



"""simple docstring"""







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeformableDetrImageProcessor






class      UpperCAmelCase__		(      unittest.TestCase     ):



		def __init__( self  :    Union[str, Any]      ,							snake_case  :    Dict      ,							snake_case  :    Tuple=7      ,							snake_case  :    Optional[Any]=3      ,							snake_case  :    List[str]=30      ,							snake_case  :    List[str]=400      ,							snake_case  :    List[str]=True      ,							snake_case  :    Any=None      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Any=[0.5, 0.5, 0.5]      ,							snake_case  :    int=[0.5, 0.5, 0.5]      ,							snake_case  :    Dict=True      ,							snake_case  :    Optional[Any]=1 / 255      ,							snake_case  :    Optional[int]=True      ,							)			->						Optional[Any]:






									'''simple docstring'''

									A         =					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
									A         =					parent
									A         =					batch_size
									A         =					num_channels
									A         =					min_resolution
									A         =					max_resolution
									A         =					do_resize
									A         =					size
									A         =					do_normalize
									A         =					image_mean
									A         =					image_std
									A         =					do_rescale
									A         =					rescale_factor
									A         =					do_pad



		def 					A_		( self  :    str				)			->						Any:






									'''simple docstring'''

									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}



		def 					A_		( self  :    List[str]      ,							snake_case  :    Tuple      ,							snake_case  :    Optional[Any]=False				)			->						Optional[Any]:






									'''simple docstring'''

									if not batched:
																A         =					image_inputs[0]
																if isinstance(snake_case      ,							Image.Image				):
																							A      ,  A         =					image.size
																else:
																							A      ,  A         =					image.shape[1], image.shape[2]
																if w < h:
																							A         =					int(self.size['shortest_edge'] * h / w				)
																							A         =					self.size['shortest_edge']
																elif w > h:
																							A         =					self.size['shortest_edge']
																							A         =					int(self.size['shortest_edge'] * w / h				)
																else:
																							A         =					self.size['shortest_edge']
																							A         =					self.size['shortest_edge']

									else:
																A         =					[]
																for image in image_inputs:
																							A      ,  A         =					self.get_expected_values([image]				)
																							expected_values.append((expected_height, expected_width)				)
																A         =					max(snake_case      ,							key=lambda snake_case				: item[0]				)[0]
																A         =					max(snake_case      ,							key=lambda snake_case				: item[1]				)[1]

									return expected_height, expected_width






@require_torch
@require_vision
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	List[Any] 							=     DeformableDetrImageProcessor if is_vision_available() else None



		def 					A_		( self  :    Tuple				)			->						str:






									'''simple docstring'''

									A         =					DeformableDetrImageProcessingTester(self				)



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






									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()



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






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									self.assertTrue(hasattr(snake_case      ,							'image_mean'				)				)
									self.assertTrue(hasattr(snake_case      ,							'image_std'				)				)
									self.assertTrue(hasattr(snake_case      ,							'do_normalize'				)				)
									self.assertTrue(hasattr(snake_case      ,							'do_resize'				)				)
									self.assertTrue(hasattr(snake_case      ,							'do_rescale'				)				)
									self.assertTrue(hasattr(snake_case      ,							'do_pad'				)				)
									self.assertTrue(hasattr(snake_case      ,							'size'				)				)



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






									'''simple docstring'''

									A         =					self.image_processing_class.from_dict(self.image_processor_dict				)
									self.assertEqual(image_processor.size      ,							{'shortest_edge': 18, 'longest_edge': 1_333}				)
									self.assertEqual(image_processor.do_pad      ,							snake_case				)

									A         =					self.image_processing_class.from_dict(
									    self.image_processor_dict      ,							size=42      ,							max_size=84      ,							pad_and_return_pixel_mask=snake_case				)
									self.assertEqual(image_processor.size      ,							{'shortest_edge': 42, 'longest_edge': 84}				)
									self.assertEqual(image_processor.do_pad      ,							snake_case				)



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






									'''simple docstring'''

									pass



		def 					A_		( self  :    Any				)			->						Any:






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									# create random PIL images
									A         =					prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=snake_case				)
									for image in image_inputs:
																self.assertIsInstance(snake_case      ,							Image.Image				)

									# Test not batched input
									A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)

									self.assertEqual(
									    encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

									# Test batched
									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)

									A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,							)



		def 					A_		( self  :    str				)			->						int:






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									# create random numpy tensors
									A         =					prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=snake_case      ,							numpify=snake_case				)
									for image in image_inputs:
																self.assertIsInstance(snake_case      ,							np.ndarray				)

									# Test not batched input
									A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)

									self.assertEqual(
									    encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

									# Test batched
									A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)

									self.assertEqual(
									    encoded_images.shape      ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,							)



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






									'''simple docstring'''

									A         =					self.image_processing_class(**self.image_processor_dict				)
									# create random PyTorch tensors
									A         =					prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=snake_case      ,							torchify=snake_case				)
									for image in image_inputs:
																self.assertIsInstance(snake_case      ,							torch.Tensor				)

									# Test not batched input
									A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)

									self.assertEqual(
									    encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

									# Test batched
									A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

									A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)

									self.assertEqual(
									    encoded_images.shape      ,							(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,							)



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






									'''simple docstring'''

									A         =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
									with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'      ,							'r'				) as f:
																A         =					json.loads(f.read()				)

									A         =					{'image_id': 39_769, 'annotations': target}

									# encode them
									A         =					DeformableDetrImageProcessor()
									A         =					image_processing(images=snake_case      ,							annotations=snake_case      ,							return_tensors='pt'				)

									# verify pixel values
									A         =					torch.Size([1, 3, 800, 1_066]				)
									self.assertEqual(encoding['pixel_values'].shape      ,							snake_case				)

									A         =					torch.tensor([0.2796, 0.3138, 0.3481]				)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,							snake_case      ,							atol=1E-4				)				)

									# verify area
									A         =					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,							snake_case				)				)
									# verify boxes
									A         =					torch.Size([6, 4]				)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,							snake_case				)
									A         =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,							snake_case      ,							atol=1E-3				)				)
									# verify image_id
									A         =					torch.tensor([39_769]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,							snake_case				)				)
									# verify is_crowd
									A         =					torch.tensor([0, 0, 0, 0, 0, 0]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,							snake_case				)				)
									# verify class_labels
									A         =					torch.tensor([75, 75, 63, 65, 17, 17]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,							snake_case				)				)
									# verify orig_size
									A         =					torch.tensor([480, 640]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,							snake_case				)				)
									# verify size
									A         =					torch.tensor([800, 1_066]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,							snake_case				)				)



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






									'''simple docstring'''

									A         =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,							'r'				) as f:
																A         =					json.loads(f.read()				)

									A         =					{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

									A         =					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

									# encode them
									A         =					DeformableDetrImageProcessor(format='coco_panoptic'				)
									A         =					image_processing(images=snake_case      ,							annotations=snake_case      ,							masks_path=snake_case      ,							return_tensors='pt'				)

									# verify pixel values
									A         =					torch.Size([1, 3, 800, 1_066]				)
									self.assertEqual(encoding['pixel_values'].shape      ,							snake_case				)

									A         =					torch.tensor([0.2796, 0.3138, 0.3481]				)
									self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]      ,							snake_case      ,							atol=1E-4				)				)

									# verify area
									A         =					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,							snake_case				)				)
									# verify boxes
									A         =					torch.Size([6, 4]				)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,							snake_case				)
									A         =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,							snake_case      ,							atol=1E-3				)				)
									# verify image_id
									A         =					torch.tensor([39_769]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,							snake_case				)				)
									# verify is_crowd
									A         =					torch.tensor([0, 0, 0, 0, 0, 0]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,							snake_case				)				)
									# verify class_labels
									A         =					torch.tensor([17, 17, 63, 75, 75, 93]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,							snake_case				)				)
									# verify masks
									A         =					822_873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,							snake_case				)
									# verify orig_size
									A         =					torch.tensor([480, 640]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,							snake_case				)				)
									# verify size
									A         =					torch.tensor([800, 1_066]				)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,							snake_case				)				)








"""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				)},
									        ],
									    ]      ,							)



