




class        __lowerCAmelCase					:

			"""simple docstring"""







			def __init__( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       {}


			def  lowercase_       ( self				)       -> None:



							'''simple docstring'''
							print(self.vertex				)
							for i in self.vertex:
											print(lowerCamelCase__     ,		' -> '     ,		' -> '.join([str(lowerCamelCase__				) for j in self.vertex[i]]				)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> None:



							'''simple docstring'''
							# check if vertex is already present,
							if from_vertex in self.vertex:
											self.vertex[from_vertex].append(lowerCamelCase__				)
							else:
											# else make a new vertex
											__lowerCamelCase									=       [to_vertex]


			def  lowercase_       ( self				)       -> None:



							'''simple docstring'''
							# visited array for storing already visited nodes
							__lowerCamelCase									=       [False] * len(self.vertex				)

							# call the recursive helper function
							for i in range(len(self.vertex				)				):
											if not visited[i]:
															self.dfs_recursive(lowerCamelCase__     ,		lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> None:



							'''simple docstring'''
							# mark start vertex as visited
							__lowerCamelCase									=       True

							print(lowerCamelCase__     ,		end=' '				)

							# Recur for all the vertices that are adjacent to this node
							for i in self.vertex:
											if not visited[i]:
															self.dfs_recursive(lowerCamelCase__     ,		lowerCamelCase__				)


if __name__ == "__main__":
					__A								=       Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print("DFS:")
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3








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        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			MODEL_FOR_MASKED_LM_MAPPING
			snake_case_  						=			TF_MODEL_FOR_MASKED_LM_MAPPING


			def  lowercase_       ( self				)       -> str:



							'''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  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='sshleifer/tiny-distilroberta-base'     ,		top_k=2     ,		framework='tf'				)
							__lowerCamelCase									=       unmasker('My name is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is grouped', 'score': 2.1e-05, 'token': 38_015, 'token_str': ' grouped'},
							        {'sequence': 'My name is accuser', 'score': 2.1e-05, 'token': 25_506, 'token_str': ' accuser'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('The largest city in France is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {
							            'sequence': 'The largest city in France is grouped',
							            'score': 2.1e-05,
							            'token': 38_015,
							            'token_str': ' grouped',
							        },
							        {
							            'sequence': 'The largest city in France is accuser',
							            'score': 2.1e-05,
							            'token': 25_506,
							            'token_str': ' accuser',
							        },
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask>'     ,		targets=[' Patrick', ' Clara', ' Teven']     ,		top_k=3				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 13_606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Patrick', 'score': 2e-05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 1.9e-05, 'token': 2_941, 'token_str': ' Te'},
							    ]     ,		)


			@require_torch
			def  lowercase_       ( self				)       -> str:



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

							__lowerCamelCase									=       unmasker('My name is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is Maul', 'score': 2.2e-05, 'token': 35_676, 'token_str': ' Maul'},
							        {'sequence': 'My name isELS', 'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('The largest city in France is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {
							            'sequence': 'The largest city in France is Maul',
							            'score': 2.2e-05,
							            'token': 35_676,
							            'token_str': ' Maul',
							        },
							        {'sequence': 'The largest city in France isELS', 'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask>'     ,		targets=[' Patrick', ' Clara', ' Teven']     ,		top_k=3				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        {'sequence': 'My name is Patrick', 'score': 2.1e-05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 2e-05, 'token': 2_941, 'token_str': ' Te'},
							        {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 13_606, 'token_str': ' Clara'},
							    ]     ,		)

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

							self.assertEqual(
							    nested_simplify(lowerCamelCase__     ,		decimals=6				)     ,		[
							        [
							            {
							                'score': 2.2e-05,
							                'token': 35_676,
							                'token_str': ' Maul',
							                'sequence': '<s>My name is Maul<mask></s>',
							            },
							            {'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
							        ],
							        [
							            {
							                'score': 2.2e-05,
							                'token': 35_676,
							                'token_str': ' Maul',
							                'sequence': '<s>My name is<mask> Maul</s>',
							            },
							            {'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
							        ],
							    ]     ,		)


			@require_torch_gpu
			def  lowercase_       ( self				)       -> int:



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

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

							__lowerCamelCase									=       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(lowerCamelCase__     ,		lowerCamelCase__				)


			@slow
			@require_torch
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='distilroberta-base'     ,		top_k=2     ,		framework='pt'				)
							self.run_large_test(lowerCamelCase__				)


			@slow
			@require_tf
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       pipeline(task='fill-mask'     ,		model='distilroberta-base'     ,		top_k=2     ,		framework='tf'				)
							self.run_large_test(lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       unmasker('My name is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {'sequence': 'My name is John', 'score': 0.0_08, 'token': 610, 'token_str': ' John'},
							        {'sequence': 'My name is Chris', 'score': 0.0_07, 'token': 1_573, 'token_str': ' Chris'},
							    ]     ,		)
							__lowerCamelCase									=       unmasker('The largest city in France is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {
							            'sequence': 'The largest city in France is Paris',
							            'score': 0.2_51,
							            'token': 2_201,
							            'token_str': ' Paris',
							        },
							        {
							            'sequence': 'The largest city in France is Lyon',
							            'score': 0.2_14,
							            'token': 12_790,
							            'token_str': ' Lyon',
							        },
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask>'     ,		targets=[' Patrick', ' Clara', ' Teven']     ,		top_k=3				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {'sequence': 'My name is Patrick', 'score': 0.0_05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Clara', 'score': 0.0_00, 'token': 13_606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Te', 'score': 0.0_00, 'token': 2_941, 'token_str': ' Te'},
							    ]     ,		)


			@require_torch
			def  lowercase_       ( self				)       -> int:



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


			@require_tf
			def  lowercase_       ( self				)       -> str:



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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[str]:



							'''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)'				)

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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       fill_masker.tokenizer
							__lowerCamelCase									=       fill_masker.model

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

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

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

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

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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Dict:



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

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

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

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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



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

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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       tokenizer.get_vocab()
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)

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

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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



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

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






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



							'''simple docstring'''
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)

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


