmelCase = pipe.invert(
prompt=UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCAmelCase ).latents
_UpperCAmelCase = pipe(
prompt=UpperCAmelCase , mask_image=UpperCAmelCase , image_latents=UpperCAmelCase , generator=UpperCAmelCase , negative_prompt=UpperCAmelCase , inpaint_strength=0.7 , output_type='numpy' , ).images[0]
_UpperCAmelCase = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
def UpperCamelCase ( self ):
"""simple docstring"""
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=UpperCAmelCase , torch_dtype=torch.floataa )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_UpperCAmelCase = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase )
_UpperCAmelCase = 'a bowl of fruit'
_UpperCAmelCase = 'a bowl of pears'
_UpperCAmelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCAmelCase , target_prompt=UpperCAmelCase , generator=UpperCAmelCase , )
_UpperCAmelCase = pipe.invert(
prompt=UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCAmelCase , num_inference_steps=25 , ).latents
_UpperCAmelCase = pipe(
prompt=UpperCAmelCase , mask_image=UpperCAmelCase , image_latents=UpperCAmelCase , generator=UpperCAmelCase , negative_prompt=UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type='numpy' , ).images[0]
_UpperCAmelCase = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 39
|
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_a = get_logger(__name__)
class __lowerCamelCase ( enum.Enum):
"""simple docstring"""
UpperCamelCase__ = "all_checks"
UpperCamelCase__ = "basic_checks"
UpperCamelCase__ = "no_checks"
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None )-> str:
"""simple docstring"""
if expected_checksums is None:
logger.info('Unable to verify checksums.' )
return
if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) )
if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0:
raise UnexpectedDownloadedFile(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) )
_UpperCAmelCase = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
_UpperCAmelCase = ' for ' + verification_name if verification_name is not None else ''
if len(__lowerCAmelCase ) > 0:
raise NonMatchingChecksumError(
F"""Checksums didn't match{for_verification_name}:\n"""
F"""{bad_urls}\n"""
'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error' )
logger.info('All the checksums matched successfully' + for_verification_name )
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
class __lowerCamelCase ( snake_case__):
"""simple docstring"""
def __A ( __lowerCAmelCase , __lowerCAmelCase )-> int:
"""simple docstring"""
if expected_splits is None:
logger.info('Unable to verify splits sizes.' )
return
if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0:
raise ExpectedMoreSplits(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) )
if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0:
raise UnexpectedSplits(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) )
_UpperCAmelCase = [
{'expected': expected_splits[name], 'recorded': recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(__lowerCAmelCase ) > 0:
raise NonMatchingSplitsSizesError(str(__lowerCAmelCase ) )
logger.info('All the splits matched successfully.' )
def __A ( __lowerCAmelCase , __lowerCAmelCase = True )-> dict:
"""simple docstring"""
if record_checksum:
_UpperCAmelCase = shaaaa()
with open(__lowerCAmelCase , 'rb' ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , b'' ):
m.update(__lowerCAmelCase )
_UpperCAmelCase = m.hexdigest()
else:
_UpperCAmelCase = None
return {"num_bytes": os.path.getsize(__lowerCAmelCase ), "checksum": checksum}
def __A ( __lowerCAmelCase )-> List[str]:
"""simple docstring"""
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 39
| 1
|
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A__ :
def __init__( self : Any , a : Optional[Any] , a : int=13 , a : List[Any]=32 , a : Optional[int]=3 , a : List[str]=4 , a : List[Any]=[10, 20, 30, 40] , a : Optional[int]=[2, 2, 3, 2] , a : Any=True , a : Tuple=True , a : int=37 , a : List[str]="gelu" , a : Dict=10 , a : Optional[int]=0.0_2 , a : List[Any]=["stage2", "stage3", "stage4"] , a : Union[str, Any]=3 , a : Tuple=None , ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = parent
lowerCAmelCase__ : Any = batch_size
lowerCAmelCase__ : Tuple = image_size
lowerCAmelCase__ : Optional[int] = num_channels
lowerCAmelCase__ : Any = num_stages
lowerCAmelCase__ : str = hidden_sizes
lowerCAmelCase__ : Dict = depths
lowerCAmelCase__ : int = is_training
lowerCAmelCase__ : str = use_labels
lowerCAmelCase__ : Dict = intermediate_size
lowerCAmelCase__ : List[str] = hidden_act
lowerCAmelCase__ : Optional[Any] = type_sequence_label_size
lowerCAmelCase__ : str = initializer_range
lowerCAmelCase__ : str = out_features
lowerCAmelCase__ : List[Any] = num_labels
lowerCAmelCase__ : Optional[int] = scope
lowerCAmelCase__ : Any = num_stages
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ : List[str] = None
if self.use_labels:
lowerCAmelCase__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ : int = self.get_config()
return config, pixel_values, labels
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=a , loss_ignore_index=255 , num_labels=self.num_labels , )
def _lowerCamelCase ( self : Tuple , a : Tuple , a : Any , a : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = UperNetForSemanticSegmentation(config=a )
model.to(a )
model.eval()
lowerCAmelCase__ : Dict = model(a )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Tuple = self.prepare_config_and_inputs()
(
lowerCAmelCase__
) : Union[str, Any] = config_and_inputs
lowerCAmelCase__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A__ ( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = UperNetModelTester(self )
lowerCAmelCase__ : Optional[Any] = ConfigTester(self , config_class=a , has_text_modality=a , hidden_size=37 )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
return
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : Tuple = model_class(a )
lowerCAmelCase__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ : Optional[int] = [*signature.parameters.keys()]
lowerCAmelCase__ : Tuple = ['pixel_values']
self.assertListEqual(arg_names[:1] , a )
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*a )
@unittest.skip(reason='UperNet does not use inputs_embeds' )
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip(reason='UperNet does not support input and output embeddings' )
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip(reason='UperNet does not have a base model' )
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip(reason='UperNet does not have a base model' )
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
pass
def _lowerCamelCase ( self : Tuple ):
'''simple docstring'''
def check_hidden_states_output(a : str , a : Optional[Any] , a : Optional[Any] ):
lowerCAmelCase__ : Optional[int] = model_class(a )
model.to(a )
model.eval()
with torch.no_grad():
lowerCAmelCase__ : int = model(**self._prepare_for_class(a , a ) )
lowerCAmelCase__ : Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase__ : Tuple = self.model_tester.num_stages
self.assertEqual(len(a ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ : Optional[int] = True
check_hidden_states_output(a , a , a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ : List[str] = True
check_hidden_states_output(a , a , a )
def _lowerCamelCase ( self : int ):
'''simple docstring'''
lowerCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ : Union[str, Any] = _config_zero_init(a )
lowerCAmelCase__ : Union[str, Any] = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
lowerCAmelCase__ : int = model_class(config=a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='UperNet does not have tied weights' )
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
pass
@slow
def _lowerCamelCase ( self : int ):
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ : int = UperNetForSemanticSegmentation.from_pretrained(a )
self.assertIsNotNone(a )
def lowerCAmelCase__ ( ) -> Optional[Any]:
lowerCAmelCase__ : Optional[int] = hf_hub_download(
repo_id='hf-internal-testing/fixtures_ade20k' , repo_type='dataset' , filename='ADE_val_00000001.jpg' )
lowerCAmelCase__ : int = Image.open(SCREAMING_SNAKE_CASE_ ).convert('RGB' )
return image
@require_torch
@require_vision
@slow
class A__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCAmelCase__ : Dict = AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny' )
lowerCAmelCase__ : Union[str, Any] = UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny' ).to(a )
lowerCAmelCase__ : int = prepare_img()
lowerCAmelCase__ : Optional[int] = processor(images=a , return_tensors='pt' ).to(a )
with torch.no_grad():
lowerCAmelCase__ : str = model(**a )
lowerCAmelCase__ : Optional[Any] = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , a )
lowerCAmelCase__ : Tuple = torch.tensor(
[[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , a , atol=1E-4 ) )
def _lowerCamelCase ( self : Dict ):
'''simple docstring'''
lowerCAmelCase__ : int = AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny' )
lowerCAmelCase__ : int = UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny' ).to(a )
lowerCAmelCase__ : str = prepare_img()
lowerCAmelCase__ : Dict = processor(images=a , return_tensors='pt' ).to(a )
with torch.no_grad():
lowerCAmelCase__ : str = model(**a )
lowerCAmelCase__ : Union[str, Any] = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , a )
lowerCAmelCase__ : Any = torch.tensor(
[[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] ).to(a )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , a , atol=1E-4 ) )
| 369
|
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Any , *a : Any , **a : Any ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Union[str, Any] , *a : Optional[int] , **a : Optional[Any] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : int , *a : List[Any] , **a : int ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : str , *a : Any , **a : Optional[Any] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : List[str] , **a : Dict ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[Any] , *a : Optional[Any] , **a : Any ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[int] , *a : List[Any] , **a : str ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : List[Any] , *a : List[str] , **a : List[str] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[Any] , *a : Union[str, Any] , **a : Optional[int] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *a : Dict , **a : List[str] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : Dict , **a : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : List[str] , **a : Dict ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Dict , *a : str , **a : Union[str, Any] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Any , *a : Any , **a : Any ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Any , *a : List[Any] , **a : str ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : str , *a : Union[str, Any] , **a : Optional[Any] ):
'''simple docstring'''
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : int , *a : Union[str, Any] , **a : Dict ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def _lowerCamelCase ( cls : Optional[int] , *a : Tuple , **a : List[str] ):
'''simple docstring'''
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 307
| 0
|
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowerCamelCase = [
'''openmmlab/upernet-convnext-tiny''',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowerCamelCase = '''UperNetConfig'''
class _a ( nn.Module):
def __init__( self : str , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Union[int, Tuple[int, int]] , _SCREAMING_SNAKE_CASE : Union[int, Tuple[int, int], str] = 0 , _SCREAMING_SNAKE_CASE : bool = False , _SCREAMING_SNAKE_CASE : Union[int, Tuple[int, int]] = 1 , )-> None:
super().__init__()
lowerCAmelCase__ : List[str] = nn.Convad(
in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , kernel_size=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , bias=_SCREAMING_SNAKE_CASE , dilation=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : List[str] = nn.BatchNormad(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Union[str, Any] = nn.ReLU()
def UpperCAmelCase__( self : int , _SCREAMING_SNAKE_CASE : torch.Tensor )-> torch.Tensor:
lowerCAmelCase__ : Optional[int] = self.conv(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[int] = self.batch_norm(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[int] = self.activation(_SCREAMING_SNAKE_CASE )
return output
class _a ( nn.Module):
def __init__( self : List[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int )-> None:
super().__init__()
lowerCAmelCase__ : Dict = [
nn.AdaptiveAvgPoolad(_SCREAMING_SNAKE_CASE ),
UperNetConvModule(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : Any , _SCREAMING_SNAKE_CASE : torch.Tensor )-> torch.Tensor:
lowerCAmelCase__ : str = input
for layer in self.layers:
lowerCAmelCase__ : Union[str, Any] = layer(_SCREAMING_SNAKE_CASE )
return hidden_state
class _a ( nn.Module):
def __init__( self : Optional[Any] , _SCREAMING_SNAKE_CASE : Tuple[int, ...] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : bool )-> None:
super().__init__()
lowerCAmelCase__ : Optional[Any] = pool_scales
lowerCAmelCase__ : int = align_corners
lowerCAmelCase__ : Any = in_channels
lowerCAmelCase__ : str = channels
lowerCAmelCase__ : List[Any] = []
for i, pool_scale in enumerate(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ : Tuple = UperNetPyramidPoolingBlock(pool_scale=_SCREAMING_SNAKE_CASE , in_channels=_SCREAMING_SNAKE_CASE , channels=_SCREAMING_SNAKE_CASE )
self.blocks.append(_SCREAMING_SNAKE_CASE )
self.add_module(str(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : Optional[int] , _SCREAMING_SNAKE_CASE : torch.Tensor )-> List[torch.Tensor]:
lowerCAmelCase__ : Dict = []
for ppm in self.blocks:
lowerCAmelCase__ : List[Any] = ppm(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[int] = nn.functional.interpolate(
_SCREAMING_SNAKE_CASE , size=x.size()[2:] , mode='''bilinear''' , align_corners=self.align_corners )
ppm_outs.append(_SCREAMING_SNAKE_CASE )
return ppm_outs
class _a ( nn.Module):
def __init__( self : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Dict )-> Union[str, Any]:
super().__init__()
lowerCAmelCase__ : int = config
lowerCAmelCase__ : List[str] = config.pool_scales # e.g. (1, 2, 3, 6)
lowerCAmelCase__ : Union[str, Any] = in_channels
lowerCAmelCase__ : List[str] = config.hidden_size
lowerCAmelCase__ : Optional[Any] = False
lowerCAmelCase__ : Dict = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
lowerCAmelCase__ : int = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
lowerCAmelCase__ : List[Any] = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
lowerCAmelCase__ : Optional[int] = nn.ModuleList()
lowerCAmelCase__ : str = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
lowerCAmelCase__ : Tuple = UperNetConvModule(_SCREAMING_SNAKE_CASE , self.channels , kernel_size=1 )
lowerCAmelCase__ : int = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(_SCREAMING_SNAKE_CASE )
self.fpn_convs.append(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[Any] = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def UpperCAmelCase__( self : Union[str, Any] )-> str:
self.apply(self._init_weights )
def UpperCAmelCase__( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] )-> Tuple:
if isinstance(_SCREAMING_SNAKE_CASE , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__( self : str , _SCREAMING_SNAKE_CASE : List[Any] )-> Dict:
lowerCAmelCase__ : Optional[int] = inputs[-1]
lowerCAmelCase__ : int = [x]
psp_outs.extend(self.psp_modules(_SCREAMING_SNAKE_CASE ) )
lowerCAmelCase__ : str = torch.cat(_SCREAMING_SNAKE_CASE , dim=1 )
lowerCAmelCase__ : str = self.bottleneck(_SCREAMING_SNAKE_CASE )
return output
def UpperCAmelCase__( self : Dict , _SCREAMING_SNAKE_CASE : torch.Tensor )-> torch.Tensor:
# build laterals
lowerCAmelCase__ : Tuple = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(_SCREAMING_SNAKE_CASE ) )
# build top-down path
lowerCAmelCase__ : List[Any] = len(_SCREAMING_SNAKE_CASE )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
lowerCAmelCase__ : Dict = laterals[i - 1].shape[2:]
lowerCAmelCase__ : Optional[Any] = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=_SCREAMING_SNAKE_CASE , mode='''bilinear''' , align_corners=self.align_corners )
# build outputs
lowerCAmelCase__ : Dict = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
lowerCAmelCase__ : List[Any] = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='''bilinear''' , align_corners=self.align_corners )
lowerCAmelCase__ : int = torch.cat(_SCREAMING_SNAKE_CASE , dim=1 )
lowerCAmelCase__ : int = self.fpn_bottleneck(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[Any] = self.classifier(_SCREAMING_SNAKE_CASE )
return output
class _a ( nn.Module):
def __init__( self : List[str] , _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : int = 2 , _SCREAMING_SNAKE_CASE : int = 3 , _SCREAMING_SNAKE_CASE : Union[int, Tuple[int, int]] = 1 )-> None:
super().__init__()
lowerCAmelCase__ : Optional[int] = config
lowerCAmelCase__ : str = config.auxiliary_in_channels
lowerCAmelCase__ : str = config.auxiliary_channels
lowerCAmelCase__ : Union[str, Any] = config.auxiliary_num_convs
lowerCAmelCase__ : str = config.auxiliary_concat_input
lowerCAmelCase__ : Union[str, Any] = in_index
lowerCAmelCase__ : Tuple = (kernel_size // 2) * dilation
lowerCAmelCase__ : int = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , dilation=_SCREAMING_SNAKE_CASE ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , dilation=_SCREAMING_SNAKE_CASE ) )
if self.num_convs == 0:
lowerCAmelCase__ : Dict = nn.Identity()
else:
lowerCAmelCase__ : List[str] = nn.Sequential(*_SCREAMING_SNAKE_CASE )
if self.concat_input:
lowerCAmelCase__ : Union[str, Any] = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=_SCREAMING_SNAKE_CASE , padding=kernel_size // 2 )
lowerCAmelCase__ : Any = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def UpperCAmelCase__( self : str )-> List[Any]:
self.apply(self._init_weights )
def UpperCAmelCase__( self : Any , _SCREAMING_SNAKE_CASE : Tuple )-> Tuple:
if isinstance(_SCREAMING_SNAKE_CASE , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def UpperCAmelCase__( self : List[Any] , _SCREAMING_SNAKE_CASE : torch.Tensor )-> torch.Tensor:
# just take the relevant feature maps
lowerCAmelCase__ : List[Any] = encoder_hidden_states[self.in_index]
lowerCAmelCase__ : Optional[int] = self.convs(_SCREAMING_SNAKE_CASE )
if self.concat_input:
lowerCAmelCase__ : Optional[Any] = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
lowerCAmelCase__ : Optional[int] = self.classifier(_SCREAMING_SNAKE_CASE )
return output
class _a ( _lowercase):
_a : Tuple = UperNetConfig
_a : Optional[int] = '''pixel_values'''
_a : Union[str, Any] = True
def UpperCAmelCase__( self : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] )-> Any:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def UpperCAmelCase__( self : Dict )-> Optional[int]:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def UpperCAmelCase__( self : List[str] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : List[str]=False )-> Optional[int]:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ : Any = value
lowerCamelCase = R'''
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
lowerCamelCase = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , _lowercase , )
class _a ( _lowercase):
def __init__( self : Optional[int] , _SCREAMING_SNAKE_CASE : Dict )-> List[str]:
super().__init__(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
lowerCAmelCase__ : Any = UperNetHead(_SCREAMING_SNAKE_CASE , in_channels=self.backbone.channels )
lowerCAmelCase__ : Any = UperNetFCNHead(_SCREAMING_SNAKE_CASE ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length''' ) )
@replace_return_docstrings(output_type=_SCREAMING_SNAKE_CASE , config_class=_CONFIG_FOR_DOC )
def UpperCAmelCase__( self : int , _SCREAMING_SNAKE_CASE : Optional[torch.Tensor] = None , _SCREAMING_SNAKE_CASE : Optional[bool] = None , _SCREAMING_SNAKE_CASE : Optional[bool] = None , _SCREAMING_SNAKE_CASE : Optional[torch.Tensor] = None , _SCREAMING_SNAKE_CASE : Optional[bool] = None , )-> Union[tuple, SemanticSegmenterOutput]:
lowerCAmelCase__ : List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
lowerCAmelCase__ : List[str] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCAmelCase__ : Dict = output_attentions if output_attentions is not None else self.config.output_attentions
lowerCAmelCase__ : List[str] = self.backbone.forward_with_filtered_kwargs(
_SCREAMING_SNAKE_CASE , output_hidden_states=_SCREAMING_SNAKE_CASE , output_attentions=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Union[str, Any] = outputs.feature_maps
lowerCAmelCase__ : Tuple = self.decode_head(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[str] = nn.functional.interpolate(_SCREAMING_SNAKE_CASE , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Dict = None
if self.auxiliary_head is not None:
lowerCAmelCase__ : Optional[Any] = self.auxiliary_head(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[int] = nn.functional.interpolate(
_SCREAMING_SNAKE_CASE , size=pixel_values.shape[2:] , mode='''bilinear''' , align_corners=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Any = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('''The number of labels should be greater than one''' )
else:
# compute weighted loss
lowerCAmelCase__ : str = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
lowerCAmelCase__ : str = loss_fct(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[str] = loss_fct(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[Any] = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
lowerCAmelCase__ : str = (logits,) + outputs[1:]
else:
lowerCAmelCase__ : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=_SCREAMING_SNAKE_CASE , logits=_SCREAMING_SNAKE_CASE , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 131
|
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
lowerCamelCase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
lowerCamelCase = {
'''facebook/blenderbot_small-90M''': 512,
}
class _a ( _lowercase):
_a : Dict = VOCAB_FILES_NAMES
_a : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_a : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a : Dict = BlenderbotSmallTokenizer
def __init__( self : Tuple , _SCREAMING_SNAKE_CASE : int=None , _SCREAMING_SNAKE_CASE : Any=None , _SCREAMING_SNAKE_CASE : Tuple="<|endoftext|>" , _SCREAMING_SNAKE_CASE : Any="<|endoftext|>" , _SCREAMING_SNAKE_CASE : Union[str, Any]="<|endoftext|>" , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : List[Any]=True , **_SCREAMING_SNAKE_CASE : Optional[Any] , )-> Union[str, Any]:
super().__init__(
ByteLevelBPETokenizer(
vocab=_SCREAMING_SNAKE_CASE , merges=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE , trim_offsets=_SCREAMING_SNAKE_CASE , ) , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : List[str] = add_prefix_space
def UpperCAmelCase__( self : Tuple , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Any=None )-> Optional[int]:
lowerCAmelCase__ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase__( self : int , _SCREAMING_SNAKE_CASE : List[int] , _SCREAMING_SNAKE_CASE : Optional[List[int]] = None )-> List[int]:
lowerCAmelCase__ : List[str] = [self.sep_token_id]
lowerCAmelCase__ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 131
| 1
|
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE__ : List[str] = get_edges(__lowerCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = edges.pop()
chosen_vertices.add(__lowerCAmelCase )
chosen_vertices.add(__lowerCAmelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(__lowerCAmelCase )
return chosen_vertices
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : Tuple = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 56
|
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a :List[Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
a :str = json.load(f)
@require_torch
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self , _a ) -> Optional[int]:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(_a )
def _a ( self , _a ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = FSMTForConditionalGeneration.from_pretrained(_a ).to(_a )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _a ( self , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = f'''facebook/wmt19-{pair}'''
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer(_a )
SCREAMING_SNAKE_CASE__ : Any = self.get_model(_a )
SCREAMING_SNAKE_CASE__ : Tuple = bleu_data[pair]["""src"""]
SCREAMING_SNAKE_CASE__ : Any = bleu_data[pair]["""tgt"""]
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , return_tensors="""pt""" , truncation=_a , padding="""longest""" ).to(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(
_a , skip_special_tokens=_a , clean_up_tokenization_spaces=_a )
SCREAMING_SNAKE_CASE__ : Dict = calculate_bleu(_a , _a )
print(_a )
self.assertGreaterEqual(scores["""bleu"""] , _a )
| 56
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : List[Any] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] = [
'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
'PegasusXForConditionalGeneration',
'PegasusXModel',
'PegasusXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
lowerCamelCase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 2
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : str = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Dict = [
'RagModel',
'RagPreTrainedModel',
'RagSequenceForGeneration',
'RagTokenForGeneration',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Optional[int] = [
'TFRagModel',
'TFRagPreTrainedModel',
'TFRagSequenceForGeneration',
'TFRagTokenForGeneration',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
lowerCamelCase : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 2
| 1
|
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
'''processing_mctct''': ['''MCTCTProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : int = [
'''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MCTCTForCTC''',
'''MCTCTModel''',
'''MCTCTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
from .feature_extraction_mctct import MCTCTFeatureExtractor
from .processing_mctct import MCTCTProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel
else:
import sys
_lowerCamelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 363
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowerCamelCase : List[Any] = [
'''small''',
'''small-base''',
'''medium''',
'''medium-base''',
'''intermediate''',
'''intermediate-base''',
'''large''',
'''large-base''',
'''xlarge''',
'''xlarge-base''',
]
_lowerCamelCase : Optional[Any] = {
'''vocab_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
'''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
'''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
'''funnel-transformer/small-base''': (
'''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
'''funnel-transformer/medium-base''': (
'''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate''': (
'''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
),
'''funnel-transformer/intermediate-base''': (
'''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
'''funnel-transformer/large-base''': (
'''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
),
'''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
'''funnel-transformer/xlarge-base''': (
'''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
),
},
}
_lowerCamelCase : Any = {f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
_lowerCamelCase : Optional[Any] = {f"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}
class lowercase ( a ):
lowercase__ : Optional[int] = VOCAB_FILES_NAMES
lowercase__ : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_INIT_CONFIGURATION
lowercase__ : Union[str, Any] = FunnelTokenizer
lowercase__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : int = 2
def __init__( self : str , _UpperCamelCase : str=None , _UpperCamelCase : str=None , _UpperCamelCase : Union[str, Any]=True , _UpperCamelCase : str="<unk>" , _UpperCamelCase : Optional[Any]="<sep>" , _UpperCamelCase : Optional[int]="<pad>" , _UpperCamelCase : int="<cls>" , _UpperCamelCase : Dict="<mask>" , _UpperCamelCase : Union[str, Any]="<s>" , _UpperCamelCase : Optional[int]="</s>" , _UpperCamelCase : Dict=True , _UpperCamelCase : List[Any]=True , _UpperCamelCase : Any=None , _UpperCamelCase : Dict="##" , **_UpperCamelCase : Dict , ) -> Optional[int]:
'''simple docstring'''
super().__init__(
_UpperCamelCase , tokenizer_file=_UpperCamelCase , do_lower_case=_UpperCamelCase , unk_token=_UpperCamelCase , sep_token=_UpperCamelCase , pad_token=_UpperCamelCase , cls_token=_UpperCamelCase , mask_token=_UpperCamelCase , bos_token=_UpperCamelCase , eos_token=_UpperCamelCase , clean_text=_UpperCamelCase , tokenize_chinese_chars=_UpperCamelCase , strip_accents=_UpperCamelCase , wordpieces_prefix=_UpperCamelCase , **_UpperCamelCase , )
SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , _UpperCamelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , _UpperCamelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _UpperCamelCase ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE = getattr(_UpperCamelCase , normalizer_state.pop("type" ) )
SCREAMING_SNAKE_CASE = do_lower_case
SCREAMING_SNAKE_CASE = strip_accents
SCREAMING_SNAKE_CASE = tokenize_chinese_chars
SCREAMING_SNAKE_CASE = normalizer_class(**_UpperCamelCase )
SCREAMING_SNAKE_CASE = do_lower_case
def __snake_case( self : Union[str, Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Dict=None ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __snake_case( self : int , _UpperCamelCase : List[int] , _UpperCamelCase : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __snake_case( self : Optional[Any] , _UpperCamelCase : str , _UpperCamelCase : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._tokenizer.model.save(_UpperCamelCase , name=_UpperCamelCase )
return tuple(_UpperCamelCase )
| 206
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|
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
lowerCamelCase__ = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
lowerCamelCase__ = {
"""allenai/led-base-16384""": 1_6384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def lowerCAmelCase__ ( ):
"""simple docstring"""
__a = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
__a = bs[:]
__a = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_SCREAMING_SNAKE_CASE )
cs.append(2**8 + n )
n += 1
__a = [chr(_SCREAMING_SNAKE_CASE ) for n in cs]
return dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
__a = set()
__a = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__a = char
return pairs
class SCREAMING_SNAKE_CASE ( __A ):
__lowerCamelCase : Tuple =VOCAB_FILES_NAMES
__lowerCamelCase : List[str] =PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Union[str, Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Dict =['input_ids', 'attention_mask']
def __init__( self : Optional[Any] , __lowercase : Any , __lowercase : Optional[Any] , __lowercase : Optional[Any]="replace" , __lowercase : List[Any]="<s>" , __lowercase : Union[str, Any]="</s>" , __lowercase : Tuple="</s>" , __lowercase : List[str]="<s>" , __lowercase : Dict="<unk>" , __lowercase : str="<pad>" , __lowercase : Optional[int]="<mask>" , __lowercase : str=False , **__lowercase : int , ):
'''simple docstring'''
__a = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else bos_token
__a = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else eos_token
__a = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else sep_token
__a = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else cls_token
__a = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else unk_token
__a = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__a = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
super().__init__(
errors=__lowercase , bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , add_prefix_space=__lowercase , **__lowercase , )
with open(__lowercase , encoding="""utf-8""" ) as vocab_handle:
__a = json.load(__lowercase )
__a = {v: k for k, v in self.encoder.items()}
__a = errors # how to handle errors in decoding
__a = bytes_to_unicode()
__a = {v: k for k, v in self.byte_encoder.items()}
with open(__lowercase , encoding="""utf-8""" ) as merges_handle:
__a = merges_handle.read().split("""\n""" )[1:-1]
__a = [tuple(merge.split() ) for merge in bpe_merges]
__a = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
__a = {}
__a = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__a = re.compile(r"""\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
return len(self.encoder )
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCamelCase_ ( self : Optional[int] , __lowercase : int ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__a = tuple(__lowercase )
__a = get_pairs(__lowercase )
if not pairs:
return token
while True:
__a = min(__lowercase , key=lambda __lowercase : self.bpe_ranks.get(__lowercase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__a , __a = bigram
__a = []
__a = 0
while i < len(__lowercase ):
try:
__a = word.index(__lowercase , __lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__a = j
if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__a = tuple(__lowercase )
__a = new_word
if len(__lowercase ) == 1:
break
else:
__a = get_pairs(__lowercase )
__a = """ """.join(__lowercase )
__a = word
return word
def UpperCamelCase_ ( self : Union[str, Any] , __lowercase : List[str] ):
'''simple docstring'''
__a = []
for token in re.findall(self.pat , __lowercase ):
__a = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowercase ).split(""" """ ) )
return bpe_tokens
def UpperCamelCase_ ( self : int , __lowercase : int ):
'''simple docstring'''
return self.encoder.get(__lowercase , self.encoder.get(self.unk_token ) )
def UpperCamelCase_ ( self : str , __lowercase : List[Any] ):
'''simple docstring'''
return self.decoder.get(__lowercase )
def UpperCamelCase_ ( self : List[str] , __lowercase : int ):
'''simple docstring'''
__a = """""".join(__lowercase )
__a = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def UpperCamelCase_ ( self : Tuple , __lowercase : List[Any] , __lowercase : Optional[int] = None ):
'''simple docstring'''
if not os.path.isdir(__lowercase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
__a = os.path.join(
__lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__a = os.path.join(
__lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(__lowercase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowercase , ensure_ascii=__lowercase ) + """\n""" )
__a = 0
with open(__lowercase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowercase : kv[1] ):
if index != token_index:
logger.warning(
F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
""" Please check that the tokenizer is not corrupted!""" )
__a = token_index
writer.write(""" """.join(__lowercase ) + """\n""" )
index += 1
return vocab_file, merge_file
def UpperCamelCase_ ( self : List[Any] , __lowercase : Optional[Any] , __lowercase : Dict = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__a = [self.cls_token_id]
__a = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Optional[Any] , __lowercase : int , __lowercase : Optional[Any] = None , __lowercase : Optional[Any] = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase )
if token_ids_a is None:
return [1] + ([0] * len(__lowercase )) + [1]
return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]
def UpperCamelCase_ ( self : Optional[int] , __lowercase : Union[str, Any] , __lowercase : Any = None ):
'''simple docstring'''
__a = [self.sep_token_id]
__a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase_ ( self : Union[str, Any] , __lowercase : List[str] , __lowercase : Optional[int]=False , **__lowercase : str ):
'''simple docstring'''
__a = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
__a = """ """ + text
return (text, kwargs)
def UpperCamelCase_ ( self : Optional[Any] , __lowercase : List[Any] , __lowercase : Tuple = None , __lowercase : str = PaddingStrategy.DO_NOT_PAD , __lowercase : List[str] = None , __lowercase : Optional[Any] = None , ):
'''simple docstring'''
__a = super()._pad(
encoded_inputs=__lowercase , max_length=__lowercase , padding_strategy=__lowercase , pad_to_multiple_of=__lowercase , return_attention_mask=__lowercase , )
# Load from model defaults
if return_attention_mask is None:
__a = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__a = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__a = len(encoded_inputs["""global_attention_mask"""] ) != len(__lowercase )
if needs_to_be_padded:
__a = len(__lowercase ) - len(encoded_inputs["""global_attention_mask"""] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__a = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
__a = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return encoded_inputs
| 302
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = ['pixel_values']
def __init__(self , __lowercase = True , __lowercase = None , __lowercase = None , __lowercase = PILImageResampling.BILINEAR , __lowercase = True , __lowercase = 1 / 2_55 , __lowercase = True , __lowercase = None , __lowercase = None , **__lowercase , ):
super().__init__(**__lowercase )
__lowerCAmelCase = size if size is not None else {'''shortest_edge''': 3_84}
__lowerCAmelCase = get_size_dict(__lowercase , default_to_square=__lowercase )
__lowerCAmelCase = do_resize
__lowerCAmelCase = size
# Default value set here for backwards compatibility where the value in config is None
__lowerCAmelCase = crop_pct if crop_pct is not None else 2_24 / 2_56
__lowerCAmelCase = resample
__lowerCAmelCase = do_rescale
__lowerCAmelCase = rescale_factor
__lowerCAmelCase = do_normalize
__lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__lowerCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _snake_case (self , __lowercase , __lowercase , __lowercase , __lowercase = PILImageResampling.BICUBIC , __lowercase = None , **__lowercase , ):
__lowerCAmelCase = get_size_dict(__lowercase , default_to_square=__lowercase )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
__lowerCAmelCase = size['''shortest_edge''']
if shortest_edge < 3_84:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
__lowerCAmelCase = int(shortest_edge / crop_pct )
__lowerCAmelCase = get_resize_output_image_size(__lowercase , size=__lowercase , default_to_square=__lowercase )
__lowerCAmelCase = resize(image=__lowercase , size=__lowercase , resample=__lowercase , data_format=__lowercase , **__lowercase )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=__lowercase , size=(shortest_edge, shortest_edge) , data_format=__lowercase , **__lowercase )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
__lowercase , size=(shortest_edge, shortest_edge) , resample=__lowercase , data_format=__lowercase , **__lowercase )
def _snake_case (self , __lowercase , __lowercase , __lowercase = None , **__lowercase , ):
return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase )
def _snake_case (self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase , ):
return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase )
def _snake_case (self , __lowercase , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , ):
__lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
__lowerCAmelCase = crop_pct if crop_pct is not None else self.crop_pct
__lowerCAmelCase = resample if resample is not None else self.resample
__lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
__lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
__lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
__lowerCAmelCase = image_std if image_std is not None else self.image_std
__lowerCAmelCase = size if size is not None else self.size
__lowerCAmelCase = get_size_dict(__lowercase , default_to_square=__lowercase )
__lowerCAmelCase = make_list_of_images(__lowercase )
if not valid_images(__lowercase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
raise ValueError('''crop_pct must be specified if size < 384.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__lowerCAmelCase = [to_numpy_array(__lowercase ) for image in images]
if do_resize:
__lowerCAmelCase = [self.resize(image=__lowercase , size=__lowercase , crop_pct=__lowercase , resample=__lowercase ) for image in images]
if do_rescale:
__lowerCAmelCase = [self.rescale(image=__lowercase , scale=__lowercase ) for image in images]
if do_normalize:
__lowerCAmelCase = [self.normalize(image=__lowercase , mean=__lowercase , std=__lowercase ) for image in images]
__lowerCAmelCase = [to_channel_dimension_format(__lowercase , __lowercase ) for image in images]
__lowerCAmelCase = {'''pixel_values''': images}
return BatchFeature(data=__lowercase , tensor_type=__lowercase )
| 174
| 0
|
from collections import deque
class __snake_case :
def __init__( self : Tuple , _snake_case : str , _snake_case : int , _snake_case : int):
"""simple docstring"""
UpperCAmelCase_ = process_name # process name
UpperCAmelCase_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
UpperCAmelCase_ = arrival_time
UpperCAmelCase_ = burst_time # remaining burst time
UpperCAmelCase_ = 0 # total time of the process wait in ready queue
UpperCAmelCase_ = 0 # time from arrival time to completion time
class __snake_case :
def __init__( self : Tuple , _snake_case : int , _snake_case : list[int] , _snake_case : deque[Process] , _snake_case : int , ):
"""simple docstring"""
UpperCAmelCase_ = number_of_queues
# time slice of queues that round robin algorithm applied
UpperCAmelCase_ = time_slices
# unfinished process is in this ready_queue
UpperCAmelCase_ = queue
# current time
UpperCAmelCase_ = current_time
# finished process is in this sequence queue
UpperCAmelCase_ = deque()
def lowerCamelCase ( self : Dict):
"""simple docstring"""
UpperCAmelCase_ = []
for i in range(len(self.finish_queue)):
sequence.append(self.finish_queue[i].process_name)
return sequence
def lowerCamelCase ( self : Optional[Any] , _snake_case : list[Process]):
"""simple docstring"""
UpperCAmelCase_ = []
for i in range(len(_snake_case)):
waiting_times.append(queue[i].waiting_time)
return waiting_times
def lowerCamelCase ( self : Dict , _snake_case : list[Process]):
"""simple docstring"""
UpperCAmelCase_ = []
for i in range(len(_snake_case)):
turnaround_times.append(queue[i].turnaround_time)
return turnaround_times
def lowerCamelCase ( self : Optional[Any] , _snake_case : list[Process]):
"""simple docstring"""
UpperCAmelCase_ = []
for i in range(len(_snake_case)):
completion_times.append(queue[i].stop_time)
return completion_times
def lowerCamelCase ( self : Dict , _snake_case : deque[Process]):
"""simple docstring"""
return [q.burst_time for q in queue]
def lowerCamelCase ( self : Tuple , _snake_case : Process):
"""simple docstring"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def lowerCamelCase ( self : List[str] , _snake_case : deque[Process]):
"""simple docstring"""
UpperCAmelCase_ = deque() # sequence deque of finished process
while len(_snake_case) != 0:
UpperCAmelCase_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(_snake_case)
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
UpperCAmelCase_ = 0
# set the process's turnaround time because it is finished
UpperCAmelCase_ = self.current_time - cp.arrival_time
# set the completion time
UpperCAmelCase_ = self.current_time
# add the process to queue that has finished queue
finished.append(_snake_case)
self.finish_queue.extend(_snake_case) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def lowerCamelCase ( self : Dict , _snake_case : deque[Process] , _snake_case : int):
"""simple docstring"""
UpperCAmelCase_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(_snake_case)):
UpperCAmelCase_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(_snake_case)
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
UpperCAmelCase_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(_snake_case)
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
UpperCAmelCase_ = 0
# set the finish time
UpperCAmelCase_ = self.current_time
# update the process' turnaround time because it is finished
UpperCAmelCase_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(_snake_case)
self.finish_queue.extend(_snake_case) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def lowerCamelCase ( self : Optional[Any]):
"""simple docstring"""
for i in range(self.number_of_queues - 1):
UpperCAmelCase_ , UpperCAmelCase_ = self.round_robin(
self.ready_queue , self.time_slices[i])
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue)
return self.finish_queue
if __name__ == "__main__":
import doctest
snake_case_ : Union[str, Any] = Process("P1", 0, 53)
snake_case_ : List[Any] = Process("P2", 0, 17)
snake_case_ : Tuple = Process("P3", 0, 68)
snake_case_ : Optional[Any] = Process("P4", 0, 24)
snake_case_ : Dict = 3
snake_case_ : Optional[Any] = [17, 25]
snake_case_ : List[Any] = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
snake_case_ : int = Process("P1", 0, 53)
snake_case_ : Tuple = Process("P2", 0, 17)
snake_case_ : Union[str, Any] = Process("P3", 0, 68)
snake_case_ : Optional[Any] = Process("P4", 0, 24)
snake_case_ : str = 3
snake_case_ : str = [17, 25]
snake_case_ : List[str] = deque([Pa, Pa, Pa, Pa])
snake_case_ : int = MLFQ(number_of_queues, time_slices, queue, 0)
snake_case_ : Optional[Any] = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 364
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class __snake_case ( unittest.TestCase ):
@slow
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
UpperCAmelCase_ = XLMRobertaModel.from_pretrained('''xlm-roberta-base''')
UpperCAmelCase_ = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
UpperCAmelCase_ = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = torch.tensor(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase_ = model(_snake_case)['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _snake_case)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _snake_case , atol=1e-3))
@slow
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
UpperCAmelCase_ = XLMRobertaModel.from_pretrained('''xlm-roberta-large''')
UpperCAmelCase_ = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
UpperCAmelCase_ = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = torch.tensor(
[[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
UpperCAmelCase_ = model(_snake_case)['''last_hidden_state'''].detach()
self.assertEqual(output.shape , _snake_case)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , _snake_case , atol=1e-3))
| 7
| 0
|
from numpy import exp, pi, sqrt
def lowerCamelCase_ ( _a : str , _a : int = 0.0 , _a : Dict = 1.0 ):
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 345
|
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__UpperCamelCase : int = 299792458
# Symbols
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Optional[int] = symbols("""ct x y z""")
def a_ ( _A ) -> float:
"""simple docstring"""
if velocity > c:
raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('Speed must be greater than or equal to 1!' )
return velocity / c
def a_ ( _A ) -> float:
"""simple docstring"""
return 1 / sqrt(1 - beta(_A ) ** 2 )
def a_ ( _A ) -> np.ndarray:
"""simple docstring"""
return np.array(
[
[gamma(_A ), -gamma(_A ) * beta(_A ), 0, 0],
[-gamma(_A ) * beta(_A ), gamma(_A ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def a_ ( _A , _A = None ) -> np.ndarray:
"""simple docstring"""
# Ensure event is not empty
if event is None:
snake_case__ = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(_A ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
__UpperCamelCase : List[Any] = transform(29979245)
print("""Example of four vector: """)
print(f'''ct\' = {four_vector[0]}''')
print(f'''x\' = {four_vector[1]}''')
print(f'''y\' = {four_vector[2]}''')
print(f'''z\' = {four_vector[3]}''')
# Substitute symbols with numerical values
__UpperCamelCase : List[Any] = {ct: c, x: 1, y: 1, z: 1}
__UpperCamelCase : Tuple = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f'''\n{numerical_vector}''')
| 307
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case__ : Any = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Union[str, Any] = ['''VivitImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Dict = [
'''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VivitModel''',
'''VivitPreTrainedModel''',
'''VivitForVideoClassification''',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
snake_case__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 359
|
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_( a__ ):
__UpperCamelCase = (DDPMScheduler,)
def lowerCamelCase__ ( self : List[Any] , **UpperCamelCase_ : Union[str, Any] ):
lowerCAmelCase : Optional[Any] = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**UpperCamelCase_ )
return config
def lowerCamelCase__ ( self : Optional[int] ):
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCamelCase_ )
def lowerCamelCase__ ( self : Tuple ):
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=UpperCamelCase_ , beta_end=UpperCamelCase_ )
def lowerCamelCase__ ( self : str ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCamelCase_ )
def lowerCamelCase__ ( self : Optional[Any] ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=UpperCamelCase_ )
def lowerCamelCase__ ( self : Optional[int] ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCamelCase_ )
def lowerCamelCase__ ( self : Any ):
self.check_over_configs(thresholding=UpperCamelCase_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=UpperCamelCase_ , prediction_type=UpperCamelCase_ , sample_max_value=UpperCamelCase_ , )
def lowerCamelCase__ ( self : Tuple ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCamelCase_ )
def lowerCamelCase__ ( self : str ):
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=UpperCamelCase_ )
def lowerCamelCase__ ( self : int ):
lowerCAmelCase : str = self.scheduler_classes[0]
lowerCAmelCase : Dict = self.get_scheduler_config()
lowerCAmelCase : Dict = scheduler_class(**UpperCamelCase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.02 ) ) < 1E-5
def lowerCamelCase__ ( self : Tuple ):
lowerCAmelCase : List[Any] = self.scheduler_classes[0]
lowerCAmelCase : List[Any] = self.get_scheduler_config()
lowerCAmelCase : List[str] = scheduler_class(**UpperCamelCase_ )
lowerCAmelCase : Union[str, Any] = len(UpperCamelCase_ )
lowerCAmelCase : List[str] = self.dummy_model()
lowerCAmelCase : Union[str, Any] = self.dummy_sample_deter
lowerCAmelCase : List[Any] = torch.manual_seed(0 )
for t in reversed(range(UpperCamelCase_ ) ):
# 1. predict noise residual
lowerCAmelCase : Optional[int] = model(UpperCamelCase_ , UpperCamelCase_ )
# 2. predict previous mean of sample x_t-1
lowerCAmelCase : Optional[Any] = scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowerCAmelCase : Union[str, Any] = pred_prev_sample
lowerCAmelCase : str = torch.sum(torch.abs(UpperCamelCase_ ) )
lowerCAmelCase : int = torch.mean(torch.abs(UpperCamelCase_ ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def lowerCamelCase__ ( self : Any ):
lowerCAmelCase : Optional[int] = self.scheduler_classes[0]
lowerCAmelCase : Any = self.get_scheduler_config(prediction_type='''v_prediction''' )
lowerCAmelCase : Tuple = scheduler_class(**UpperCamelCase_ )
lowerCAmelCase : Dict = len(UpperCamelCase_ )
lowerCAmelCase : Any = self.dummy_model()
lowerCAmelCase : Any = self.dummy_sample_deter
lowerCAmelCase : List[Any] = torch.manual_seed(0 )
for t in reversed(range(UpperCamelCase_ ) ):
# 1. predict noise residual
lowerCAmelCase : str = model(UpperCamelCase_ , UpperCamelCase_ )
# 2. predict previous mean of sample x_t-1
lowerCAmelCase : List[Any] = scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
lowerCAmelCase : List[Any] = pred_prev_sample
lowerCAmelCase : List[str] = torch.sum(torch.abs(UpperCamelCase_ ) )
lowerCAmelCase : Dict = torch.mean(torch.abs(UpperCamelCase_ ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def lowerCamelCase__ ( self : Any ):
lowerCAmelCase : Dict = self.scheduler_classes[0]
lowerCAmelCase : Tuple = self.get_scheduler_config()
lowerCAmelCase : int = scheduler_class(**UpperCamelCase_ )
lowerCAmelCase : List[Any] = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=UpperCamelCase_ )
lowerCAmelCase : Dict = scheduler.timesteps
for i, timestep in enumerate(UpperCamelCase_ ):
if i == len(UpperCamelCase_ ) - 1:
lowerCAmelCase : List[Any] = -1
else:
lowerCAmelCase : Union[str, Any] = timesteps[i + 1]
lowerCAmelCase : Any = scheduler.previous_timestep(UpperCamelCase_ )
lowerCAmelCase : Dict = prev_t.item()
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
def lowerCamelCase__ ( self : Dict ):
lowerCAmelCase : Union[str, Any] = self.scheduler_classes[0]
lowerCAmelCase : List[Any] = self.get_scheduler_config()
lowerCAmelCase : Tuple = scheduler_class(**UpperCamelCase_ )
lowerCAmelCase : int = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(UpperCamelCase_ , msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=UpperCamelCase_ )
def lowerCamelCase__ ( self : Tuple ):
lowerCAmelCase : Any = self.scheduler_classes[0]
lowerCAmelCase : Optional[int] = self.get_scheduler_config()
lowerCAmelCase : str = scheduler_class(**UpperCamelCase_ )
lowerCAmelCase : List[str] = [1_0_0, 8_7, 5_0, 1, 0]
lowerCAmelCase : int = len(UpperCamelCase_ )
with self.assertRaises(UpperCamelCase_ , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=UpperCamelCase_ , timesteps=UpperCamelCase_ )
def lowerCamelCase__ ( self : Optional[Any] ):
lowerCAmelCase : List[Any] = self.scheduler_classes[0]
lowerCAmelCase : Tuple = self.get_scheduler_config()
lowerCAmelCase : Dict = scheduler_class(**UpperCamelCase_ )
lowerCAmelCase : Optional[Any] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCamelCase_ , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=UpperCamelCase_ )
| 314
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a : Optional[int] = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase ) -> List[int]:
'''simple docstring'''
if isinstance(__UpperCAmelCase, np.ndarray ):
return list(tensor.shape )
snake_case_ = tf.shape(__UpperCAmelCase )
if tensor.shape == tf.TensorShape(__UpperCAmelCase ):
return dynamic
snake_case_ = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(__UpperCAmelCase )]
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None ) -> tf.Tensor:
'''simple docstring'''
return tf.nn.softmax(logits=logits + 1e-9, axis=__UpperCAmelCase, name=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=1e-5, __UpperCAmelCase=-1 ) -> Any:
'''simple docstring'''
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__UpperCAmelCase, __UpperCAmelCase ):
raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )
# Get mean and variance on the axis to be normalized
snake_case_ ,snake_case_ = tf.nn.moments(__UpperCAmelCase, axes=[axis], keepdims=__UpperCAmelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
snake_case_ = [1] * inputs.shape.rank
snake_case_ = shape_list(__UpperCAmelCase )[axis]
snake_case_ = tf.reshape(__UpperCAmelCase, __UpperCAmelCase )
snake_case_ = tf.reshape(__UpperCAmelCase, __UpperCAmelCase )
# Compute layer normalization using the batch_normalization
# function.
snake_case_ = tf.nn.batch_normalization(
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, offset=__UpperCAmelCase, scale=__UpperCAmelCase, variance_epsilon=__UpperCAmelCase, )
return outputs
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase=0, __UpperCAmelCase=-1 ) -> Optional[int]:
'''simple docstring'''
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
snake_case_ = tf.shape(__UpperCAmelCase )
snake_case_ = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
snake_case_ = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]], axis=0 )
return tf.reshape(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> tf.Tensor:
'''simple docstring'''
if not isinstance(__UpperCAmelCase, tf.Tensor ):
snake_case_ = tf.convert_to_tensor(__UpperCAmelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
snake_case_ = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
snake_case_ = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
snake_case_ = (
tf.cast(1, encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = "input_ids" ) -> None:
'''simple docstring'''
tf.debugging.assert_less(
__UpperCAmelCase, tf.cast(__UpperCAmelCase, dtype=tensor.dtype ), message=(
F"The maximum value of {tensor_name} ({tf.math.reduce_max(__UpperCAmelCase )}) must be smaller than the embedding "
F"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
), )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = 6_4512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
snake_case_ = [x for x in data if len(__UpperCAmelCase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'''The following attributes cannot be saved to HDF5 file because '''
F"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
F"bytes: {bad_attributes}" )
snake_case_ = np.asarray(__UpperCAmelCase )
snake_case_ = 1
snake_case_ = np.array_split(__UpperCAmelCase, __UpperCAmelCase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
snake_case_ = np.array_split(__UpperCAmelCase, __UpperCAmelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(__UpperCAmelCase ):
snake_case_ = chunk_data
else:
snake_case_ = data
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
if name in group.attrs:
snake_case_ = [n.decode('''utf8''' ) if hasattr(__UpperCAmelCase, '''decode''' ) else n for n in group.attrs[name]]
else:
snake_case_ = []
snake_case_ = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(__UpperCAmelCase, '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def __magic_name__ ( __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
def _expand_single_ad_tensor(__UpperCAmelCase ):
if isinstance(__UpperCAmelCase, tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(__UpperCAmelCase, axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor, __UpperCAmelCase )
| 56
|
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 56
| 1
|
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
'''simple docstring'''
def __init__(self ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = {}
def _a (self , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = {}
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
if nodea not in self.connections:
self.add_node(_lowerCamelCase )
if nodea not in self.connections:
self.add_node(_lowerCamelCase )
UpperCAmelCase__ : Union[str, Any] = probability
def _a (self ):
"""simple docstring"""
return list(self.connections )
def _a (self , _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Any = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> dict[str, int]:
UpperCAmelCase__ : Any = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = Counter(graph.get_nodes() )
UpperCAmelCase__ : str = start
for _ in range(lowerCAmelCase ):
UpperCAmelCase__ : List[str] = graph.transition(lowerCAmelCase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 166
|
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def _a (self ):
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"""content""": datasets.Value("""string""" )} ) , supervised_keys=_lowerCamelCase , )
def _a (self , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_dummy_examples()} )]
def _a (self , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_lowerCamelCase )
class lowerCamelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def _a (self ):
"""simple docstring"""
return datasets.DatasetInfo(
features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) , supervised_keys=_lowerCamelCase , )
def _a (self , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_nested_examples()} )
]
def _a (self , _lowerCamelCase , _lowerCamelCase ):
"""simple docstring"""
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_lowerCamelCase )
def a__ ( ) -> Tuple:
return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
def a__ ( ) -> List[str]:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
@require_beam
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase__ : Dict = DummyBeamDataset(cache_dir=_lowerCamelCase , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_lowerCamelCase , builder.name , """default""" , """0.0.0""" , F"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
UpperCAmelCase__ : List[Any] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , _lowerCamelCase )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , _lowerCamelCase )
self.assertDictEqual(dset["""train"""][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_lowerCamelCase , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def _a (self ):
"""simple docstring"""
import apache_beam as beam
UpperCAmelCase__ : Optional[int] = beam.io.parquetio.WriteToParquet
UpperCAmelCase__ : int = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase__ : Any = DummyBeamDataset(cache_dir=_lowerCamelCase , beam_runner="""DirectRunner""" )
with patch("""apache_beam.io.parquetio.WriteToParquet""" ) as write_parquet_mock:
UpperCAmelCase__ : int = partial(_lowerCamelCase , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
_lowerCamelCase , builder.name , """default""" , """0.0.0""" , F"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
_lowerCamelCase , builder.name , """default""" , """0.0.0""" , F"""{builder.name}-train-00000-of-00002.arrow""" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
UpperCAmelCase__ : Union[str, Any] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , _lowerCamelCase )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , _lowerCamelCase )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["""train"""]["""content"""] ) , sorted(["""foo""", """bar""", """foobar"""] ) )
self.assertTrue(
os.path.exists(os.path.join(_lowerCamelCase , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def _a (self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase__ : Union[str, Any] = DummyBeamDataset(cache_dir=_lowerCamelCase )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase__ : Optional[Any] = NestedBeamDataset(cache_dir=_lowerCamelCase , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_lowerCamelCase , builder.name , """default""" , """0.0.0""" , F"""{builder.name}-train.arrow""" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) )
UpperCAmelCase__ : str = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , _lowerCamelCase )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , _lowerCamelCase )
self.assertDictEqual(dset["""train"""][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_lowerCamelCase , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
| 166
| 1
|
"""simple docstring"""
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def UpperCamelCase_ ( lowerCAmelCase__ : Any ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase_ : Optional[int] = VideoMAEConfig()
set_architecture_configs(lowerCamelCase__ , lowerCamelCase__ )
if "finetuned" not in model_name:
lowerCAmelCase_ : Dict = False
if "finetuned" in model_name:
lowerCAmelCase_ : List[Any] = """huggingface/label-files"""
if "kinetics" in model_name:
lowerCAmelCase_ : Dict = 400
lowerCAmelCase_ : List[str] = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
lowerCAmelCase_ : Tuple = 174
lowerCAmelCase_ : str = """something-something-v2-id2label.json"""
else:
raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.' )
lowerCAmelCase_ : Dict = json.load(open(hf_hub_download(lowerCamelCase__ , lowerCamelCase__ , repo_type='dataset' ) , 'r' ) )
lowerCAmelCase_ : List[str] = {int(lowerCamelCase__ ): v for k, v in idalabel.items()}
lowerCAmelCase_ : Optional[Any] = idalabel
lowerCAmelCase_ : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def UpperCamelCase_ ( lowerCAmelCase__ : Any , lowerCAmelCase__ : List[str] ) -> List[Any]:
"""simple docstring"""
if "small" in model_name:
lowerCAmelCase_ : int = 384
lowerCAmelCase_ : Union[str, Any] = 1536
lowerCAmelCase_ : List[str] = 12
lowerCAmelCase_ : Optional[int] = 16
lowerCAmelCase_ : Any = 12
lowerCAmelCase_ : int = 3
lowerCAmelCase_ : Optional[Any] = 192
lowerCAmelCase_ : Union[str, Any] = 768
elif "large" in model_name:
lowerCAmelCase_ : List[Any] = 1024
lowerCAmelCase_ : Optional[Any] = 4096
lowerCAmelCase_ : Optional[Any] = 24
lowerCAmelCase_ : List[str] = 16
lowerCAmelCase_ : Any = 12
lowerCAmelCase_ : str = 8
lowerCAmelCase_ : str = 512
lowerCAmelCase_ : int = 2048
elif "huge" in model_name:
lowerCAmelCase_ : Optional[Any] = 1280
lowerCAmelCase_ : str = 5120
lowerCAmelCase_ : str = 32
lowerCAmelCase_ : int = 16
lowerCAmelCase_ : Any = 12
lowerCAmelCase_ : Union[str, Any] = 8
lowerCAmelCase_ : Dict = 640
lowerCAmelCase_ : Optional[Any] = 2560
elif "base" not in model_name:
raise ValueError('Model name should include either \"small\", \"base\", \"large\", or \"huge\"' )
def UpperCamelCase_ ( lowerCAmelCase__ : str ) -> Optional[int]:
"""simple docstring"""
if "encoder." in name:
lowerCAmelCase_ : List[Any] = name.replace('encoder.' , '' )
if "cls_token" in name:
lowerCAmelCase_ : List[str] = name.replace('cls_token' , 'videomae.embeddings.cls_token' )
if "decoder_pos_embed" in name:
lowerCAmelCase_ : Tuple = name.replace('decoder_pos_embed' , 'decoder.decoder_pos_embed' )
if "pos_embed" in name and "decoder" not in name:
lowerCAmelCase_ : int = name.replace('pos_embed' , 'videomae.embeddings.position_embeddings' )
if "patch_embed.proj" in name:
lowerCAmelCase_ : Optional[Any] = name.replace('patch_embed.proj' , 'videomae.embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
lowerCAmelCase_ : Dict = name.replace('patch_embed.norm' , 'videomae.embeddings.norm' )
if "decoder.blocks" in name:
lowerCAmelCase_ : List[str] = name.replace('decoder.blocks' , 'decoder.decoder_layers' )
if "blocks" in name:
lowerCAmelCase_ : List[str] = name.replace('blocks' , 'videomae.encoder.layer' )
if "attn.proj" in name:
lowerCAmelCase_ : str = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "bias" not in name:
lowerCAmelCase_ : str = name.replace('attn' , 'attention.self' )
if "attn" in name:
lowerCAmelCase_ : Union[str, Any] = name.replace('attn' , 'attention.attention' )
if "norm1" in name:
lowerCAmelCase_ : Any = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCAmelCase_ : List[str] = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCAmelCase_ : Dict = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase_ : List[str] = name.replace('mlp.fc2' , 'output.dense' )
if "decoder_embed" in name:
lowerCAmelCase_ : Optional[Any] = name.replace('decoder_embed' , 'decoder.decoder_embed' )
if "decoder_norm" in name:
lowerCAmelCase_ : Tuple = name.replace('decoder_norm' , 'decoder.decoder_norm' )
if "decoder_pred" in name:
lowerCAmelCase_ : Tuple = name.replace('decoder_pred' , 'decoder.decoder_pred' )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
lowerCAmelCase_ : Dict = name.replace('norm.weight' , 'videomae.layernorm.weight' )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
lowerCAmelCase_ : List[str] = name.replace('norm.bias' , 'videomae.layernorm.bias' )
if "head" in name and "decoder" not in name:
lowerCAmelCase_ : Optional[Any] = name.replace('head' , 'classifier' )
return name
def UpperCamelCase_ ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str ) -> Optional[Any]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCAmelCase_ : str = orig_state_dict.pop(lowerCamelCase__ )
if key.startswith('encoder.' ):
lowerCAmelCase_ : Tuple = key.replace('encoder.' , '' )
if "qkv" in key:
lowerCAmelCase_ : Optional[int] = key.split('.' )
if key.startswith('decoder.blocks' ):
lowerCAmelCase_ : Union[str, Any] = config.decoder_hidden_size
lowerCAmelCase_ : Any = int(key_split[2] )
lowerCAmelCase_ : int = """decoder.decoder_layers."""
if "weight" in key:
lowerCAmelCase_ : Optional[Any] = val[:dim, :]
lowerCAmelCase_ : Any = val[dim : dim * 2, :]
lowerCAmelCase_ : Dict = val[-dim:, :]
else:
lowerCAmelCase_ : List[Any] = config.hidden_size
lowerCAmelCase_ : List[Any] = int(key_split[1] )
lowerCAmelCase_ : int = """videomae.encoder.layer."""
if "weight" in key:
lowerCAmelCase_ : Any = val[:dim, :]
lowerCAmelCase_ : Union[str, Any] = val[dim : dim * 2, :]
lowerCAmelCase_ : List[str] = val[-dim:, :]
else:
lowerCAmelCase_ : Union[str, Any] = val
return orig_state_dict
def UpperCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : List[Any] = hf_hub_download(
repo_id='hf-internal-testing/spaghetti-video' , filename='eating_spaghetti.npy' , repo_type='dataset' )
lowerCAmelCase_ : Optional[Any] = np.load(lowerCamelCase__ )
return list(lowerCamelCase__ )
def UpperCamelCase_ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[str] ) -> Dict:
"""simple docstring"""
lowerCAmelCase_ : Any = get_videomae_config(lowerCamelCase__ )
if "finetuned" in model_name:
lowerCAmelCase_ : List[str] = VideoMAEForVideoClassification(lowerCamelCase__ )
else:
lowerCAmelCase_ : Optional[Any] = VideoMAEForPreTraining(lowerCamelCase__ )
# download original checkpoint, hosted on Google Drive
lowerCAmelCase_ : Optional[Any] = """pytorch_model.bin"""
gdown.cached_download(lowerCamelCase__ , lowerCamelCase__ , quiet=lowerCamelCase__ )
lowerCAmelCase_ : Any = torch.load(lowerCamelCase__ , map_location='cpu' )
if "model" in files:
lowerCAmelCase_ : Any = files["""model"""]
else:
lowerCAmelCase_ : Dict = files["""module"""]
lowerCAmelCase_ : Any = convert_state_dict(lowerCamelCase__ , lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__ )
model.eval()
# verify model on basic input
lowerCAmelCase_ : int = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
lowerCAmelCase_ : Union[str, Any] = prepare_video()
lowerCAmelCase_ : str = image_processor(lowerCamelCase__ , return_tensors='pt' )
if "finetuned" not in model_name:
lowerCAmelCase_ : List[str] = hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos' , filename='bool_masked_pos.pt' )
lowerCAmelCase_ : Optional[Any] = torch.load(lowerCamelCase__ )
lowerCAmelCase_ : Dict = model(**lowerCamelCase__ )
lowerCAmelCase_ : List[Any] = outputs.logits
lowerCAmelCase_ : Any = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
lowerCAmelCase_ : str = torch.Size([1, 400] )
lowerCAmelCase_ : Optional[Any] = torch.tensor([-0.9291, -0.4061, -0.9307] )
elif model_name == "videomae-small-finetuned-ssv2":
lowerCAmelCase_ : str = torch.Size([1, 174] )
lowerCAmelCase_ : Union[str, Any] = torch.tensor([0.2671, -0.4689, -0.8235] )
elif model_name == "videomae-base":
lowerCAmelCase_ : Tuple = torch.Size([1, 1408, 1536] )
lowerCAmelCase_ : List[str] = torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]] )
elif model_name == "videomae-base-short":
lowerCAmelCase_ : Dict = torch.Size([1, 1408, 1536] )
lowerCAmelCase_ : List[str] = torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] )
# we verified the loss both for normalized and unnormalized targets for this one
lowerCAmelCase_ : List[Any] = torch.tensor([0.5142] ) if config.norm_pix_loss else torch.tensor([0.6469] )
elif model_name == "videomae-large":
lowerCAmelCase_ : str = torch.Size([1, 1408, 1536] )
lowerCAmelCase_ : Dict = torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]] )
elif model_name == "videomae-large-finetuned-kinetics":
lowerCAmelCase_ : int = torch.Size([1, 400] )
lowerCAmelCase_ : Optional[Any] = torch.tensor([0.0771, 0.0011, -0.3625] )
elif model_name == "videomae-huge-finetuned-kinetics":
lowerCAmelCase_ : Union[str, Any] = torch.Size([1, 400] )
lowerCAmelCase_ : Optional[int] = torch.tensor([0.2433, 0.1632, -0.4894] )
elif model_name == "videomae-base-short-finetuned-kinetics":
lowerCAmelCase_ : List[Any] = torch.Size([1, 400] )
lowerCAmelCase_ : Optional[Any] = torch.tensor([0.6588, 0.0990, -0.2493] )
elif model_name == "videomae-base-finetuned-kinetics":
lowerCAmelCase_ : Union[str, Any] = torch.Size([1, 400] )
lowerCAmelCase_ : Tuple = torch.tensor([0.3669, -0.0688, -0.2421] )
elif model_name == "videomae-base-short-ssv2":
lowerCAmelCase_ : Optional[Any] = torch.Size([1, 1408, 1536] )
lowerCAmelCase_ : List[Any] = torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
lowerCAmelCase_ : Any = torch.Size([1, 174] )
lowerCAmelCase_ : Any = torch.tensor([-0.0537, -0.1539, -0.3266] )
elif model_name == "videomae-base-ssv2":
lowerCAmelCase_ : Dict = torch.Size([1, 1408, 1536] )
lowerCAmelCase_ : Dict = torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]] )
elif model_name == "videomae-base-finetuned-ssv2":
lowerCAmelCase_ : Any = torch.Size([1, 174] )
lowerCAmelCase_ : str = torch.tensor([0.1961, -0.8337, -0.6389] )
else:
raise ValueError(f"Model name not supported. Should be one of {model_names}" )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , lowerCamelCase__ , atol=1e-4 )
else:
print('Logits:' , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , lowerCamelCase__ , atol=1e-4 )
print('Logits ok!' )
# verify loss, if applicable
if model_name == "videomae-base-short":
lowerCAmelCase_ : Optional[int] = outputs.loss
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1e-4 )
print('Loss ok!' )
if pytorch_dump_folder_path is not None:
print(f"Saving model and image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(lowerCamelCase__ )
model.save_pretrained(lowerCamelCase__ )
if push_to_hub:
print('Pushing to the hub...' )
model.push_to_hub(lowerCamelCase__ , organization='nielsr' )
if __name__ == "__main__":
lowercase__ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
lowercase__ : Union[str, Any] = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 224
|
'''simple docstring'''
import argparse
import struct
import unittest
class _lowerCAmelCase :
def __init__(self , lowercase ):
A_ : List[str] = data
# Initialize hash values
A_ : Tuple = [
0X6A09_E667,
0XBB67_AE85,
0X3C6E_F372,
0XA54F_F53A,
0X510E_527F,
0X9B05_688C,
0X1F83_D9AB,
0X5BE0_CD19,
]
# Initialize round constants
A_ : List[Any] = [
0X428A_2F98,
0X7137_4491,
0XB5C0_FBCF,
0XE9B5_DBA5,
0X3956_C25B,
0X59F1_11F1,
0X923F_82A4,
0XAB1C_5ED5,
0XD807_AA98,
0X1283_5B01,
0X2431_85BE,
0X550C_7DC3,
0X72BE_5D74,
0X80DE_B1FE,
0X9BDC_06A7,
0XC19B_F174,
0XE49B_69C1,
0XEFBE_4786,
0X0FC1_9DC6,
0X240C_A1CC,
0X2DE9_2C6F,
0X4A74_84AA,
0X5CB0_A9DC,
0X76F9_88DA,
0X983E_5152,
0XA831_C66D,
0XB003_27C8,
0XBF59_7FC7,
0XC6E0_0BF3,
0XD5A7_9147,
0X06CA_6351,
0X1429_2967,
0X27B7_0A85,
0X2E1B_2138,
0X4D2C_6DFC,
0X5338_0D13,
0X650A_7354,
0X766A_0ABB,
0X81C2_C92E,
0X9272_2C85,
0XA2BF_E8A1,
0XA81A_664B,
0XC24B_8B70,
0XC76C_51A3,
0XD192_E819,
0XD699_0624,
0XF40E_3585,
0X106A_A070,
0X19A4_C116,
0X1E37_6C08,
0X2748_774C,
0X34B0_BCB5,
0X391C_0CB3,
0X4ED8_AA4A,
0X5B9C_CA4F,
0X682E_6FF3,
0X748F_82EE,
0X78A5_636F,
0X84C8_7814,
0X8CC7_0208,
0X90BE_FFFA,
0XA450_6CEB,
0XBEF9_A3F7,
0XC671_78F2,
]
A_ : Tuple = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def _a (lowercase ):
A_ : Any = b"""\x80""" + (b"""\x00""" * (63 - (len(lowercase ) + 8) % 64))
A_ : Optional[Any] = struct.pack(""">Q""" , (len(lowercase ) * 8) )
return data + padding + big_endian_integer
def _a (self ):
# Convert into blocks of 64 bytes
A_ : str = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
A_ : Tuple = list(struct.unpack(""">16L""" , lowercase ) )
# add 48 0-ed integers
words += [0] * 48
A_, A_, A_, A_, A_, A_, A_, A_ : Dict = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
A_ : Optional[Any] = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
A_ : Tuple = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
A_ : Any = (
words[index - 16] + sa + words[index - 7] + sa
) % 0X1_0000_0000
# Compression
A_ : Union[str, Any] = self.ror(lowercase , 6 ) ^ self.ror(lowercase , 11 ) ^ self.ror(lowercase , 25 )
A_ : List[Any] = (e & f) ^ ((~e & 0XFFFF_FFFF) & g)
A_ : Dict = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_0000_0000
A_ : List[str] = self.ror(lowercase , 2 ) ^ self.ror(lowercase , 13 ) ^ self.ror(lowercase , 22 )
A_ : str = (a & b) ^ (a & c) ^ (b & c)
A_ : int = (sa + maj) % 0X1_0000_0000
A_, A_, A_, A_, A_, A_, A_, A_ : List[Any] = (
g,
f,
e,
((d + tempa) % 0X1_0000_0000),
c,
b,
a,
((tempa + tempa) % 0X1_0000_0000),
)
A_ : int = [a, b, c, d, e, f, g, h]
# Modify final values
A_ : Dict = [
((element + mutated_hash_values[index]) % 0X1_0000_0000)
for index, element in enumerate(self.hashes )
]
A_ : Any = """""".join([hex(lowercase )[2:].zfill(8 ) for value in self.hashes] )
def _a (self , lowercase , lowercase ):
return 0XFFFF_FFFF & (value << (32 - rotations)) | (value >> rotations)
class _lowerCAmelCase ( unittest.TestCase ):
def _a (self ):
import hashlib
A_ : Optional[Any] = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(lowercase ).hash , hashlib.shaaaa(lowercase ).hexdigest() )
def a ( ):
'''simple docstring'''
import doctest
doctest.testmod()
A_ : Any = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
A_ : List[str] = parser.parse_args()
A_ : Tuple = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
A_ : Union[str, Any] = f.read()
else:
A_ : Optional[Any] = bytes(lowerCamelCase__ , """utf-8""" )
print(SHAaaa(lowerCamelCase__ ).hash )
if __name__ == "__main__":
main()
| 206
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _UpperCAmelCase ( a ,unittest.TestCase ):
'''simple docstring'''
a__ =KandinskyVaaImgaImgPipeline
a__ =['''image_embeds''', '''negative_image_embeds''', '''image''']
a__ =[
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
a__ =[
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
a__ =False
@property
def __lowerCAmelCase ( self ) -> Dict:
return 3_2
@property
def __lowerCAmelCase ( self ) -> Tuple:
return 3_2
@property
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return self.time_input_dim
@property
def __lowerCAmelCase ( self ) -> Tuple:
return self.time_input_dim * 4
@property
def __lowerCAmelCase ( self ) -> int:
return 1_0_0
@property
def __lowerCAmelCase ( self ) -> Optional[int]:
torch.manual_seed(0 )
_UpperCAmelCase : Any = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_UpperCAmelCase : str = UNetaDConditionModel(**A )
return model
@property
def __lowerCAmelCase ( self ) -> Optional[Any]:
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowerCAmelCase ( self ) -> Union[str, Any]:
torch.manual_seed(0 )
_UpperCAmelCase : int = VQModel(**self.dummy_movq_kwargs )
return model
def __lowerCAmelCase ( self ) -> List[str]:
_UpperCAmelCase : Optional[Any] = self.dummy_unet
_UpperCAmelCase : Tuple = self.dummy_movq
_UpperCAmelCase : Optional[int] = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00_085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
_UpperCAmelCase : Optional[int] = DDIMScheduler(**A )
_UpperCAmelCase : Tuple = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowerCAmelCase ( self , A , A=0 ) -> int:
_UpperCAmelCase : Optional[int] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(A ) ).to(A )
_UpperCAmelCase : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
A )
# create init_image
_UpperCAmelCase : Dict = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(A ) ).to(A )
_UpperCAmelCase : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase : Optional[int] = Image.fromarray(np.uinta(A ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
if str(A ).startswith('''mps''' ):
_UpperCAmelCase : Tuple = torch.manual_seed(A )
else:
_UpperCAmelCase : Dict = torch.Generator(device=A ).manual_seed(A )
_UpperCAmelCase : List[Any] = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 1_0,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Optional[int] = '''cpu'''
_UpperCAmelCase : List[Any] = self.get_dummy_components()
_UpperCAmelCase : Any = self.pipeline_class(**A )
_UpperCAmelCase : Optional[int] = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_UpperCAmelCase : List[Any] = pipe(**self.get_dummy_inputs(A ) )
_UpperCAmelCase : List[Any] = output.images
_UpperCAmelCase : Dict = pipe(
**self.get_dummy_inputs(A ) , return_dict=A , )[0]
_UpperCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
_UpperCAmelCase : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
_UpperCAmelCase : Tuple = np.array(
[0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Optional[int] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_img2img_frog.npy''' )
_UpperCAmelCase : Optional[int] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_UpperCAmelCase : List[Any] = '''A red cartoon frog, 4k'''
_UpperCAmelCase : Dict = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(A )
_UpperCAmelCase : Dict = KandinskyVaaImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
_UpperCAmelCase : str = pipeline.to(A )
pipeline.set_progress_bar_config(disable=A )
_UpperCAmelCase : Dict = torch.Generator(device='''cpu''' ).manual_seed(0 )
_UpperCAmelCase , _UpperCAmelCase : Tuple = pipe_prior(
A , generator=A , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_UpperCAmelCase : List[Any] = pipeline(
image=A , image_embeds=A , negative_image_embeds=A , generator=A , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type='''np''' , )
_UpperCAmelCase : Any = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(A , A )
| 68
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=a ):
'''simple docstring'''
a__ =['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A , **A ) -> int:
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __lowerCAmelCase ( cls , *A , **A ) -> Tuple:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __lowerCAmelCase ( cls , *A , **A ) -> Dict:
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 68
| 1
|
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
lowerCAmelCase : List[Any] = """true"""
def a__ ( snake_case__ , snake_case__=82 , snake_case__=16 ) -> Optional[Any]:
set_seed(42 )
lowerCamelCase = RegressionModel()
lowerCamelCase = deepcopy(SCREAMING_SNAKE_CASE__ )
lowerCamelCase = RegressionDataset(length=SCREAMING_SNAKE_CASE__ )
lowerCamelCase = DataLoader(SCREAMING_SNAKE_CASE__ , batch_size=SCREAMING_SNAKE_CASE__ )
model.to(accelerator.device )
lowerCamelCase , lowerCamelCase = accelerator.prepare(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return model, ddp_model, dataloader
def a__ ( snake_case__ , snake_case__=False ) -> int:
lowerCamelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
lowerCamelCase = load_dataset("""glue""" , """mrpc""" , split="""validation""" )
def tokenize_function(snake_case__ ):
lowerCamelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ )
return outputs
with accelerator.main_process_first():
lowerCamelCase = dataset.map(
SCREAMING_SNAKE_CASE__ , batched=SCREAMING_SNAKE_CASE__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
lowerCamelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(snake_case__ ):
if use_longest:
return tokenizer.pad(SCREAMING_SNAKE_CASE__ , padding="""longest""" , return_tensors="""pt""" )
return tokenizer.pad(SCREAMING_SNAKE_CASE__ , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return DataLoader(SCREAMING_SNAKE_CASE__ , shuffle=SCREAMING_SNAKE_CASE__ , collate_fn=SCREAMING_SNAKE_CASE__ , batch_size=16 )
def a__ ( snake_case__ , snake_case__ ) -> str:
lowerCamelCase = Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__ , split_batches=SCREAMING_SNAKE_CASE__ )
lowerCamelCase = get_dataloader(SCREAMING_SNAKE_CASE__ , not dispatch_batches )
lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
"""hf-internal-testing/mrpc-bert-base-cased""" , return_dict=SCREAMING_SNAKE_CASE__ )
lowerCamelCase , lowerCamelCase = accelerator.prepare(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
lowerCamelCase = []
for batch in dataloader:
lowerCamelCase , lowerCamelCase = batch.values()
with torch.no_grad():
lowerCamelCase = model(SCREAMING_SNAKE_CASE__ )
lowerCamelCase , lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowerCamelCase , lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(SCREAMING_SNAKE_CASE__ )
targs.append(SCREAMING_SNAKE_CASE__ )
lowerCamelCase , lowerCamelCase = torch.cat(SCREAMING_SNAKE_CASE__ ), torch.cat(SCREAMING_SNAKE_CASE__ )
return logits, targs
def a__ ( snake_case__ , snake_case__=82 , snake_case__=False , snake_case__=False , snake_case__=16 ) -> List[Any]:
lowerCamelCase , lowerCamelCase , lowerCamelCase = get_basic_setup(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowerCamelCase , lowerCamelCase = generate_predictions(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
assert (
len(SCREAMING_SNAKE_CASE__ ) == num_samples
), F'Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(SCREAMING_SNAKE_CASE__ )}'
def a__ ( snake_case__ = False , snake_case__ = False ) -> str:
lowerCamelCase = evaluate.load("""glue""" , """mrpc""" )
lowerCamelCase , lowerCamelCase = get_mrpc_setup(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# First do baseline
lowerCamelCase , lowerCamelCase , lowerCamelCase = setup["""no"""]
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
for batch in dataloader:
batch.to(SCREAMING_SNAKE_CASE__ )
with torch.inference_mode():
lowerCamelCase = model(**SCREAMING_SNAKE_CASE__ )
lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE__ , references=batch["""labels"""] )
lowerCamelCase = metric.compute()
# Then do distributed
lowerCamelCase , lowerCamelCase , lowerCamelCase = setup["""ddp"""]
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowerCamelCase = model(**SCREAMING_SNAKE_CASE__ )
lowerCamelCase = outputs.logits.argmax(dim=-1 )
lowerCamelCase = batch["""labels"""]
lowerCamelCase , lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE__ , references=SCREAMING_SNAKE_CASE__ )
lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'
def a__ ( ) -> Optional[Any]:
lowerCamelCase = Accelerator(split_batches=SCREAMING_SNAKE_CASE__ , dispatch_batches=SCREAMING_SNAKE_CASE__ )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("""**Testing gather_for_metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`' )
test_mrpc(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test torch metrics**""" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowerCamelCase = Accelerator(split_batches=SCREAMING_SNAKE_CASE__ , dispatch_batches=SCREAMING_SNAKE_CASE__ )
if accelerator.is_local_main_process:
print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
test_torch_metrics(SCREAMING_SNAKE_CASE__ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("""**Test last batch is not dropped when perfectly divisible**""" )
lowerCamelCase = Accelerator()
test_torch_metrics(SCREAMING_SNAKE_CASE__ , 5_12 )
accelerator.state._reset_state()
def a__ ( snake_case__ ) -> Union[str, Any]:
main()
if __name__ == "__main__":
main()
| 291
|
from timeit import timeit
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
A__ = 0
while number:
number &= number - 1
result += 1
return result
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if number < 0:
raise ValueError('the value of input must not be negative' )
A__ = 0
while number:
if number % 2 == 1:
result += 1
number >>= 1
return result
def _snake_case( ) -> None:
'''simple docstring'''
def do_benchmark(SCREAMING_SNAKE_CASE__ : int ) -> None:
A__ = 'import __main__ as z'
print(f'Benchmark when {number = }:' )
print(f'{get_set_bits_count_using_modulo_operator(SCREAMING_SNAKE_CASE__ ) = }' )
A__ = timeit('z.get_set_bits_count_using_modulo_operator(25)' , setup=SCREAMING_SNAKE_CASE__ )
print(f'timeit() runs in {timing} seconds' )
print(f'{get_set_bits_count_using_brian_kernighans_algorithm(SCREAMING_SNAKE_CASE__ ) = }' )
A__ = timeit(
'z.get_set_bits_count_using_brian_kernighans_algorithm(25)' , setup=SCREAMING_SNAKE_CASE__ , )
print(f'timeit() runs in {timing} seconds' )
for number in (25, 37, 58, 0):
do_benchmark(SCREAMING_SNAKE_CASE__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 7
| 0
|
'''simple docstring'''
import os
def snake_case__ ( ) -> List[str]:
A_ : Optional[Any] = os.path.join(os.path.dirname(lowerCamelCase__ ) , '''num.txt''' )
with open(lowerCamelCase__ ) as file_hand:
return str(sum(int(lowerCamelCase__ ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
print(solution())
| 4
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'table-transformer'
_lowerCAmelCase = ['past_key_values']
_lowerCAmelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Any , _lowerCamelCase : Union[str, Any]=True , _lowerCamelCase : Dict=None , _lowerCamelCase : int=3 , _lowerCamelCase : Any=100 , _lowerCamelCase : List[Any]=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : Any=8 , _lowerCamelCase : Dict=6 , _lowerCamelCase : Tuple=2048 , _lowerCamelCase : int=8 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : List[Any]=0.0 , _lowerCamelCase : List[Any]=True , _lowerCamelCase : Optional[int]="relu" , _lowerCamelCase : Union[str, Any]=256 , _lowerCamelCase : Any=0.1 , _lowerCamelCase : Tuple=0.0 , _lowerCamelCase : Optional[int]=0.0 , _lowerCamelCase : str=0.02 , _lowerCamelCase : Tuple=1.0 , _lowerCamelCase : Dict=False , _lowerCamelCase : str="sine" , _lowerCamelCase : str="resnet50" , _lowerCamelCase : Any=True , _lowerCamelCase : List[str]=False , _lowerCamelCase : Any=1 , _lowerCamelCase : int=5 , _lowerCamelCase : Tuple=2 , _lowerCamelCase : Optional[int]=1 , _lowerCamelCase : Any=1 , _lowerCamelCase : Dict=5 , _lowerCamelCase : str=2 , _lowerCamelCase : Union[str, Any]=0.1 , **_lowerCamelCase : int , ):
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
A_ : int = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
A_ : str = backbone_config.get('''model_type''' )
A_ : Optional[int] = CONFIG_MAPPING[backbone_model_type]
A_ : List[str] = config_class.from_dict(_lowerCamelCase )
# set timm attributes to None
A_ ,A_ ,A_ : Union[str, Any] = None, None, None
A_ : Optional[Any] = use_timm_backbone
A_ : Optional[int] = backbone_config
A_ : Optional[Any] = num_channels
A_ : Dict = num_queries
A_ : str = d_model
A_ : List[str] = encoder_ffn_dim
A_ : int = encoder_layers
A_ : Optional[Any] = encoder_attention_heads
A_ : List[str] = decoder_ffn_dim
A_ : Any = decoder_layers
A_ : List[str] = decoder_attention_heads
A_ : Tuple = dropout
A_ : Optional[Any] = attention_dropout
A_ : Any = activation_dropout
A_ : List[Any] = activation_function
A_ : Dict = init_std
A_ : Any = init_xavier_std
A_ : List[Any] = encoder_layerdrop
A_ : int = decoder_layerdrop
A_ : Any = encoder_layers
A_ : List[str] = auxiliary_loss
A_ : List[Any] = position_embedding_type
A_ : Optional[Any] = backbone
A_ : Tuple = use_pretrained_backbone
A_ : List[Any] = dilation
# Hungarian matcher
A_ : List[str] = class_cost
A_ : str = bbox_cost
A_ : Union[str, Any] = giou_cost
# Loss coefficients
A_ : Any = mask_loss_coefficient
A_ : Optional[int] = dice_loss_coefficient
A_ : Dict = bbox_loss_coefficient
A_ : int = giou_loss_coefficient
A_ : int = eos_coefficient
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def _a ( self : List[Any] ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a ( self : Any ):
"""simple docstring"""
return self.d_model
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = version.parse('1.11' )
@property
def _a ( self : Tuple ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def _a ( self : Optional[int] ):
"""simple docstring"""
return 1E-5
@property
def _a ( self : str ):
"""simple docstring"""
return 12
| 4
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
lowerCAmelCase__ = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wikisql-supervised''': (
'''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-tabfact''': (
'''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
),
}
class lowercase_ (A__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 'tapas'
def __init__( self : int ,lowercase__ : Optional[Any]=3_0_5_2_2 ,lowercase__ : Tuple=7_6_8 ,lowercase__ : int=1_2 ,lowercase__ : Any=1_2 ,lowercase__ : Union[str, Any]=3_0_7_2 ,lowercase__ : Optional[int]="gelu" ,lowercase__ : Optional[int]=0.1 ,lowercase__ : Dict=0.1 ,lowercase__ : str=1_0_2_4 ,lowercase__ : Union[str, Any]=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0] ,lowercase__ : Optional[int]=0.0_2 ,lowercase__ : List[str]=1e-1_2 ,lowercase__ : Optional[Any]=0 ,lowercase__ : Optional[Any]=1_0.0 ,lowercase__ : Optional[Any]=0 ,lowercase__ : str=1.0 ,lowercase__ : Union[str, Any]=None ,lowercase__ : List[Any]=1.0 ,lowercase__ : Optional[Any]=False ,lowercase__ : Union[str, Any]=None ,lowercase__ : int=1.0 ,lowercase__ : Dict=1.0 ,lowercase__ : Optional[int]=False ,lowercase__ : int=False ,lowercase__ : List[str]="ratio" ,lowercase__ : Tuple=None ,lowercase__ : List[Any]=None ,lowercase__ : List[Any]=6_4 ,lowercase__ : Any=3_2 ,lowercase__ : Tuple=False ,lowercase__ : Union[str, Any]=True ,lowercase__ : Optional[int]=False ,lowercase__ : Tuple=False ,lowercase__ : Tuple=True ,lowercase__ : Optional[Any]=False ,lowercase__ : Union[str, Any]=None ,lowercase__ : Optional[Any]=None ,**lowercase__ : str ,):
super().__init__(pad_token_id=__lowerCamelCase ,**__lowerCamelCase )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_sizes
__lowercase = initializer_range
__lowercase = layer_norm_eps
# Fine-tuning task hyperparameters
__lowercase = positive_label_weight
__lowercase = num_aggregation_labels
__lowercase = aggregation_loss_weight
__lowercase = use_answer_as_supervision
__lowercase = answer_loss_importance
__lowercase = use_normalized_answer_loss
__lowercase = huber_loss_delta
__lowercase = temperature
__lowercase = aggregation_temperature
__lowercase = use_gumbel_for_cells
__lowercase = use_gumbel_for_aggregation
__lowercase = average_approximation_function
__lowercase = cell_selection_preference
__lowercase = answer_loss_cutoff
__lowercase = max_num_rows
__lowercase = max_num_columns
__lowercase = average_logits_per_cell
__lowercase = select_one_column
__lowercase = allow_empty_column_selection
__lowercase = init_cell_selection_weights_to_zero
__lowercase = reset_position_index_per_cell
__lowercase = disable_per_token_loss
# Aggregation hyperparameters
__lowercase = aggregation_labels
__lowercase = no_aggregation_label_index
if isinstance(self.aggregation_labels ,__lowerCamelCase ):
__lowercase = {int(__lowerCamelCase ): v for k, v in aggregation_labels.items()}
| 104
|
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase__ ( A__ ):
"""simple docstring"""
def __init__( self : str , __lowerCamelCase : Tuple , __lowerCamelCase : Dict ) -> str:
super().__init__()
# make sure scheduler can always be converted to DDIM
SCREAMING_SNAKE_CASE__ = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=__lowerCamelCase , scheduler=__lowerCamelCase )
@torch.no_grad()
def __call__( self : List[Any] , __lowerCamelCase : int = 1 , __lowerCamelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCamelCase : float = 0.0 , __lowerCamelCase : int = 50 , __lowerCamelCase : Optional[bool] = None , __lowerCamelCase : Optional[str] = "pil" , __lowerCamelCase : bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size , __lowerCamelCase ):
SCREAMING_SNAKE_CASE__ = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
SCREAMING_SNAKE_CASE__ = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(__lowerCamelCase , __lowerCamelCase ) and len(__lowerCamelCase ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(__lowerCamelCase )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
SCREAMING_SNAKE_CASE__ = randn_tensor(__lowerCamelCase , generator=__lowerCamelCase , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(__lowerCamelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
SCREAMING_SNAKE_CASE__ = self.unet(__lowerCamelCase , __lowerCamelCase ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
SCREAMING_SNAKE_CASE__ = self.scheduler.step(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , eta=__lowerCamelCase , use_clipped_model_output=__lowerCamelCase , generator=__lowerCamelCase ).prev_sample
SCREAMING_SNAKE_CASE__ = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE__ = self.numpy_to_pil(__lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowerCamelCase )
| 314
| 0
|
import math
from datetime import datetime, timedelta
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = year % 19
SCREAMING_SNAKE_CASE : List[str] = year % 4
SCREAMING_SNAKE_CASE : str = year % 7
SCREAMING_SNAKE_CASE : List[Any] = math.floor(year / 100 )
SCREAMING_SNAKE_CASE : int = math.floor((13 + 8 * leap_day_inhibits) / 25 )
SCREAMING_SNAKE_CASE : List[str] = leap_day_inhibits / 4
SCREAMING_SNAKE_CASE : Dict = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
SCREAMING_SNAKE_CASE : Dict = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
SCREAMING_SNAKE_CASE : Union[str, Any] = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
SCREAMING_SNAKE_CASE : List[str] = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(lowercase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(lowercase , 4 , 18 )
else:
return datetime(lowercase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1_994, 2_000, 2_010, 2_021, 2_023):
snake_case = """will be""" if year > datetime.now().year else """was"""
print(F"""Easter in {year} {tense} {gauss_easter(year)}""")
| 369
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 319
| 0
|
'''simple docstring'''
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[False] * len(_lowerCAmelCase )
__lowercase =[]
queue.append(_lowerCAmelCase )
__lowercase =True
while queue:
__lowercase =queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(_lowerCAmelCase )
__lowercase =True
__lowercase =u
return visited[t]
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[-1] * (len(_lowerCAmelCase ))
__lowercase =0
while bfs(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowercase =float('Inf' )
__lowercase =sink
while s != source:
# Find the minimum value in select path
__lowercase =min(_lowerCAmelCase , graph[parent[s]][s] )
__lowercase =parent[s]
max_flow += path_flow
__lowercase =sink
while v != source:
__lowercase =parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
__lowercase =parent[v]
return max_flow
lowerCamelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
lowerCamelCase , lowerCamelCase = 0, 5
print(ford_fulkerson(graph, source, sink))
| 166
|
'''simple docstring'''
from __future__ import annotations
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[True] * limit
__lowercase =False
__lowercase =False
__lowercase =True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
__lowercase =i * 2
while index < limit:
__lowercase =False
__lowercase =index + i
__lowercase =[2]
for i in range(3 , _lowerCAmelCase , 2 ):
if is_prime[i]:
primes.append(_lowerCAmelCase )
return primes
def _A ( _lowerCAmelCase = 1_000_000 ):
"""simple docstring"""
__lowercase =prime_sieve(_lowerCAmelCase )
__lowercase =0
__lowercase =0
for i in range(len(_lowerCAmelCase ) ):
for j in range(i + length , len(_lowerCAmelCase ) ):
__lowercase =sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
__lowercase =j - i
__lowercase =sol
return largest
if __name__ == "__main__":
print(f"{solution() = }")
| 166
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: str = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
'''tokenization_lxmert''': ['''LxmertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: Union[str, Any] = ['''LxmertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: str = [
'''LxmertEncoder''',
'''LxmertForPreTraining''',
'''LxmertForQuestionAnswering''',
'''LxmertModel''',
'''LxmertPreTrainedModel''',
'''LxmertVisualFeatureEncoder''',
'''LxmertXLayer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase: int = [
'''TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLxmertForPreTraining''',
'''TFLxmertMainLayer''',
'''TFLxmertModel''',
'''TFLxmertPreTrainedModel''',
'''TFLxmertVisualFeatureEncoder''',
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
_lowercase: List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 368
|
import math
from numpy import inf
from scipy.integrate import quad
def a( A : float ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(A , 0 , A , args=(A) )[0]
def a( A : float , A : float ) -> float:
"""simple docstring"""
return math.pow(A , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 71
| 0
|
from ...processing_utils import ProcessorMixin
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = 'WhisperFeatureExtractor'
__lowerCamelCase = 'WhisperTokenizer'
def __init__( self , lowercase , lowercase ) -> Dict:
'''simple docstring'''
super().__init__(lowercase , lowercase )
A__ = self.feature_extractor
A__ = False
def UpperCamelCase ( self , lowercase=None , lowercase=None , lowercase=True ) -> Optional[Any]:
'''simple docstring'''
return self.tokenizer.get_decoder_prompt_ids(task=lowercase , language=lowercase , no_timestamps=lowercase )
def __call__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*lowercase , **lowercase )
A__ = kwargs.pop("audio" , lowercase )
A__ = kwargs.pop("sampling_rate" , lowercase )
A__ = kwargs.pop("text" , lowercase )
if len(lowercase ) > 0:
A__ = args[0]
A__ = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
A__ = self.feature_extractor(lowercase , *lowercase , sampling_rate=lowercase , **lowercase )
if text is not None:
A__ = self.tokenizer(lowercase , **lowercase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
A__ = encodings["input_ids"]
return inputs
def UpperCamelCase ( self , *lowercase , **lowercase ) -> List[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def UpperCamelCase ( self , *lowercase , **lowercase ) -> str:
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
def UpperCamelCase ( self , lowercase , lowercase="np" ) -> Dict:
'''simple docstring'''
return self.tokenizer.get_prompt_ids(lowercase , return_tensors=lowercase )
| 68
|
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaTokenizer
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(lowercase )
A__ = AddedToken("<mask>" , lstrip=lowercase , rstrip=lowercase )
A__ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = "this is a test"
A__ = "this is a test"
return input_text, output_text
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=20 , lowercase=5 ) -> Optional[Any]:
'''simple docstring'''
A__ , A__ = self.get_input_output_texts(lowercase )
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A__ = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
return text, ids
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = "<pad>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(lowercase ) , 81 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
A__ = tokenizer.add_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size + len(lowercase ) )
A__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
A__ = tokenizer.add_special_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size_a + len(lowercase ) )
A__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(lowercase , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
A__ = tokenizer.convert_tokens_to_ids(lowercase )
# fmt: off
self.assertListEqual(lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
A__ = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"attention_mask": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=lowercase , )
| 68
| 1
|
"""simple docstring"""
from __future__ import annotations
from math import gcd
def __A ( a_ :int , a_ :int = 2 , a_ :int = 1 , a_ :int = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('''The input value cannot be less than 2''')
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(a_ :int , a_ :int , a_ :int) -> int:
return (pow(a_ , 2) + step) % modulus
for _ in range(a_):
# These track the position within the cycle detection logic.
__a : Any = seed
__a : int = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
__a : Optional[Any] = rand_fn(a_ , a_ , a_)
__a : int = rand_fn(a_ , a_ , a_)
__a : List[Any] = rand_fn(a_ , a_ , a_)
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
__a : int = gcd(hare - tortoise , a_)
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
__a : Optional[int] = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
A = argparse.ArgumentParser()
parser.add_argument(
'''num''',
type=int,
help='''The value to find a divisor of''',
)
parser.add_argument(
'''--attempts''',
type=int,
default=3,
help='''The number of attempts before giving up''',
)
A = parser.parse_args()
A = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F'{args.num} is probably prime')
else:
A = args.num // divisor
print(F'{args.num} = {divisor} * {quotient}')
| 188
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
__lowerCAmelCase = '''CIDAS/clipseg-rd64-refined'''
__lowerCAmelCase = '''image_segmenter'''
__lowerCAmelCase = CLIPSegForImageSegmentation
__lowerCAmelCase = ['''image''', '''text''']
__lowerCAmelCase = ['''image''']
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ):
requires_backends(self , ['''vision'''] )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase ):
return self.pre_processor(text=[label] , images=[image] , padding=_UpperCAmelCase , return_tensors='''pt''' )
def _lowerCamelCase ( self , _UpperCAmelCase ):
with torch.no_grad():
__a : List[str] = self.model(**_UpperCAmelCase ).logits
return logits
def _lowerCamelCase ( self , _UpperCAmelCase ):
__a : str = outputs.cpu().detach().numpy()
__a : int = 0
__a : Optional[int] = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 188
| 1
|
'''simple docstring'''
import os
def a_ ( ):
lowerCAmelCase = os.path.join(os.path.dirname(lowerCamelCase ) , 'num.txt' )
with open(lowerCamelCase ) as file_hand:
return str(sum(int(lowerCamelCase ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 4
|
'''simple docstring'''
def a_ ( lowerCamelCase : Optional[Any] ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def a_ ( lowerCamelCase : dict[int, list[int]] ):
lowerCAmelCase = 0
lowerCAmelCase = len(lowerCamelCase ) # No of vertices in graph
lowerCAmelCase = [0] * n
lowerCAmelCase = [False] * n
def dfs(lowerCamelCase : Tuple , lowerCamelCase : str , lowerCamelCase : Dict , lowerCamelCase : str ):
lowerCAmelCase = True
lowerCAmelCase = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(lowerCamelCase , lowerCamelCase , lowerCamelCase , id_ )
lowerCAmelCase = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
lowerCAmelCase = min(low[at] , low[to] )
lowerCAmelCase = []
for i in range(lowerCamelCase ):
if not visited[i]:
dfs(lowerCamelCase , -1 , lowerCamelCase , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4
| 1
|
"""simple docstring"""
import string
def __lowerCAmelCase ( lowercase : str ) -> str:
"""simple docstring"""
snake_case : List[str] = ""
for i in sequence:
snake_case : Optional[Any] = ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <= 122:
output += chr(219 - extract )
else:
output += i
return output
def __lowerCAmelCase ( lowercase : str ) -> str:
"""simple docstring"""
snake_case : Dict = string.ascii_letters
snake_case : List[Any] = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )
def __lowerCAmelCase ( ) -> None:
"""simple docstring"""
from timeit import timeit
print("Running performance benchmarks..." )
snake_case : Optional[int] = "from string import printable ; from __main__ import atbash, atbash_slow"
print(F'> atbash_slow(): {timeit("atbash_slow(printable)" , setup=lowercase )} seconds' )
print(F'> atbash(): {timeit("atbash(printable)" , setup=lowercase )} seconds' )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(F'''{example} encrypted in atbash: {atbash(example)}''')
benchmark()
| 112
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase ( self ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
snake_case : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : Any = self.dummy_uncond_unet
snake_case : Tuple = KarrasVeScheduler()
snake_case : int = KarrasVePipeline(unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Optional[Any] = torch.manual_seed(0 )
snake_case : List[Any] = pipe(num_inference_steps=2 , generator=UpperCamelCase__ , output_type="numpy" ).images
snake_case : Dict = torch.manual_seed(0 )
snake_case : Dict = pipe(num_inference_steps=2 , generator=UpperCamelCase__ , output_type="numpy" , return_dict=UpperCamelCase__ )[0]
snake_case : Tuple = image[0, -3:, -3:, -1]
snake_case : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case : int = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[Any] = "google/ncsnpp-celebahq-256"
snake_case : List[str] = UNetaDModel.from_pretrained(UpperCamelCase__ )
snake_case : Optional[Any] = KarrasVeScheduler()
snake_case : Optional[int] = KarrasVePipeline(unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = torch.manual_seed(0 )
snake_case : Union[str, Any] = pipe(num_inference_steps=20 , generator=UpperCamelCase__ , output_type="numpy" ).images
snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
snake_case : Any = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 112
| 1
|
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def _UpperCAmelCase ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
_lowerCAmelCase = s.rsplit(__lowercase , __lowercase )
return new.join(__lowercase )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = {}
_lowerCAmelCase = ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
_lowerCAmelCase = key.replace(F'{group_key}.' , F'{group_key}.group.' )
if "res_path" in key:
_lowerCAmelCase = key.replace("""res_path.""" , """res_path.path.""" )
if key.endswith(""".w""" ):
_lowerCAmelCase = rreplace(__lowercase , """.w""" , """.weight""" , 1 )
if key.endswith(""".b""" ):
_lowerCAmelCase = rreplace(__lowercase , """.b""" , """.bias""" , 1 )
_lowerCAmelCase = value.float()
return upgrade
@torch.no_grad()
def _UpperCAmelCase ( snake_case , snake_case , snake_case=None , snake_case=True ):
"""simple docstring"""
from dall_e import Encoder
_lowerCAmelCase = Encoder()
if os.path.exists(__lowercase ):
_lowerCAmelCase = torch.load(__lowercase )
else:
_lowerCAmelCase = torch.hub.load_state_dict_from_url(__lowercase )
if isinstance(__lowercase , __lowercase ):
_lowerCAmelCase = ckpt.state_dict()
encoder.load_state_dict(__lowercase )
if config_path is not None:
_lowerCAmelCase = FlavaImageCodebookConfig.from_pretrained(__lowercase )
else:
_lowerCAmelCase = FlavaImageCodebookConfig()
_lowerCAmelCase = FlavaImageCodebook(__lowercase ).eval()
_lowerCAmelCase = encoder.state_dict()
_lowerCAmelCase = upgrade_state_dict(__lowercase )
hf_model.load_state_dict(__lowercase )
_lowerCAmelCase = hf_model.state_dict()
_lowerCAmelCase = count_parameters(__lowercase )
_lowerCAmelCase = count_parameters(__lowercase )
assert torch.allclose(__lowercase , __lowercase , atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(__lowercase )
else:
return hf_state_dict
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
A__ = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 82
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = [
('''bert.bert''', '''visual_bert'''),
('''bert.cls''', '''cls'''),
('''bert.classifier''', '''cls'''),
('''token_type_embeddings_visual''', '''visual_token_type_embeddings'''),
('''position_embeddings_visual''', '''visual_position_embeddings'''),
('''projection''', '''visual_projection'''),
]
UpperCamelCase = [
'''nlvr2_coco_pre_trained.th''',
'''nlvr2_fine_tuned.th''',
'''nlvr2_pre_trained.th''',
'''vcr_coco_pre_train.th''',
'''vcr_fine_tune.th''',
'''vcr_pre_train.th''',
'''vqa_coco_pre_trained.th''',
'''vqa_fine_tuned.th''',
'''vqa_pre_trained.th''',
]
def SCREAMING_SNAKE_CASE( __lowercase ) -> List[Any]:
A: List[Any] = torch.load(__lowercase , map_location='''cpu''' )
return sd
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=rename_keys_prefix ) -> Optional[Any]:
A: Tuple = OrderedDict()
A: Dict = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
A: int = key
for name_pair in rename_keys_prefix:
A: Optional[int] = new_key.replace(name_pair[0] , name_pair[1] )
A: Union[str, Any] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
A: int = new_d['''cls.predictions.bias''']
return new_d
@torch.no_grad()
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Dict:
assert (
checkpoint_path.split('''/''' )[-1] in ACCEPTABLE_CHECKPOINTS
), F"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""
# Get Config
if "pre" in checkpoint_path:
A: Optional[Any] = '''pretraining'''
if "vcr" in checkpoint_path:
A: Optional[int] = {'''visual_embedding_dim''': 5_1_2}
elif "vqa_advanced" in checkpoint_path:
A: Optional[Any] = {'''visual_embedding_dim''': 2_0_4_8}
elif "vqa" in checkpoint_path:
A: Dict = {'''visual_embedding_dim''': 2_0_4_8}
elif "nlvr" in checkpoint_path:
A: Tuple = {'''visual_embedding_dim''': 1_0_2_4}
else:
raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`.""" )
else:
if "vcr" in checkpoint_path:
A: Dict = {'''visual_embedding_dim''': 5_1_2}
A: List[str] = '''multichoice'''
elif "vqa_advanced" in checkpoint_path:
A: List[str] = {'''visual_embedding_dim''': 2_0_4_8}
A: Optional[int] = '''vqa_advanced'''
elif "vqa" in checkpoint_path:
A: Dict = {'''visual_embedding_dim''': 2_0_4_8, '''num_labels''': 3_1_2_9}
A: Union[str, Any] = '''vqa'''
elif "nlvr" in checkpoint_path:
A: Optional[int] = {
'''visual_embedding_dim''': 1_0_2_4,
'''num_labels''': 2,
}
A: str = '''nlvr'''
A: Union[str, Any] = VisualBertConfig(**__lowercase )
# Load State Dict
A: Union[str, Any] = load_state_dict(__lowercase )
A: str = get_new_dict(__lowercase , __lowercase )
if model_type == "pretraining":
A: Optional[Any] = VisualBertForPreTraining(__lowercase )
elif model_type == "vqa":
A: Optional[Any] = VisualBertForQuestionAnswering(__lowercase )
elif model_type == "nlvr":
A: Union[str, Any] = VisualBertForVisualReasoning(__lowercase )
elif model_type == "multichoice":
A: Any = VisualBertForMultipleChoice(__lowercase )
model.load_state_dict(__lowercase )
# Save Checkpoints
Path(__lowercase ).mkdir(exist_ok=__lowercase )
model.save_pretrained(__lowercase )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''orig_checkpoint_path''', type=str, help='''A path to .th on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', type=str, help='''Path to the output PyTorch model.''')
UpperCamelCase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 319
| 0
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
snake_case_ = [[1, 2, 4], [1, 2, 3, 4]]
snake_case_ = DisjunctiveConstraint(a__ )
self.assertTrue(isinstance(dc.token_ids , a__ ) )
with self.assertRaises(a__ ):
DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )
with self.assertRaises(a__ ):
DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
snake_case_ = [[1, 2], [1, 2, 3, 4]]
with self.assertRaises(a__ ):
DisjunctiveConstraint(a__ ) # fails here
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = [[1, 2, 3], [1, 2, 4]]
snake_case_ = DisjunctiveConstraint(a__ )
snake_case_ , snake_case_ , snake_case_ = dc.update(1 )
snake_case_ = stepped is True and completed is False and reset is False
self.assertTrue(a__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
snake_case_ , snake_case_ , snake_case_ = dc.update(2 )
snake_case_ = stepped is True and completed is False and reset is False
self.assertTrue(a__ )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
snake_case_ , snake_case_ , snake_case_ = dc.update(3 )
snake_case_ = stepped is True and completed is True and reset is False
self.assertTrue(a__ )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 3] )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
snake_case_ = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]
snake_case_ = DisjunctiveConstraint(a__ )
snake_case_ , snake_case_ , snake_case_ = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1] )
snake_case_ , snake_case_ , snake_case_ = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2] )
snake_case_ , snake_case_ , snake_case_ = dc.update(4 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.current_seq == [1, 2, 4] )
snake_case_ , snake_case_ , snake_case_ = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.current_seq == [1, 2, 4, 5] )
dc.reset()
snake_case_ , snake_case_ , snake_case_ = dc.update(1 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 3 )
self.assertTrue(dc.current_seq == [1] )
snake_case_ , snake_case_ , snake_case_ = dc.update(2 )
self.assertTrue(not dc.completed )
self.assertTrue(dc.remaining() == 2 )
self.assertTrue(dc.current_seq == [1, 2] )
snake_case_ , snake_case_ , snake_case_ = dc.update(5 )
self.assertTrue(dc.completed ) # Completed!
self.assertTrue(dc.remaining() == 0 )
self.assertTrue(dc.current_seq == [1, 2, 5] )
| 92
|
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[int] = "Alexander Joslin"
import operator as op
from .stack import Stack
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}
snake_case_ = Stack()
snake_case_ = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(snake_case ) )
elif i in operators:
# RULE 2
operator_stack.push(snake_case )
elif i == ")":
# RULE 4
snake_case_ = operator_stack.peek()
operator_stack.pop()
snake_case_ = operand_stack.peek()
operand_stack.pop()
snake_case_ = operand_stack.peek()
operand_stack.pop()
snake_case_ = operators[opr](snake_case , snake_case )
operand_stack.push(snake_case )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : List[Any] = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
| 92
| 1
|
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def _snake_case ( _snake_case : Iterable[str] , _snake_case : int ):
lowerCAmelCase : Optional[int] = iter(_snake_case )
while True:
lowerCAmelCase : Tuple = tuple(itertools.islice(_snake_case , _snake_case ) )
if not chunk:
return
yield chunk
def _snake_case ( _snake_case : str ):
lowerCAmelCase : List[Any] = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
lowerCAmelCase : Union[str, Any] = ''''''
if len(_snake_case ) < 2:
return dirty
for i in range(len(_snake_case ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(_snake_case ) & 1:
clean += "X"
return clean
def _snake_case ( _snake_case : str ):
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
lowerCAmelCase : Dict = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
lowerCAmelCase : Any = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(_snake_case )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(_snake_case )
return table
def _snake_case ( _snake_case : str , _snake_case : str ):
lowerCAmelCase : Union[str, Any] = generate_table(_snake_case )
lowerCAmelCase : str = prepare_input(_snake_case )
lowerCAmelCase : Any = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_snake_case , 2 ):
lowerCAmelCase, lowerCAmelCase : List[Any] = divmod(table.index(_snake_case ) , 5 )
lowerCAmelCase, lowerCAmelCase : List[str] = divmod(table.index(_snake_case ) , 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def _snake_case ( _snake_case : str , _snake_case : str ):
lowerCAmelCase : Dict = generate_table(_snake_case )
lowerCAmelCase : Union[str, Any] = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(_snake_case , 2 ):
lowerCAmelCase, lowerCAmelCase : List[str] = divmod(table.index(_snake_case ) , 5 )
lowerCAmelCase, lowerCAmelCase : Union[str, Any] = divmod(table.index(_snake_case ) , 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 60
|
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(a_ )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(a_ ):
for j in range(i + 1 ,a_ ):
if random.random() < probability:
graph[i].append(a_ )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(a_ )
return graph
def A ( a_ ) -> dict:
return {
i: [j for j in range(a_ ) if i != j] for i in range(a_ )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 71
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : Dict = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ : str = [
"""FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FalconForCausalLM""",
"""FalconModel""",
"""FalconPreTrainedModel""",
"""FalconForSequenceClassification""",
"""FalconForTokenClassification""",
"""FalconForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
lowercase__ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 289
|
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Dict=1_3 , SCREAMING_SNAKE_CASE_ : List[Any]=7 , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : str=True , SCREAMING_SNAKE_CASE_ : List[str]=9_9 , SCREAMING_SNAKE_CASE_ : int=1_6 , SCREAMING_SNAKE_CASE_ : List[str]=3_6 , SCREAMING_SNAKE_CASE_ : List[Any]=6 , SCREAMING_SNAKE_CASE_ : Tuple=6 , SCREAMING_SNAKE_CASE_ : List[Any]=6 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=3_7 , SCREAMING_SNAKE_CASE_ : Tuple="gelu" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE_ : int=0.1 , SCREAMING_SNAKE_CASE_ : Optional[Any]=5_1_2 , SCREAMING_SNAKE_CASE_ : List[str]=1_6 , SCREAMING_SNAKE_CASE_ : List[str]=2 , SCREAMING_SNAKE_CASE_ : List[Any]=0.02 , SCREAMING_SNAKE_CASE_ : Dict=3 , SCREAMING_SNAKE_CASE_ : int=4 , SCREAMING_SNAKE_CASE_ : Tuple=None , ):
lowerCAmelCase_ : Any = parent
lowerCAmelCase_ : Optional[int] = batch_size
lowerCAmelCase_ : Dict = seq_length
lowerCAmelCase_ : Tuple = is_training
lowerCAmelCase_ : str = use_input_mask
lowerCAmelCase_ : Union[str, Any] = use_token_type_ids
lowerCAmelCase_ : Tuple = use_labels
lowerCAmelCase_ : Optional[int] = vocab_size
lowerCAmelCase_ : Any = embedding_size
lowerCAmelCase_ : Optional[Any] = hidden_size
lowerCAmelCase_ : str = num_hidden_layers
lowerCAmelCase_ : Optional[Any] = num_hidden_groups
lowerCAmelCase_ : Dict = num_attention_heads
lowerCAmelCase_ : Optional[Any] = intermediate_size
lowerCAmelCase_ : Any = hidden_act
lowerCAmelCase_ : Union[str, Any] = hidden_dropout_prob
lowerCAmelCase_ : int = attention_probs_dropout_prob
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : List[Any] = type_vocab_size
lowerCAmelCase_ : Any = type_sequence_label_size
lowerCAmelCase_ : Optional[int] = initializer_range
lowerCAmelCase_ : Tuple = num_labels
lowerCAmelCase_ : Dict = num_choices
lowerCAmelCase_ : Tuple = scope
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase_ : str = None
if self.use_input_mask:
lowerCAmelCase_ : int = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ : List[Any] = None
if self.use_token_type_ids:
lowerCAmelCase_ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : Union[str, Any] = None
lowerCAmelCase_ : str = None
if self.use_labels:
lowerCAmelCase_ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase_ : Dict = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase_ : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
return AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , num_hidden_groups=self.num_hidden_groups , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str ):
lowerCAmelCase_ : Union[str, Any] = AlbertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : List[str] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Tuple ):
lowerCAmelCase_ : Optional[Any] = AlbertForPreTraining(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , sentence_order_label=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict ):
lowerCAmelCase_ : str = AlbertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : str = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any] ):
lowerCAmelCase_ : List[str] = AlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Any = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self : Dict , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict ):
lowerCAmelCase_ : Union[str, Any] = self.num_labels
lowerCAmelCase_ : Union[str, Any] = AlbertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Any , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
lowerCAmelCase_ : List[str] = self.num_labels
lowerCAmelCase_ : List[Any] = AlbertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : str = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
lowerCAmelCase_ : Optional[Any] = self.num_choices
lowerCAmelCase_ : int = AlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase_ : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : List[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase_ : List[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : Optional[int] = self.prepare_config_and_inputs()
(
(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,(
lowerCAmelCase_
) ,
) : Optional[int] = config_and_inputs
lowerCAmelCase_ : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( lowercase_, lowercase_, unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE = (
{
"""feature-extraction""": AlbertModel,
"""fill-mask""": AlbertForMaskedLM,
"""question-answering""": AlbertForQuestionAnswering,
"""text-classification""": AlbertForSequenceClassification,
"""token-classification""": AlbertForTokenClassification,
"""zero-shot""": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = True
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str=False ):
lowerCAmelCase_ : List[str] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowerCAmelCase_ : str = AlbertModelTester(self )
lowerCAmelCase_ : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : str ):
lowerCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCAmelCase_ : int = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Optional[Any] = AlbertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : Any = AlbertModel.from_pretrained('albert-base-v2' )
lowerCAmelCase_ : Tuple = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
lowerCAmelCase_ : Tuple = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowerCAmelCase_ : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )[0]
lowerCAmelCase_ : str = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = torch.tensor(
[[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ) )
| 289
| 1
|
import random
from .binary_exp_mod import bin_exp_mod
def UpperCAmelCase__ ( _A : Optional[int] , _A : Dict=10_00 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
a__ =n - 1
a__ =0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
a__ =0
while count < prec:
a__ =random.randint(2 , n - 1 )
a__ =bin_exp_mod(_A , _A , _A )
if b != 1:
a__ =True
for _ in range(_A ):
if b == n - 1:
a__ =False
break
a__ =b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
lowerCamelCase = abs(int(input('''Enter bound : ''').strip()))
print('''Here\'s the list of primes:''')
print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 188
|
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
lowerCamelCase = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __magic_name__ ( lowerCamelCase__ ):
'''simple docstring'''
def __init__( self, **lowercase_ ) -> Optional[int]:
"""simple docstring"""
super().__init__(**lowercase_ )
requires_backends(self, '''vision''' )
requires_backends(self, '''torch''' )
if self.framework != "pt":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )
self.check_model_type(lowercase_ )
def _UpperCAmelCase ( self, **lowercase_ ) -> Optional[Any]:
"""simple docstring"""
a__ ={}
a__ ={}
a__ ={}
# preprocess args
if "points_per_batch" in kwargs:
a__ =kwargs['''points_per_batch''']
if "points_per_crop" in kwargs:
a__ =kwargs['''points_per_crop''']
if "crops_n_layers" in kwargs:
a__ =kwargs['''crops_n_layers''']
if "crop_overlap_ratio" in kwargs:
a__ =kwargs['''crop_overlap_ratio''']
if "crop_n_points_downscale_factor" in kwargs:
a__ =kwargs['''crop_n_points_downscale_factor''']
# postprocess args
if "pred_iou_thresh" in kwargs:
a__ =kwargs['''pred_iou_thresh''']
if "stability_score_offset" in kwargs:
a__ =kwargs['''stability_score_offset''']
if "mask_threshold" in kwargs:
a__ =kwargs['''mask_threshold''']
if "stability_score_thresh" in kwargs:
a__ =kwargs['''stability_score_thresh''']
if "crops_nms_thresh" in kwargs:
a__ =kwargs['''crops_nms_thresh''']
if "output_rle_mask" in kwargs:
a__ =kwargs['''output_rle_mask''']
if "output_bboxes_mask" in kwargs:
a__ =kwargs['''output_bboxes_mask''']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self, lowercase_, *lowercase_, lowercase_=None, lowercase_=None, **lowercase_ ) -> List[Any]:
"""simple docstring"""
return super().__call__(lowercase_, *lowercase_, num_workers=lowercase_, batch_size=lowercase_, **lowercase_ )
def _UpperCAmelCase ( self, lowercase_, lowercase_=64, lowercase_ = 0, lowercase_ = 512 / 1500, lowercase_ = 32, lowercase_ = 1, ) -> Any:
"""simple docstring"""
a__ =load_image(lowercase_ )
a__ =self.image_processor.size['''longest_edge''']
a__, a__, a__, a__ =self.image_processor.generate_crop_boxes(
lowercase_, lowercase_, lowercase_, lowercase_, lowercase_, lowercase_ )
a__ =self.image_processor(images=lowercase_, return_tensors='''pt''' )
with self.device_placement():
if self.framework == "pt":
a__ =self.get_inference_context()
with inference_context():
a__ =self._ensure_tensor_on_device(lowercase_, device=self.device )
a__ =self.model.get_image_embeddings(model_inputs.pop('''pixel_values''' ) )
a__ =image_embeddings
a__ =grid_points.shape[1]
a__ =points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
'''To return all points at once, set points_per_batch to None''' )
for i in range(0, lowercase_, lowercase_ ):
a__ =grid_points[:, i : i + points_per_batch, :, :]
a__ =input_labels[:, i : i + points_per_batch]
a__ =i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def _UpperCAmelCase ( self, lowercase_, lowercase_=0.88, lowercase_=0.95, lowercase_=0, lowercase_=1, ) -> int:
"""simple docstring"""
a__ =model_inputs.pop('''input_boxes''' )
a__ =model_inputs.pop('''is_last''' )
a__ =model_inputs.pop('''original_sizes''' ).tolist()
a__ =model_inputs.pop('''reshaped_input_sizes''' ).tolist()
a__ =self.model(**lowercase_ )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
a__ =model_outputs['''pred_masks''']
a__ =self.image_processor.post_process_masks(
lowercase_, lowercase_, lowercase_, lowercase_, binarize=lowercase_ )
a__ =model_outputs['''iou_scores''']
a__, a__, a__ =self.image_processor.filter_masks(
masks[0], iou_scores[0], original_sizes[0], input_boxes[0], lowercase_, lowercase_, lowercase_, lowercase_, )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def _UpperCAmelCase ( self, lowercase_, lowercase_=False, lowercase_=False, lowercase_=0.7, ) -> Any:
"""simple docstring"""
a__ =[]
a__ =[]
a__ =[]
for model_output in model_outputs:
all_scores.append(model_output.pop('''iou_scores''' ) )
all_masks.extend(model_output.pop('''masks''' ) )
all_boxes.append(model_output.pop('''boxes''' ) )
a__ =torch.cat(lowercase_ )
a__ =torch.cat(lowercase_ )
a__, a__, a__, a__ =self.image_processor.post_process_for_mask_generation(
lowercase_, lowercase_, lowercase_, lowercase_ )
a__ =defaultdict(lowercase_ )
for output in model_outputs:
for k, v in output.items():
extra[k].append(lowercase_ )
a__ ={}
if output_rle_mask:
a__ =rle_mask
if output_bboxes_mask:
a__ =bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 188
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case_ = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
"""MEGA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegaForCausalLM""",
"""MegaForMaskedLM""",
"""MegaForMultipleChoice""",
"""MegaForQuestionAnswering""",
"""MegaForSequenceClassification""",
"""MegaForTokenClassification""",
"""MegaModel""",
"""MegaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 181
|
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer
from ...test_tokenization_common import TokenizerTesterMixin
@require_phonemizer
class A_ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase = WavaVecaPhonemeCTCTokenizer
__UpperCamelCase = False
def UpperCAmelCase__ ( self :Optional[int] ) -> int:
super().setUp()
UpperCAmelCase = (
'<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː '
'ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː '
'ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 '
'oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ '
'pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ '
'yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ '
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
'ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ '
'ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ '
'uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ '
'ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ '
'ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ '
'ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4'
).split(' ' )
UpperCAmelCase = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
UpperCAmelCase = {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}
UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowercase_ ) + '\n' )
def UpperCAmelCase__ ( self :Dict , lowercase_ :Any , lowercase_ :Union[str, Any]=False , lowercase_ :int=20 , lowercase_ :Dict=5 ) -> Tuple[str, list]:
UpperCAmelCase = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=lowercase_ )) for i in range(len(lowercase_ ) )]
UpperCAmelCase = list(filter(lambda lowercase_ : [t[0]] == tokenizer.encode(t[1] , do_phonemize=lowercase_ ) , lowercase_ ) )
if max_length is not None and len(lowercase_ ) > max_length:
UpperCAmelCase = toks[:max_length]
if min_length is not None and len(lowercase_ ) < min_length and len(lowercase_ ) > 0:
while len(lowercase_ ) < min_length:
UpperCAmelCase = toks + toks
# toks_str = [t[1] for t in toks]
UpperCAmelCase = [t[0] for t in toks]
# Ensure consistency
UpperCAmelCase = tokenizer.decode(lowercase_ , clean_up_tokenization_spaces=lowercase_ )
if " " not in output_txt and len(lowercase_ ) > 1:
UpperCAmelCase = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=lowercase_ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=lowercase_ )
)
if with_prefix_space:
UpperCAmelCase = ' ' + output_txt
UpperCAmelCase = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
return output_txt, output_ids
def UpperCAmelCase__ ( self :Union[str, Any] , **lowercase_ :Union[str, Any] ) -> Optional[Any]:
kwargs.update(self.special_tokens_map )
return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **lowercase_ )
def UpperCAmelCase__ ( self :int ) -> str:
UpperCAmelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
# check adding a single token
tokenizer.add_tokens('xxx' )
UpperCAmelCase = tokenizer('m xxx ɪ' , do_phonemize=lowercase_ ).input_ids
self.assertEqual(lowercase_ , [13, 3_92, 17] ) # xxx should be last token
tokenizer.add_tokens(['aaa', 'bbb', 'ccc'] )
UpperCAmelCase = tokenizer('m aaa ɪ ccc' , do_phonemize=lowercase_ ).input_ids
self.assertEqual(lowercase_ , [13, 3_93, 17, 3_95] ) # aaa and ccc should be after xxx and 2 after aaa
UpperCAmelCase = tokenizer('maɪ c' , do_phonemize=lowercase_ ).input_ids
self.assertEqual(lowercase_ , [3, 2_00] ) # mai should be <unk> (=3)
def UpperCAmelCase__ ( self :Tuple ) -> Union[str, Any]:
UpperCAmelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer.phonemize(lowercase_ , phonemizer_lang='en-us' )
self.assertEqual(lowercase_ , 'h ə l oʊ h aʊ ɑːɹ j uː' )
def UpperCAmelCase__ ( self :Dict ) -> int:
UpperCAmelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer.phonemize(lowercase_ , phonemizer_lang='en-us' )
self.assertEqual(tokenizer(lowercase_ ).input_ids , tokenizer(lowercase_ , do_phonemize=lowercase_ ).input_ids )
def UpperCAmelCase__ ( self :Optional[Any] ) -> Dict:
UpperCAmelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer.phonemize(lowercase_ , phonemizer_lang='en-us' )
UpperCAmelCase = tokenizer.decode(tokenizer(lowercase_ ).input_ids )
self.assertEqual(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Optional[int] ) -> str:
UpperCAmelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
UpperCAmelCase = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, 24, 22, 5, 77],
]
UpperCAmelCase = tokenizer.decode(sample_ids[0] )
UpperCAmelCase = tokenizer.batch_decode(lowercase_ )
self.assertEqual(lowercase_ , batch_tokens[0] )
self.assertEqual(lowercase_ , ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ'] )
def UpperCAmelCase__ ( self :Any ) -> str:
UpperCAmelCase = self.tokenizer_class.from_pretrained(
'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' )
tokenizer.add_tokens('|' )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer.phonemize(lowercase_ , phonemizer_lang='en-us' )
self.assertEqual(lowercase_ , 'h ə l oʊ | h aʊ | ɑːɹ | j uː |' )
def UpperCAmelCase__ ( self :Any ) -> Any:
UpperCAmelCase = self.tokenizer_class.from_pretrained(
'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' )
tokenizer.add_tokens('|' )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer.phonemize(lowercase_ , phonemizer_lang='en-us' )
self.assertEqual(tokenizer(lowercase_ ).input_ids , tokenizer(lowercase_ , do_phonemize=lowercase_ ).input_ids )
def UpperCAmelCase__ ( self :Dict ) -> Union[str, Any]:
UpperCAmelCase = self.tokenizer_class.from_pretrained(
'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' )
tokenizer.add_tokens('|' )
# fmt: off
UpperCAmelCase = [
[11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
[tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
]
# fmt: on
# decode with word_del_token filter
UpperCAmelCase = tokenizer.decode(sample_ids[0] )
UpperCAmelCase = tokenizer.batch_decode(lowercase_ )
self.assertEqual(lowercase_ , batch_tokens[0] )
self.assertEqual(lowercase_ , ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ'] )
# decode with no word_del_token filter
UpperCAmelCase = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=lowercase_ )
UpperCAmelCase = tokenizer.batch_decode(lowercase_ , filter_word_delimiter_token=lowercase_ )
self.assertEqual(lowercase_ , batch_tokens[0] )
self.assertEqual(lowercase_ , ['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ'] )
def UpperCAmelCase__ ( self :int ) -> int:
UpperCAmelCase = self.tokenizer_class.from_pretrained(
'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' )
tokenizer.add_tokens('|' )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer.phonemize(lowercase_ , phonemizer_lang='en-us' )
UpperCAmelCase = tokenizer.decode(tokenizer(lowercase_ ).input_ids , filter_word_delimiter_token=lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Optional[Any]:
UpperCAmelCase = self.tokenizer_class.from_pretrained(
'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' )
tokenizer.add_tokens('|' )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer.phonemize(lowercase_ , phonemizer_lang='en-us' )
UpperCAmelCase = tokenizer.decode(tokenizer(lowercase_ ).input_ids , filter_word_delimiter_token=lowercase_ )
self.assertEqual(' '.join([p.strip() for p in phonemes.split(' |' )] ).strip() , lowercase_ )
def UpperCAmelCase__ ( self :int ) -> Optional[Any]:
UpperCAmelCase = self.tokenizer_class.from_pretrained(
'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token=lowercase_ )
UpperCAmelCase = 'Hello how are you'
UpperCAmelCase = tokenizer(lowercase_ , phonemizer_lang='en-us' ).input_ids
UpperCAmelCase = tokenizer(lowercase_ , phonemizer_lang='fr-fr' ).input_ids
self.assertNotEqual(lowercase_ , lowercase_ )
UpperCAmelCase = tokenizer.decode(lowercase_ )
UpperCAmelCase = tokenizer.decode(lowercase_ )
self.assertEqual(lowercase_ , 'h ə l oʊ h aʊ ɑːɹ j uː' )
self.assertEqual(lowercase_ , 'ɛ l o h aʊ a ʁ j u' )
def UpperCAmelCase__ ( self :int ) -> List[Any]:
UpperCAmelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
UpperCAmelCase = 'Hello how Are you'
UpperCAmelCase = 'hello how are you'
UpperCAmelCase = tokenizer(lowercase_ ).input_ids
UpperCAmelCase = tokenizer(lowercase_ ).input_ids
self.assertEqual(lowercase_ , lowercase_ )
def UpperCAmelCase__ ( self :Optional[Any] ) -> int:
UpperCAmelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' )
tokenizer.add_tokens(['!', '?'] )
tokenizer.add_special_tokens({'cls_token': '$$$'} )
# fmt: off
UpperCAmelCase = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 3_92, 3_92, 3_93, 3_92, 3_92, 3_93, 3_94, 3_94],
[24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 3_94, 3_94],
]
# fmt: on
UpperCAmelCase = tokenizer.batch_decode(lowercase_ )
self.assertEqual(lowercase_ , ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$'] )
@staticmethod
def UpperCAmelCase__ ( lowercase_ :List[str] , lowercase_ :List[str] ) -> List[str]:
UpperCAmelCase = [d[key] for d in offsets]
return retrieved_list
def UpperCAmelCase__ ( self :str ) -> Optional[int]:
UpperCAmelCase = self.get_tokenizer(word_delimiter_token='|' )
tokenizer.add_tokens('|' )
# fmt: off
# ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
UpperCAmelCase = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
# fmt: on
UpperCAmelCase = tokenizer.decode(lowercase_ , output_char_offsets=lowercase_ , filter_word_delimiter_token=lowercase_ )
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs.keys() ) , 2 )
self.assertTrue('text' in outputs )
self.assertTrue('char_offsets' in outputs )
self.assertTrue(isinstance(lowercase_ , lowercase_ ) )
# check that order of chars is correct and identical for both outputs
self.assertEqual(' '.join(self.get_from_offsets(outputs['char_offsets'] , 'char' ) ) , outputs.text )
self.assertListEqual(
self.get_from_offsets(outputs['char_offsets'] , 'char' ) , ['k', 's', 'ɾ', 'ɾ', '|', 'ɾ', 'l', '|', 'ɭʲ'] )
# check that offsets are actually correct for char
# 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
# 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
self.assertListEqual(
self.get_from_offsets(outputs['char_offsets'] , 'start_offset' ) , [0, 1, 4, 7, 9, 11, 12, 15, 16] )
self.assertListEqual(
self.get_from_offsets(outputs['char_offsets'] , 'end_offset' ) , [1, 4, 6, 9, 10, 12, 15, 16, 17] )
def UpperCAmelCase__ ( self :Optional[int] ) -> Optional[int]:
UpperCAmelCase = self.get_tokenizer(word_delimiter_token='|' )
def check_list_tuples_equal(lowercase_ :List[Any] , lowercase_ :str ):
self.assertTrue(isinstance(lowercase_ , lowercase_ ) )
self.assertTrue(isinstance(outputs_list[0] , lowercase_ ) )
# transform list to ModelOutput
UpperCAmelCase = WavaVecaPhonemeCTCTokenizerOutput(
{k: [d[k] for d in outputs_list] for k in outputs_list[0]} )
self.assertListEqual(outputs_batch['text'] , outputs_batch_a['text'] )
def recursive_check(lowercase_ :Any , lowercase_ :str ):
if isinstance(lowercase_ , lowercase_ ):
[recursive_check(lowercase_ , lowercase_ ) for la, la in zip(lowercase_ , lowercase_ )]
self.assertEqual(lowercase_ , lowercase_ )
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch['char_offsets'] , outputs_batch_a['char_offsets'] )
# fmt: off
UpperCAmelCase = [
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
UpperCAmelCase = tokenizer.batch_decode(lowercase_ , output_char_offsets=lowercase_ )
UpperCAmelCase = [tokenizer.decode(lowercase_ , output_char_offsets=lowercase_ ) for ids in sample_ids]
check_list_tuples_equal(lowercase_ , lowercase_ )
@unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes' )
def UpperCAmelCase__ ( self :Any ) -> str:
pass
@unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes' )
def UpperCAmelCase__ ( self :str ) -> List[str]:
pass
@unittest.skip('encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency' )
def UpperCAmelCase__ ( self :List[str] ) -> int:
pass
@unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing' )
def UpperCAmelCase__ ( self :List[Any] ) -> Optional[int]:
pass
def UpperCAmelCase__ ( self :int ) -> Optional[Any]:
UpperCAmelCase = self.get_tokenizers(do_lower_case=lowercase_ )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
UpperCAmelCase = tokenizer.vocab_size
UpperCAmelCase = len(lowercase_ )
self.assertNotEqual(lowercase_ , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
UpperCAmelCase = ['aaaaa bbbbbb', 'cccccccccdddddddd']
UpperCAmelCase = tokenizer.add_tokens(lowercase_ )
UpperCAmelCase = tokenizer.vocab_size
UpperCAmelCase = len(lowercase_ )
self.assertNotEqual(lowercase_ , 0 )
self.assertEqual(lowercase_ , lowercase_ )
self.assertEqual(lowercase_ , len(lowercase_ ) )
self.assertEqual(lowercase_ , all_size + len(lowercase_ ) )
UpperCAmelCase = tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l' , add_special_tokens=lowercase_ )
self.assertGreaterEqual(len(lowercase_ ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
UpperCAmelCase = {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
UpperCAmelCase = tokenizer.add_special_tokens(lowercase_ )
UpperCAmelCase = tokenizer.vocab_size
UpperCAmelCase = len(lowercase_ )
self.assertNotEqual(lowercase_ , 0 )
self.assertEqual(lowercase_ , lowercase_ )
self.assertEqual(lowercase_ , len(lowercase_ ) )
self.assertEqual(lowercase_ , all_size_a + len(lowercase_ ) )
UpperCAmelCase = tokenizer.encode(
'>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l' , add_special_tokens=lowercase_ )
self.assertGreaterEqual(len(lowercase_ ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
@unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.' )
def UpperCAmelCase__ ( self :Tuple ) -> Optional[Any]:
pass
@unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.' )
def UpperCAmelCase__ ( self :int ) -> Any:
pass
def UpperCAmelCase__ ( self :Tuple ) -> Dict:
# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
# is not the case for Wav2Vec2PhonemeCTCTokenizer.
UpperCAmelCase = self.get_tokenizers(fast=lowercase_ , do_lower_case=lowercase_ )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
UpperCAmelCase = ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't']
UpperCAmelCase = tokenizer.convert_tokens_to_string(lowercase_ )
self.assertIsInstance(output['text'] , lowercase_ )
| 181
| 1
|
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( _lowerCamelCase: list[int] ):
return len(set(_lowerCamelCase ) ) == len(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 112
|
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: float , _lowerCamelCase: list[float] ):
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
__SCREAMING_SNAKE_CASE : List[str] = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowerCamelCase ) )
return round(_lowerCamelCase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 112
| 1
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class lowercase__ :
'''simple docstring'''
def __init__( self : Union[str, Any] , _UpperCAmelCase : int = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase_ = None
UpperCAmelCase_ = None
self.create_linked_list(_SCREAMING_SNAKE_CASE )
def lowercase__ ( self : List[Any] , _UpperCAmelCase : int ) -> None:
'''simple docstring'''
UpperCAmelCase_ = Node()
UpperCAmelCase_ = current_node
UpperCAmelCase_ = current_node
UpperCAmelCase_ = current_node
for _ in range(1 , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ = Node()
UpperCAmelCase_ = current_node
UpperCAmelCase_ = previous_node
UpperCAmelCase_ = current_node
UpperCAmelCase_ = self.front
UpperCAmelCase_ = previous_node
def lowercase__ ( self : Optional[int] ) -> bool:
'''simple docstring'''
return (
self.front == self.rear
and self.front is not None
and self.front.data is None
)
def lowercase__ ( self : List[str] ) -> Any | None:
'''simple docstring'''
self.check_can_perform_operation()
return self.front.data if self.front else None
def lowercase__ ( self : List[str] , _UpperCAmelCase : Any ) -> None:
'''simple docstring'''
if self.rear is None:
return
self.check_is_full()
if not self.is_empty():
UpperCAmelCase_ = self.rear.next
if self.rear:
UpperCAmelCase_ = data
def lowercase__ ( self : str ) -> Any:
'''simple docstring'''
self.check_can_perform_operation()
if self.rear is None or self.front is None:
return None
if self.front == self.rear:
UpperCAmelCase_ = self.front.data
UpperCAmelCase_ = None
return data
UpperCAmelCase_ = self.front
UpperCAmelCase_ = old_front.next
UpperCAmelCase_ = old_front.data
UpperCAmelCase_ = None
return data
def lowercase__ ( self : str ) -> None:
'''simple docstring'''
if self.is_empty():
raise Exception("Empty Queue" )
def lowercase__ ( self : Union[str, Any] ) -> None:
'''simple docstring'''
if self.rear and self.rear.next == self.front:
raise Exception("Full Queue" )
class lowercase__ :
'''simple docstring'''
def __init__( self : Optional[Any] ) -> None:
'''simple docstring'''
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = None
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364
|
"""simple docstring"""
def a__ ( lowerCAmelCase__ = 2000000 ):
UpperCAmelCase_ = [0 for i in range(n + 1 )]
UpperCAmelCase_ = 1
UpperCAmelCase_ = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCAmelCase__ ):
UpperCAmelCase_ = 1
UpperCAmelCase_ = 0
for i in range(lowerCAmelCase__ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F"{solution() = }")
| 241
| 0
|
def _a ( SCREAMING_SNAKE_CASE_ : int ): # noqa: E741
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = 0
__lowerCAmelCase = [0] * n
__lowerCAmelCase = [False] * n
__lowerCAmelCase = [False] * n
def dfs(SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[Any] ):
if parent == root:
out_edge_count += 1
__lowerCAmelCase = True
__lowerCAmelCase = at
for to in l[at]:
if to == parent:
pass
elif not visited[to]:
__lowerCAmelCase = dfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = min(low[at] , low[to] )
# AP found via bridge
if at < low[to]:
__lowerCAmelCase = True
# AP found via cycle
if at == low[to]:
__lowerCAmelCase = True
else:
__lowerCAmelCase = min(low[at] , SCREAMING_SNAKE_CASE_ )
return out_edge_count
for i in range(SCREAMING_SNAKE_CASE_ ):
if not visited[i]:
__lowerCAmelCase = 0
__lowerCAmelCase = dfs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , -1 , SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = out_edge_count > 1
for x in range(len(SCREAMING_SNAKE_CASE_ ) ):
if is_art[x] is True:
print(SCREAMING_SNAKE_CASE_ )
# Adjacency list of graph
UpperCamelCase__ = {
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
}
compute_ap(data)
| 92
|
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class a__ ( snake_case__ ):
def __init__( self , *_A , _A=None , _A=None , **_A ):
"""simple docstring"""
super().__init__(*_A , **_A )
__lowerCAmelCase = eval_examples
__lowerCAmelCase = post_process_function
def __SCREAMING_SNAKE_CASE( self , _A = None , _A=None , _A = None , _A = "eval" , **_A , ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
__lowerCAmelCase = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
__lowerCAmelCase = gen_kwargs
__lowerCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
__lowerCAmelCase = self.get_eval_dataloader(_A )
__lowerCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
__lowerCAmelCase = self.post_process_function(_A , _A , _A )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
else:
__lowerCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_A )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
__lowerCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , _A )
return metrics
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A=None , _A = "test" , **_A ):
"""simple docstring"""
__lowerCAmelCase = gen_kwargs.copy()
__lowerCAmelCase = self.get_test_dataloader(_A )
# Temporarily disable metric computation, we will do it in the loop here.
__lowerCAmelCase = self.compute_metrics
__lowerCAmelCase = None
__lowerCAmelCase = time.time()
__lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__lowerCAmelCase = eval_loop(
_A , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_A , metric_key_prefix=_A , )
finally:
__lowerCAmelCase = compute_metrics
__lowerCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[f"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
_A , _A , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
__lowerCAmelCase = self.post_process_function(_A , _A , _A , "predict" )
__lowerCAmelCase = self.compute_metrics(_A )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"""{metric_key_prefix}_""" ):
__lowerCAmelCase = metrics.pop(_A )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_A )
| 92
| 1
|
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowercase :
"""simple docstring"""
def __init__( self ,a_ ,a_=2 ,a_=3 ,a_=4 ,a_=2 ,a_=7 ,a_=True ,a_=True ,a_=True ,a_=True ,a_=99 ,a_=36 ,a_=2 ,a_=4 ,a_=37 ,a_="gelu" ,a_=0.1 ,a_=0.1 ,a_=512 ,a_=16 ,a_=2 ,a_=0.02 ,a_=6 ,a_=6 ,a_=3 ,a_=4 ,a_=None ,a_=1_000 ,) -> Dict:
_UpperCAmelCase : int = parent
_UpperCAmelCase : Dict = batch_size
_UpperCAmelCase : Any = num_channels
_UpperCAmelCase : List[str] = image_size
_UpperCAmelCase : int = patch_size
_UpperCAmelCase : List[Any] = is_training
_UpperCAmelCase : List[str] = use_input_mask
_UpperCAmelCase : int = use_token_type_ids
_UpperCAmelCase : int = use_labels
_UpperCAmelCase : Optional[int] = vocab_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : List[str] = num_attention_heads
_UpperCAmelCase : Dict = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : Optional[int] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : Any = max_position_embeddings
_UpperCAmelCase : int = type_vocab_size
_UpperCAmelCase : Dict = type_sequence_label_size
_UpperCAmelCase : Optional[Any] = initializer_range
_UpperCAmelCase : Dict = coordinate_size
_UpperCAmelCase : Union[str, Any] = shape_size
_UpperCAmelCase : str = num_labels
_UpperCAmelCase : Tuple = num_choices
_UpperCAmelCase : int = scope
_UpperCAmelCase : str = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
_UpperCAmelCase : int = text_seq_length
_UpperCAmelCase : List[str] = (image_size // patch_size) ** 2 + 1
_UpperCAmelCase : List[str] = self.text_seq_length + self.image_seq_length
def _snake_case ( self ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.text_seq_length] ,self.vocab_size )
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.text_seq_length, 4] ,self.range_bbox )
_UpperCAmelCase : Dict = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_UpperCAmelCase : int = bbox[i, j, 3]
_UpperCAmelCase : Optional[int] = bbox[i, j, 1]
_UpperCAmelCase : Optional[int] = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
_UpperCAmelCase : Dict = bbox[i, j, 2]
_UpperCAmelCase : str = bbox[i, j, 0]
_UpperCAmelCase : str = tmp_coordinate
_UpperCAmelCase : List[str] = tf.constant(a_ )
_UpperCAmelCase : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : int = None
if self.use_input_mask:
_UpperCAmelCase : List[str] = random_attention_mask([self.batch_size, self.text_seq_length] )
_UpperCAmelCase : str = None
if self.use_token_type_ids:
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.text_seq_length] ,self.type_vocab_size )
_UpperCAmelCase : Union[str, Any] = None
_UpperCAmelCase : List[str] = None
if self.use_labels:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] ,self.num_labels )
_UpperCAmelCase : str = LayoutLMvaConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,coordinate_size=self.coordinate_size ,shape_size=self.shape_size ,input_size=self.image_size ,patch_size=self.patch_size ,)
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : Tuple = TFLayoutLMvaModel(config=a_ )
# text + image
_UpperCAmelCase : Any = model(a_ ,pixel_values=a_ ,training=a_ )
_UpperCAmelCase : Tuple = model(
a_ ,bbox=a_ ,pixel_values=a_ ,attention_mask=a_ ,token_type_ids=a_ ,training=a_ ,)
_UpperCAmelCase : int = model(a_ ,bbox=a_ ,pixel_values=a_ ,training=a_ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
# text only
_UpperCAmelCase : Optional[Any] = model(a_ ,training=a_ )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
_UpperCAmelCase : Optional[Any] = model({"""pixel_values""": pixel_values} ,training=a_ )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.image_seq_length, self.hidden_size) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Any:
_UpperCAmelCase : Optional[Any] = self.num_labels
_UpperCAmelCase : Union[str, Any] = TFLayoutLMvaForSequenceClassification(config=a_ )
_UpperCAmelCase : List[Any] = model(
a_ ,bbox=a_ ,pixel_values=a_ ,attention_mask=a_ ,token_type_ids=a_ ,labels=a_ ,training=a_ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Optional[Any]:
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : List[Any] = TFLayoutLMvaForTokenClassification(config=a_ )
_UpperCAmelCase : Any = model(
a_ ,bbox=a_ ,pixel_values=a_ ,attention_mask=a_ ,token_type_ids=a_ ,labels=a_ ,training=a_ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.text_seq_length, self.num_labels) )
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ ) -> Optional[int]:
_UpperCAmelCase : str = 2
_UpperCAmelCase : Tuple = TFLayoutLMvaForQuestionAnswering(config=a_ )
_UpperCAmelCase : Union[str, Any] = model(
a_ ,bbox=a_ ,pixel_values=a_ ,attention_mask=a_ ,token_type_ids=a_ ,start_positions=a_ ,end_positions=a_ ,training=a_ ,)
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : int = self.prepare_config_and_inputs()
((_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase) ,(_UpperCAmelCase)) : Optional[Any] = config_and_inputs
_UpperCAmelCase : str = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class lowercase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
UpperCAmelCase = (
{"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
if is_tf_available()
else {}
)
UpperCAmelCase = False
UpperCAmelCase = False
UpperCAmelCase = False
def _snake_case ( self ,a_ ,a_ ,a_ ,a_ ,a_ ) -> List[Any]:
return True
def _snake_case ( self ,a_ ,a_ ,a_=False ) -> dict:
_UpperCAmelCase : Dict = copy.deepcopy(a_ )
if model_class in get_values(a_ ):
_UpperCAmelCase : Tuple = {
k: tf.tile(tf.expand_dims(a_ ,1 ) ,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(a_ ,tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(a_ ):
_UpperCAmelCase : Optional[Any] = tf.ones(self.model_tester.batch_size ,dtype=tf.intaa )
elif model_class in get_values(a_ ):
_UpperCAmelCase : List[Any] = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
_UpperCAmelCase : Dict = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
elif model_class in get_values(a_ ):
_UpperCAmelCase : Optional[Any] = tf.zeros(self.model_tester.batch_size ,dtype=tf.intaa )
elif model_class in get_values(a_ ):
_UpperCAmelCase : int = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) ,dtype=tf.intaa )
return inputs_dict
def _snake_case ( self ) -> Optional[int]:
_UpperCAmelCase : List[str] = TFLayoutLMvaModelTester(self )
_UpperCAmelCase : Dict = ConfigTester(self ,config_class=a_ ,hidden_size=37 )
def _snake_case ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> Tuple:
_UpperCAmelCase ,_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Optional[int] = model_class(a_ )
if getattr(a_ ,"""hf_compute_loss""" ,a_ ):
# The number of elements in the loss should be the same as the number of elements in the label
_UpperCAmelCase : str = self._prepare_for_class(inputs_dict.copy() ,a_ ,return_labels=a_ )
_UpperCAmelCase : Union[str, Any] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() ,reverse=a_ )[0]
]
_UpperCAmelCase : Union[str, Any] = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
_UpperCAmelCase : Dict = self._prepare_for_class(inputs_dict.copy() ,a_ ,return_labels=a_ )
_UpperCAmelCase : Optional[Any] = prepared_for_class.pop("""input_ids""" )
_UpperCAmelCase : List[Any] = model(a_ ,**a_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
_UpperCAmelCase : str = self._prepare_for_class(inputs_dict.copy() ,a_ ,return_labels=a_ )
_UpperCAmelCase : Dict = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
_UpperCAmelCase : Any = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
_UpperCAmelCase : Any = -100
_UpperCAmelCase : Union[str, Any] = tf.convert_to_tensor(a_ )
_UpperCAmelCase : Any = model(a_ ,**a_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
_UpperCAmelCase : List[Any] = self._prepare_for_class(inputs_dict.copy() ,a_ ,return_labels=a_ )
_UpperCAmelCase : List[Any] = model(a_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
_UpperCAmelCase : int = self._prepare_for_class(inputs_dict.copy() ,a_ ,return_labels=a_ )
# Get keys that were added with the _prepare_for_class function
_UpperCAmelCase : Any = prepared_for_class.keys() - inputs_dict.keys()
_UpperCAmelCase : Union[str, Any] = inspect.signature(model.call ).parameters
_UpperCAmelCase : List[str] = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
_UpperCAmelCase : str = {0: """input_ids"""}
for label_key in label_keys:
_UpperCAmelCase : str = signature_names.index(a_ )
_UpperCAmelCase : Optional[int] = label_key
_UpperCAmelCase : Dict = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
_UpperCAmelCase : Tuple = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
_UpperCAmelCase : List[Any] = prepared_for_class[value]
_UpperCAmelCase : Tuple = tuple(a_ )
# Send to model
_UpperCAmelCase : Optional[int] = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def _snake_case ( self ) -> str:
(
(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(a_ ,a_ ,a_ ,a_ ,a_ ,a_ )
def _snake_case ( self ) -> List[str]:
(
(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,
) : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : int = type
self.model_tester.create_and_check_model(a_ ,a_ ,a_ ,a_ ,a_ ,a_ )
def _snake_case ( self ) -> str:
(
(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ )
def _snake_case ( self ) -> Tuple:
(
(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,
) : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ )
def _snake_case ( self ) -> str:
(
(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,(
_UpperCAmelCase
) ,
) : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
a_ ,a_ ,a_ ,a_ ,a_ ,a_ ,a_ )
@slow
def _snake_case ( self ) -> List[str]:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Tuple = TFLayoutLMvaModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def snake_case_ ( )-> int:
'''simple docstring'''
_UpperCAmelCase : str = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _snake_case ( self ) -> str:
return LayoutLMvaImageProcessor(apply_ocr=a_ ) if is_vision_available() else None
@slow
def _snake_case ( self ) -> List[Any]:
_UpperCAmelCase : Union[str, Any] = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
_UpperCAmelCase : Optional[int] = self.default_image_processor
_UpperCAmelCase : Optional[Any] = prepare_img()
_UpperCAmelCase : int = image_processor(images=a_ ,return_tensors="""tf""" ).pixel_values
_UpperCAmelCase : Tuple = tf.constant([[1, 2]] )
_UpperCAmelCase : Tuple = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) ,axis=0 )
# forward pass
_UpperCAmelCase : Union[str, Any] = model(input_ids=a_ ,bbox=a_ ,pixel_values=a_ ,training=a_ )
# verify the logits
_UpperCAmelCase : Optional[int] = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape ,a_ )
_UpperCAmelCase : Optional[Any] = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] ,a_ ,atol=1E-4 ) )
| 349
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[Any] = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 349
| 1
|
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = coefficient_matrix.shape
_UpperCAmelCase , _UpperCAmelCase = constant_matrix.shape
if rowsa != colsa:
_UpperCAmelCase = f'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
raise ValueError(lowercase )
if colsa != 1:
_UpperCAmelCase = f'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
raise ValueError(lowercase )
if rowsa != rowsa:
_UpperCAmelCase = (
"""Coefficient and constant matrices dimensions must be nxn and nx1 but """
f'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
)
raise ValueError(lowercase )
if len(lowercase ) != rowsa:
_UpperCAmelCase = (
"""Number of initial values must be equal to number of rows in coefficient """
f'''matrix but received {len(lowercase )} and {rowsa}'''
)
raise ValueError(lowercase )
if iterations <= 0:
raise ValueError("""Iterations must be at least 1""" )
_UpperCAmelCase = np.concatenate(
(coefficient_matrix, constant_matrix) ,axis=1 )
_UpperCAmelCase , _UpperCAmelCase = table.shape
strictly_diagonally_dominant(lowercase )
# Iterates the whole matrix for given number of times
for _ in range(lowercase ):
_UpperCAmelCase = []
for row in range(lowercase ):
_UpperCAmelCase = 0
for col in range(lowercase ):
if col == row:
_UpperCAmelCase = table[row][col]
elif col == cols - 1:
_UpperCAmelCase = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
_UpperCAmelCase = (temp + val) / denom
new_val.append(lowercase )
_UpperCAmelCase = new_val
return [float(lowercase ) for i in new_val]
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = table.shape
_UpperCAmelCase = True
for i in range(0 ,lowercase ):
_UpperCAmelCase = 0
for j in range(0 ,cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError("""Coefficient matrix is not strictly diagonally dominant""" )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 289
|
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
UpperCAmelCase__ = {
"""b0""": {
"""hidden_dim""": 1_2_8_0,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 2_2_4,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_2_8_0,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 2_4_0,
"""dropout_rate""": 0.2,
"""dw_padding""": [1_6],
},
"""b2""": {
"""hidden_dim""": 1_4_0_8,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 2_6_0,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 1_6],
},
"""b3""": {
"""hidden_dim""": 1_5_3_6,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 3_0_0,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 1_8],
},
"""b4""": {
"""hidden_dim""": 1_7_9_2,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 3_8_0,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_0_4_8,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 4_5_6,
"""dropout_rate""": 0.4,
"""dw_padding""": [1_3, 2_7],
},
"""b6""": {
"""hidden_dim""": 2_3_0_4,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 5_2_8,
"""dropout_rate""": 0.5,
"""dw_padding""": [3_1],
},
"""b7""": {
"""hidden_dim""": 2_5_6_0,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 6_0_0,
"""dropout_rate""": 0.5,
"""dw_padding""": [1_8],
},
}
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = EfficientNetConfig()
_UpperCAmelCase = CONFIG_MAP[model_name]["""hidden_dim"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""width_coef"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""depth_coef"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""dropout_rate"""]
_UpperCAmelCase = CONFIG_MAP[model_name]["""dw_padding"""]
_UpperCAmelCase = """huggingface/label-files"""
_UpperCAmelCase = """imagenet-1k-id2label.json"""
_UpperCAmelCase = 10_00
_UpperCAmelCase = json.load(open(hf_hub_download(lowercase ,lowercase ,repo_type="""dataset""" ) ,"""r""" ) )
_UpperCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_UpperCAmelCase = Image.open(requests.get(lowercase ,stream=lowercase ).raw )
return im
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} ,image_mean=[0.4_85, 0.4_56, 0.4_06] ,image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] ,do_center_crop=lowercase ,)
return preprocessor
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
_UpperCAmelCase = sorted(set(lowercase ) )
_UpperCAmelCase = len(lowercase )
_UpperCAmelCase = {b: str(lowercase ) for b, i in zip(lowercase ,range(lowercase ) )}
_UpperCAmelCase = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
_UpperCAmelCase = block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
_UpperCAmelCase = {}
for item in rename_keys:
if item[0] in original_param_names:
_UpperCAmelCase = """efficientnet.""" + item[1]
_UpperCAmelCase = """classifier.weight"""
_UpperCAmelCase = """classifier.bias"""
return key_mapping
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ):
"""simple docstring"""
for key, value in tf_params.items():
if "normalization" in key:
continue
_UpperCAmelCase = key_mapping[key]
if "_conv" in key and "kernel" in key:
_UpperCAmelCase = torch.from_numpy(lowercase ).permute(3 ,2 ,0 ,1 )
elif "depthwise_kernel" in key:
_UpperCAmelCase = torch.from_numpy(lowercase ).permute(2 ,3 ,0 ,1 )
elif "kernel" in key:
_UpperCAmelCase = torch.from_numpy(np.transpose(lowercase ) )
else:
_UpperCAmelCase = torch.from_numpy(lowercase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase )
@torch.no_grad()
def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = model_classes[model_name](
include_top=lowercase ,weights="""imagenet""" ,input_tensor=lowercase ,input_shape=lowercase ,pooling=lowercase ,classes=10_00 ,classifier_activation="""softmax""" ,)
_UpperCAmelCase = original_model.trainable_variables
_UpperCAmelCase = original_model.non_trainable_variables
_UpperCAmelCase = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_UpperCAmelCase = param.numpy()
_UpperCAmelCase = list(tf_params.keys() )
# Load HuggingFace model
_UpperCAmelCase = get_efficientnet_config(lowercase )
_UpperCAmelCase = EfficientNetForImageClassification(lowercase ).eval()
_UpperCAmelCase = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
_UpperCAmelCase = rename_keys(lowercase )
replace_params(lowercase ,lowercase ,lowercase )
# Initialize preprocessor and preprocess input image
_UpperCAmelCase = convert_image_processor(lowercase )
_UpperCAmelCase = preprocessor(images=prepare_img() ,return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
_UpperCAmelCase = hf_model(**lowercase )
_UpperCAmelCase = outputs.logits.detach().numpy()
# Original model inference
_UpperCAmelCase = False
_UpperCAmelCase = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase = prepare_img().resize((image_size, image_size) ,resample=PIL.Image.NEAREST )
_UpperCAmelCase = image.img_to_array(lowercase )
_UpperCAmelCase = np.expand_dims(lowercase ,axis=0 )
_UpperCAmelCase = original_model.predict(lowercase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase ,lowercase ,atol=1E-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase ):
os.mkdir(lowercase )
# Save converted model and image processor
hf_model.save_pretrained(lowercase )
preprocessor.save_pretrained(lowercase )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
_UpperCAmelCase = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(lowercase )
hf_model.push_to_hub(lowercase )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
UpperCAmelCase__ = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 289
| 1
|
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCAmelCase: List[str] = logging.get_logger(__name__)
lowerCAmelCase: Optional[int] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
lowerCAmelCase: int = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
lowerCAmelCase: Dict = {
'facebook/blenderbot_small-90M': 5_1_2,
}
class a__( lowerCamelCase__ ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = BlenderbotSmallTokenizer
def __init__( self : Dict , __snake_case : Union[str, Any]=None , __snake_case : Dict=None , __snake_case : int="<|endoftext|>" , __snake_case : Dict="<|endoftext|>" , __snake_case : str="<|endoftext|>" , __snake_case : List[str]=False , __snake_case : str=True , **__snake_case : int , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=__snake_case , merges=__snake_case , add_prefix_space=__snake_case , trim_offsets=__snake_case , ) , bos_token=__snake_case , eos_token=__snake_case , unk_token=__snake_case , **__snake_case , )
a : Union[str, Any] = add_prefix_space
def lowercase_ ( self : Union[str, Any] , __snake_case : Optional[int] , __snake_case : int=None ):
a : Any = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase_ ( self : List[str] , __snake_case : List[int] , __snake_case : Optional[List[int]] = None ):
a : Dict = [self.sep_token_id]
a : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 96
|
'''simple docstring'''
from math import factorial, pi
def lowerCamelCase__ ( _A , _A = 30 ):
if not isinstance(_A , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(_A , _A ) or accuracy <= 0:
raise ValueError('maclaurin_sin() requires a positive int for accuracy' )
a : Dict = float(_A )
a : List[Any] = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(_A ) )
def lowerCamelCase__ ( _A , _A = 30 ):
if not isinstance(_A , (int, float) ):
raise ValueError('maclaurin_cos() requires either an int or float for theta' )
if not isinstance(_A , _A ) or accuracy <= 0:
raise ValueError('maclaurin_cos() requires a positive int for accuracy' )
a : int = float(_A )
a : str = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(_A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(1_0))
print(maclaurin_sin(-1_0))
print(maclaurin_sin(1_0, 1_5))
print(maclaurin_sin(-1_0, 1_5))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(1_0, 1_5))
print(maclaurin_cos(-1_0, 1_5))
| 96
| 1
|
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