



"""simple docstring"""







import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A    =			'▁'

A    =			get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     BertGenerationTokenizer
		lowerCAmelCase_       :	Any 							=     False
		lowerCAmelCase_       :	Optional[int] 							=     True



		def 					A_		( self  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

									super().setUp()

									A         =					BertGenerationTokenizer(snake_case      ,							keep_accents=snake_case				)
									tokenizer.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    List[str]				)			->						int:






									'''simple docstring'''

									A         =					'<s>'
									A         =					1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case				)      ,							snake_case				)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case				)      ,							snake_case				)



		def 					A_		( self  :    List[str]				)			->						List[Any]:






									'''simple docstring'''

									A         =					list(self.get_tokenizer().get_vocab().keys()				)

									self.assertEqual(vocab_keys[0]      ,							'<unk>'				)
									self.assertEqual(vocab_keys[1]      ,							'<s>'				)
									self.assertEqual(vocab_keys[-1]      ,							'<pad>'				)
									self.assertEqual(len(snake_case				)      ,							1_002				)



		def 					A_		( self  :    int				)			->						str:






									'''simple docstring'''

									self.assertEqual(self.get_tokenizer().vocab_size      ,							1_000				)



		def 					A_		( self  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									A         =					BertGenerationTokenizer(snake_case      ,							keep_accents=snake_case				)

									A         =					tokenizer.tokenize('This is a test'				)
									self.assertListEqual(snake_case      ,							['▁This', '▁is', '▁a', '▁t', 'est']				)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(snake_case				)      ,							[285, 46, 10, 170, 382]      ,							)

									A         =					tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
									self.assertListEqual(
									    snake_case      ,							[
									        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',
									        '.',
									    ]      ,							)
									A         =					tokenizer.convert_tokens_to_ids(snake_case				)
									self.assertListEqual(
									    snake_case      ,							[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,							)

									A         =					tokenizer.convert_ids_to_tokens(snake_case				)
									self.assertListEqual(
									    snake_case      ,							[
									        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>',
									        '.',
									    ]      ,							)



		@cached_property
		def 					A_		( self  :    Tuple				)			->						str:






									'''simple docstring'''

									return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'				)



		@slow
		def 					A_		( self  :    Optional[int]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					'Hello World!'
									A         =					[18_536, 2_260, 101]

									self.assertListEqual(snake_case      ,							self.big_tokenizer.encode(snake_case				)				)



		@slow
		def 					A_		( self  :    str				)			->						Tuple:






									'''simple docstring'''

									A         =					(
									    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
									    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
									)
									A         =					[
									    871,
									    419,
									    358,
									    946,
									    991,
									    2_521,
									    452,
									    358,
									    1_357,
									    387,
									    7_751,
									    3_536,
									    112,
									    985,
									    456,
									    126,
									    865,
									    938,
									    5_400,
									    5_734,
									    458,
									    1_368,
									    467,
									    786,
									    2_462,
									    5_246,
									    1_159,
									    633,
									    865,
									    4_519,
									    457,
									    582,
									    852,
									    2_557,
									    427,
									    916,
									    508,
									    405,
									    34_324,
									    497,
									    391,
									    408,
									    11_342,
									    1_244,
									    385,
									    100,
									    938,
									    985,
									    456,
									    574,
									    362,
									    12_597,
									    3_200,
									    3_129,
									    1_172,
									]

									self.assertListEqual(snake_case      ,							self.big_tokenizer.encode(snake_case				)				)



		@require_torch
		@slow
		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									import torch

									from transformers import BertGenerationConfig, BertGenerationEncoder

									# Build sequence
									A         =					list(self.big_tokenizer.get_vocab().keys()				)[:10]
									A         =					' '.join(snake_case				)
									A         =					self.big_tokenizer.encode_plus(snake_case      ,							return_tensors='pt'      ,							return_token_type_ids=snake_case				)
									A         =					self.big_tokenizer.batch_encode_plus(
									    [sequence + ' ' + sequence]      ,							return_tensors='pt'      ,							return_token_type_ids=snake_case				)

									A         =					BertGenerationConfig()
									A         =					BertGenerationEncoder(snake_case				)

									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
																model(**snake_case				)
																model(**snake_case				)



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					{'input_ids': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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], [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]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=snake_case      ,							model_name='google/bert_for_seq_generation_L-24_bbc_encoder'      ,							revision='c817d1fd1be2ffa69431227a1fe320544943d4db'      ,							)








"""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      UpperCAmelCase__		(      unittest.TestCase     ):
		lowerCAmelCase_       :	int 							=     MODEL_FOR_MASKED_LM_MAPPING
		lowerCAmelCase_       :	Tuple 							=     TF_MODEL_FOR_MASKED_LM_MAPPING



		def 					A_		( self  :    Tuple				)			->						int:






									'''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  :    int				)			->						Optional[int]:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							top_k=2      ,							framework='tf'				)
									A         =					unmasker('My name is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is grouped', 'score': 2.1E-0_5, 'token': 38_015, 'token_str': ' grouped'},
									        {'sequence': 'My name is accuser', 'score': 2.1E-0_5, 'token': 25_506, 'token_str': ' accuser'},
									    ]      ,							)

									A         =					unmasker('The largest city in France is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {
									            'sequence': 'The largest city in France is grouped',
									            'score': 2.1E-0_5,
									            'token': 38_015,
									            'token_str': ' grouped',
									        },
									        {
									            'sequence': 'The largest city in France is accuser',
									            'score': 2.1E-0_5,
									            'token': 25_506,
									            'token_str': ' accuser',
									        },
									    ]      ,							)

									A         =					unmasker('My name is <mask>'      ,							targets=[' Patrick', ' Clara', ' Teven']      ,							top_k=3				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is Clara', 'score': 2E-0_5, 'token': 13_606, 'token_str': ' Clara'},
									        {'sequence': 'My name is Patrick', 'score': 2E-0_5, 'token': 3_499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 1.9E-0_5, 'token': 2_941, 'token_str': ' Te'},
									    ]      ,							)



		@require_torch
		def 					A_		( self  :    str				)			->						int:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							top_k=2      ,							framework='pt'				)

									A         =					unmasker('My name is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is Maul', 'score': 2.2E-0_5, 'token': 35_676, 'token_str': ' Maul'},
									        {'sequence': 'My name isELS', 'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS'},
									    ]      ,							)

									A         =					unmasker('The largest city in France is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {
									            'sequence': 'The largest city in France is Maul',
									            'score': 2.2E-0_5,
									            'token': 35_676,
									            'token_str': ' Maul',
									        },
									        {'sequence': 'The largest city in France isELS', 'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS'},
									    ]      ,							)

									A         =					unmasker('My name is <mask>'      ,							targets=[' Patrick', ' Clara', ' Teven']      ,							top_k=3				)
									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        {'sequence': 'My name is Patrick', 'score': 2.1E-0_5, 'token': 3_499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 2E-0_5, 'token': 2_941, 'token_str': ' Te'},
									        {'sequence': 'My name is Clara', 'score': 2E-0_5, 'token': 13_606, 'token_str': ' Clara'},
									    ]      ,							)

									A         =					unmasker('My name is <mask> <mask>'      ,							top_k=2				)

									self.assertEqual(
									    nested_simplify(snake_case      ,							decimals=6				)      ,							[
									        [
									            {
									                'score': 2.2E-0_5,
									                'token': 35_676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is Maul<mask></s>',
									            },
									            {'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
									        ],
									        [
									            {
									                'score': 2.2E-0_5,
									                'token': 35_676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is<mask> Maul</s>',
									            },
									            {'score': 2.2E-0_5, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
									        ],
									    ]      ,							)



		@require_torch_gpu
		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					pipeline('fill-mask'      ,							model='hf-internal-testing/tiny-random-distilbert'      ,							device=0      ,							framework='pt'				)

									# convert model to fp16
									pipe.model.half()

									A         =					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(snake_case      ,							snake_case				)



		@slow
		@require_torch
		def 					A_		( self  :    List[Any]				)			->						int:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='distilroberta-base'      ,							top_k=2      ,							framework='pt'				)
									self.run_large_test(snake_case				)



		@slow
		@require_tf
		def 					A_		( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='distilroberta-base'      ,							top_k=2      ,							framework='tf'				)
									self.run_large_test(snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					unmasker('My name is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case				)      ,							[
									        {'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'},
									    ]      ,							)
									A         =					unmasker('The largest city in France is <mask>'				)
									self.assertEqual(
									    nested_simplify(snake_case				)      ,							[
									        {
									            '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',
									        },
									    ]      ,							)

									A         =					unmasker('My name is <mask>'      ,							targets=[' Patrick', ' Clara', ' Teven']      ,							top_k=3				)
									self.assertEqual(
									    nested_simplify(snake_case				)      ,							[
									        {'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]				)			->						Any:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							framework='pt'				)
									A         =					None
									A         =					None
									self.run_pipeline_test(snake_case      ,							[]				)



		@require_tf
		def 					A_		( self  :    Tuple				)			->						Dict:






									'''simple docstring'''

									A         =					pipeline(task='fill-mask'      ,							model='sshleifer/tiny-distilroberta-base'      ,							framework='tf'				)
									A         =					None
									A         =					None
									self.run_pipeline_test(snake_case      ,							[]				)



		def 					A_		( self  :    str      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[Any]				)			->						Union[str, 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)'				)

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					[
									    f"""This is another {tokenizer.mask_token} test""",
									]
									return fill_masker, examples



		def 					A_		( self  :    Any      ,							snake_case  :    Any      ,							snake_case  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					fill_masker.tokenizer
									A         =					fill_masker.model

									A         =					fill_masker(
									    f"""This is a {tokenizer.mask_token}"""      ,							)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)

									A         =					fill_masker([f"""This is a {tokenizer.mask_token}"""]				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)

									A         =					fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""]				)
									self.assertEqual(
									    snake_case      ,							[
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									    ]      ,							)

									with self.assertRaises(snake_case				):
																fill_masker([None]				)
									# No mask_token is not supported
									with self.assertRaises(snake_case				):
																fill_masker('This is'				)

									self.run_test_top_k(snake_case      ,							snake_case				)
									self.run_test_targets(snake_case      ,							snake_case				)
									self.run_test_top_k_targets(snake_case      ,							snake_case				)
									self.fill_mask_with_duplicate_targets_and_top_k(snake_case      ,							snake_case				)
									self.fill_mask_with_multiple_masks(snake_case      ,							snake_case				)



		def 					A_		( self  :    str      ,							snake_case  :    Any      ,							snake_case  :    Optional[int]				)			->						str:






									'''simple docstring'''

									A         =					tokenizer.get_vocab()
									A         =					sorted(vocab.keys()				)[:2]
									# Pipeline argument
									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case      ,							targets=snake_case				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)
									A         =					{vocab[el] for el in targets}
									self.assertEqual({el['token'] for el in outputs}      ,							snake_case				)
									A         =					[tokenizer.decode([x]				) for x in target_ids]
									self.assertEqual({el['token_str'] for el in outputs}      ,							set(snake_case				)				)

									# Call argument
									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=snake_case				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)
									A         =					{vocab[el] for el in targets}
									self.assertEqual({el['token'] for el in outputs}      ,							snake_case				)
									A         =					[tokenizer.decode([x]				) for x in target_ids]
									self.assertEqual({el['token_str'] for el in outputs}      ,							set(snake_case				)				)

									# Score equivalence
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=snake_case				)
									A         =					[top_mask['token_str'] for top_mask in outputs]
									A         =					[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(snake_case				) == set(snake_case				):
																A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=snake_case				)
																A         =					[top_mask['score'] for top_mask in unmasked_targets]
																self.assertEqual(nested_simplify(snake_case				)      ,							nested_simplify(snake_case				)				)

									# Raises with invalid
									with self.assertRaises(snake_case				):
																A         =					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(snake_case				):
																							A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=['']				)
																with self.assertRaises(snake_case				):
																							A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							targets=''				)



		def 					A_		( self  :    Any      ,							snake_case  :    Optional[Any]      ,							snake_case  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case      ,							top_k=2				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							top_k=2				)
									self.assertEqual(
									    snake_case      ,							[
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									    ]      ,							)
									self.assertEqual(nested_simplify(snake_case				)      ,							nested_simplify(snake_case				)				)



		def 					A_		( self  :    str      ,							snake_case  :    List[str]      ,							snake_case  :    Dict				)			->						Tuple:






									'''simple docstring'''

									A         =					tokenizer.get_vocab()
									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)

									# top_k=2, ntargets=3
									A         =					sorted(vocab.keys()				)[:3]
									A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							top_k=2      ,							targets=snake_case				)

									# If we use the most probably targets, and filter differently, we should still
									# have the same results
									A         =					[el['token_str'] for el in sorted(snake_case      ,							key=lambda snake_case				: x["score"]      ,							reverse=snake_case				)]
									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(snake_case				).issubset(snake_case				):
																A         =					fill_masker(f"""This is a {tokenizer.mask_token}"""      ,							top_k=3      ,							targets=snake_case				)
																# They should yield exactly the same result
																self.assertEqual(nested_simplify(snake_case				)      ,							nested_simplify(snake_case				)				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Dict				)			->						int:






									'''simple docstring'''

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)
									A         =					tokenizer.get_vocab()
									# String duplicates + id duplicates
									A         =					sorted(vocab.keys()				)[:3]
									A         =					[targets[0], targets[1], targets[0], targets[2], targets[1]]
									A         =					fill_masker(f"""My name is {tokenizer.mask_token}"""      ,							targets=snake_case      ,							top_k=10				)

									# The target list contains duplicates, so we can't output more
									# than them
									self.assertEqual(len(snake_case				)      ,							3				)



		def 					A_		( self  :    str      ,							snake_case  :    List[Any]      ,							snake_case  :    Union[str, Any]				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					FillMaskPipeline(model=snake_case      ,							tokenizer=snake_case				)

									A         =					fill_masker(
									    f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""      ,							top_k=2				)
									self.assertEqual(
									    snake_case      ,							[
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									        [
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									            {'sequence': ANY(snake_case				), 'score': ANY(snake_case				), 'token': ANY(snake_case				), 'token_str': ANY(snake_case				)},
									        ],
									    ]      ,							)



