



"""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


A    =			'true'

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=82				,      lowerCamelCase__=16			) ->							Union[str, Any]:
							set_seed(42			)
							A         =					RegressionModel()
							A         =					deepcopy(lowerCamelCase__			)
							A         =					RegressionDataset(length=lowerCamelCase__			)
							A         =					DataLoader(lowerCamelCase__				,      batch_size=lowerCamelCase__			)
							model.to(accelerator.device			)
							A      ,  A         =					accelerator.prepare(lowerCamelCase__				,      lowerCamelCase__			)
							return model, ddp_model, dataloader

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=False			) ->							Optional[int]:
							A         =					AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'			)
							A         =					load_dataset('glue'				,      'mrpc'				,      split='validation'			)

							def tokenize_function(lowerCamelCase__			):
														A         =					tokenizer(examples['sentence1']				,      examples['sentence2']				,      truncation=lowerCamelCase__				,      max_length=lowerCamelCase__			)
														return outputs

							with accelerator.main_process_first():
														A         =					dataset.map(
														    lowerCamelCase__				,      batched=lowerCamelCase__				,      remove_columns=['idx', 'sentence1', 'sentence2']				,      )

							A         =					tokenized_datasets.rename_column('label'				,      'labels'			)

							def collate_fn(lowerCamelCase__			):
														if use_longest:
																					return tokenizer.pad(lowerCamelCase__				,      padding='longest'				,      return_tensors='pt'			)
														return tokenizer.pad(lowerCamelCase__				,      padding='max_length'				,      max_length=128				,      return_tensors='pt'			)

							return DataLoader(lowerCamelCase__				,      shuffle=lowerCamelCase__				,      collate_fn=lowerCamelCase__				,      batch_size=16			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							A         =					Accelerator(dispatch_batches=lowerCamelCase__				,      split_batches=lowerCamelCase__			)
							A         =					get_dataloader(lowerCamelCase__				,      not dispatch_batches			)
							A         =					AutoModelForSequenceClassification.from_pretrained(
							    'hf-internal-testing/mrpc-bert-base-cased'				,      return_dict=lowerCamelCase__			)
							A      ,  A         =					accelerator.prepare(lowerCamelCase__				,      lowerCamelCase__			)
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							Dict:
							A         =					[]
							for batch in dataloader:
														A      ,  A         =					batch.values()
														with torch.no_grad():
																					A         =					model(lowerCamelCase__			)
																					A      ,  A         =					accelerator.gather_for_metrics((logit, target)			)
																					logits_and_targets.append((logit, target)			)
							A      ,  A         =					[], []
							for logit, targ in logits_and_targets:
														logits.append(lowerCamelCase__			)
														targs.append(lowerCamelCase__			)
							A      ,  A         =					torch.cat(lowerCamelCase__			), torch.cat(lowerCamelCase__			)
							return logits, targs

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=82				,      lowerCamelCase__=False				,      lowerCamelCase__=False				,      lowerCamelCase__=16			) ->							List[Any]:
							A      ,  A      ,  A         =					get_basic_setup(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)
							A      ,  A         =					generate_predictions(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)
							assert (
							    len(lowerCamelCase__			) == num_samples
							), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__			)}"""

def   lowerCAmelCase__			(     lowerCamelCase__ = False				,      lowerCamelCase__ = False			) ->							Optional[int]:
							A         =					evaluate.load('glue'				,      'mrpc'			)
							A      ,  A         =					get_mrpc_setup(lowerCamelCase__				,      lowerCamelCase__			)
							# First do baseline
							A      ,  A      ,  A         =					setup['no']
							model.to(lowerCamelCase__			)
							model.eval()
							for batch in dataloader:
														batch.to(lowerCamelCase__			)
														with torch.inference_mode():
																					A         =					model(**lowerCamelCase__			)
														A         =					outputs.logits.argmax(dim=-1			)
														metric.add_batch(predictions=lowerCamelCase__				,      references=batch['labels']			)
							A         =					metric.compute()

							# Then do distributed
							A      ,  A      ,  A         =					setup['ddp']
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					A         =					model(**lowerCamelCase__			)
														A         =					outputs.logits.argmax(dim=-1			)
														A         =					batch['labels']
														A      ,  A         =					accelerator.gather_for_metrics((preds, references)			)
														metric.add_batch(predictions=lowerCamelCase__				,      references=lowerCamelCase__			)
							A         =					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   lowerCAmelCase__			(     ) ->							Any:
							A         =					Accelerator(split_batches=lowerCamelCase__				,      dispatch_batches=lowerCamelCase__			)
							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(lowerCamelCase__				,      lowerCamelCase__			)
																												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]:
																					A         =					Accelerator(split_batches=lowerCamelCase__				,      dispatch_batches=lowerCamelCase__			)
																					if accelerator.is_local_main_process:
																												print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""			)
																					test_torch_metrics(lowerCamelCase__				,      99			)
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('**Test last batch is not dropped when perfectly divisible**'			)
							A         =					Accelerator()
							test_torch_metrics(lowerCamelCase__				,      512			)
							accelerator.state._reset_state()

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Tuple:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()








"""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				)},
									        ],
									    ]      ,							)



