


from __future__ import annotations

from typing import Any

class 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			None:




									"""simple docstring"""





									_UpperCAmelCase											=     num_of_nodes
									_UpperCAmelCase											=     []
									_UpperCAmelCase											=     {}
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			None:




									"""simple docstring"""





									self.m_edges.append([u_node, v_node, weight]		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									if self.m_component[u_node] == u_node:
																return u_node
									return self.find_component(self.m_component[u_node]		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			None:




									"""simple docstring"""





									if self.m_component[u_node] != u_node:
																for k in self.m_component:
																							_UpperCAmelCase											=     self.find_component(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			None:




									"""simple docstring"""





									if component_size[u_node] <= component_size[v_node]:
																_UpperCAmelCase											=     v_node
																component_size[v_node] += component_size[u_node]
																self.set_component(_SCREAMING_SNAKE_CASE		)

									elif component_size[u_node] >= component_size[v_node]:
																_UpperCAmelCase											=     self.find_component(_SCREAMING_SNAKE_CASE		)
																component_size[u_node] += component_size[v_node]
																self.set_component(_SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self		)					->			None:




									"""simple docstring"""





									_UpperCAmelCase											=     []
									_UpperCAmelCase											=     0

									_UpperCAmelCase											=     [-1] * self.m_num_of_nodes

									# A list of components (initialized to all of the nodes)
									for node in range(self.m_num_of_nodes		):
																self.m_component.update({node: node}		)
																component_size.append(1		)

									_UpperCAmelCase											=     self.m_num_of_nodes

									while num_of_components > 1:
																for edge in self.m_edges:
																							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     edge

																							_UpperCAmelCase											=     self.m_component[u]
																							_UpperCAmelCase											=     self.m_component[v]

																							if u_component != v_component:

																														for component in (u_component, v_component):
																																					if (
																																					    minimum_weight_edge[component] == -1
																																					    or minimum_weight_edge[component][2] > w
																																					):
																																												_UpperCAmelCase											=     [u, v, w]

																for edge in minimum_weight_edge:
																							if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     edge

																														_UpperCAmelCase											=     self.m_component[u]
																														_UpperCAmelCase											=     self.m_component[v]

																														if u_component != v_component:
																																					mst_weight += w
																																					self.union(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																																					print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''		)
																																					num_of_components -= 1

																_UpperCAmelCase											=     [-1] * self.m_num_of_nodes
									print(f'''The total weight of the minimal spanning tree is: {mst_weight}'''		)



def 			lowerCAmelCase__					(     )    ->						None:


							'''simple docstring'''







if __name__ == "__main__":
		import doctest

		doctest.testmod()




import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class 						__a (							unittest.TestCase     ):
		_a      :    int           =      MODEL_FOR_MASKED_LM_MAPPING
		_a      :    Tuple           =      TF_MODEL_FOR_MASKED_LM_MAPPING
		def       UpperCAmelCase__							(     self		)					->			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       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     pipeline(task='fill-mask'     ,       model='sshleifer/tiny-distilroberta-base'     ,       top_k=2     ,       framework='tf'		)
									_UpperCAmelCase											=     unmasker('My name is <mask>'		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE     ,       decimals=6		)     ,       [
									        {'sequence': 'My name is grouped', 'score': 2.1e-0_5, 'token': 38015, 'token_str': ' grouped'},
									        {'sequence': 'My name is accuser', 'score': 2.1e-0_5, 'token': 25506, 'token_str': ' accuser'},
									    ]     ,       )

									_UpperCAmelCase											=     unmasker('The largest city in France is <mask>'		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE     ,       decimals=6		)     ,       [
									        {
									            'sequence': 'The largest city in France is grouped',
									            'score': 2.1e-0_5,
									            'token': 38015,
									            'token_str': ' grouped',
									        },
									        {
									            'sequence': 'The largest city in France is accuser',
									            'score': 2.1e-0_5,
									            'token': 25506,
									            'token_str': ' accuser',
									        },
									    ]     ,       )

									_UpperCAmelCase											=     unmasker('My name is <mask>'     ,       targets=[' Patrick', ' Clara', ' Teven']     ,       top_k=3		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE     ,       decimals=6		)     ,       [
									        {'sequence': 'My name is Clara', 'score': 2e-0_5, 'token': 13606, 'token_str': ' Clara'},
									        {'sequence': 'My name is Patrick', 'score': 2e-0_5, 'token': 3499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 1.9e-0_5, 'token': 2941, 'token_str': ' Te'},
									    ]     ,       )
		@require_torch
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





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

									_UpperCAmelCase											=     unmasker('My name is <mask>'		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE     ,       decimals=6		)     ,       [
									        {'sequence': 'My name is Maul', 'score': 2.2e-0_5, 'token': 35676, 'token_str': ' Maul'},
									        {'sequence': 'My name isELS', 'score': 2.2e-0_5, 'token': 16416, 'token_str': 'ELS'},
									    ]     ,       )

									_UpperCAmelCase											=     unmasker('The largest city in France is <mask>'		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE     ,       decimals=6		)     ,       [
									        {
									            'sequence': 'The largest city in France is Maul',
									            'score': 2.2e-0_5,
									            'token': 35676,
									            'token_str': ' Maul',
									        },
									        {'sequence': 'The largest city in France isELS', 'score': 2.2e-0_5, 'token': 16416, 'token_str': 'ELS'},
									    ]     ,       )

									_UpperCAmelCase											=     unmasker('My name is <mask>'     ,       targets=[' Patrick', ' Clara', ' Teven']     ,       top_k=3		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE     ,       decimals=6		)     ,       [
									        {'sequence': 'My name is Patrick', 'score': 2.1e-0_5, 'token': 3499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 2e-0_5, 'token': 2941, 'token_str': ' Te'},
									        {'sequence': 'My name is Clara', 'score': 2e-0_5, 'token': 13606, 'token_str': ' Clara'},
									    ]     ,       )

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

									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE     ,       decimals=6		)     ,       [
									        [
									            {
									                'score': 2.2e-0_5,
									                'token': 35676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is Maul<mask></s>',
									            },
									            {'score': 2.2e-0_5, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
									        ],
									        [
									            {
									                'score': 2.2e-0_5,
									                'token': 35676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is<mask> Maul</s>',
									            },
									            {'score': 2.2e-0_5, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
									        ],
									    ]     ,       )
		@require_torch_gpu
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





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

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

									_UpperCAmelCase											=     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(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		@slow
		@require_torch
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     pipeline(task='fill-mask'     ,       model='distilroberta-base'     ,       top_k=2     ,       framework='pt'		)
									self.run_large_test(_SCREAMING_SNAKE_CASE		)
		@slow
		@require_tf
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     pipeline(task='fill-mask'     ,       model='distilroberta-base'     ,       top_k=2     ,       framework='tf'		)
									self.run_large_test(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     unmasker('My name is <mask>'		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE		)     ,       [
									        {'sequence': 'My name is John', 'score': 0.008, 'token': 610, 'token_str': ' John'},
									        {'sequence': 'My name is Chris', 'score': 0.007, 'token': 1573, 'token_str': ' Chris'},
									    ]     ,       )
									_UpperCAmelCase											=     unmasker('The largest city in France is <mask>'		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE		)     ,       [
									        {
									            'sequence': 'The largest city in France is Paris',
									            'score': 0.251,
									            'token': 2201,
									            'token_str': ' Paris',
									        },
									        {
									            'sequence': 'The largest city in France is Lyon',
									            'score': 0.214,
									            'token': 12790,
									            'token_str': ' Lyon',
									        },
									    ]     ,       )

									_UpperCAmelCase											=     unmasker('My name is <mask>'     ,       targets=[' Patrick', ' Clara', ' Teven']     ,       top_k=3		)
									self.assertEqual(
									    nested_simplify(_SCREAMING_SNAKE_CASE		)     ,       [
									        {'sequence': 'My name is Patrick', 'score': 0.005, 'token': 3499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Clara', 'score': 0.000, 'token': 13606, 'token_str': ' Clara'},
									        {'sequence': 'My name is Te', 'score': 0.000, 'token': 2941, 'token_str': ' Te'},
									    ]     ,       )
		@require_torch
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     pipeline(task='fill-mask'     ,       model='sshleifer/tiny-distilroberta-base'     ,       framework='pt'		)
									_UpperCAmelCase											=     None
									_UpperCAmelCase											=     None
									self.run_pipeline_test(_SCREAMING_SNAKE_CASE     ,       []		)
		@require_tf
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     pipeline(task='fill-mask'     ,       model='sshleifer/tiny-distilroberta-base'     ,       framework='tf'		)
									_UpperCAmelCase											=     None
									_UpperCAmelCase											=     None
									self.run_pipeline_test(_SCREAMING_SNAKE_CASE     ,       []		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									if tokenizer is None or tokenizer.mask_token_id is None:
																self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)'		)

									_UpperCAmelCase											=     FillMaskPipeline(model=_SCREAMING_SNAKE_CASE     ,       tokenizer=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     [
									    f'''This is another {tokenizer.mask_token} test''',
									]
									return fill_masker, examples
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     fill_masker.tokenizer
									_UpperCAmelCase											=     fill_masker.model

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

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

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

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

									self.run_test_top_k(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									self.run_test_targets(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									self.run_test_top_k_targets(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									self.fill_mask_with_duplicate_targets_and_top_k(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									self.fill_mask_with_multiple_masks(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





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

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

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

									# Raises with invalid
									with self.assertRaises(_SCREAMING_SNAKE_CASE		):
																_UpperCAmelCase											=     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(_SCREAMING_SNAKE_CASE		):
																							_UpperCAmelCase											=     fill_masker(f'''This is a {tokenizer.mask_token}'''     ,       targets=['']		)
																with self.assertRaises(_SCREAMING_SNAKE_CASE		):
																							_UpperCAmelCase											=     fill_masker(f'''This is a {tokenizer.mask_token}'''     ,       targets=''		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





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

									_UpperCAmelCase											=     FillMaskPipeline(model=_SCREAMING_SNAKE_CASE     ,       tokenizer=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     fill_masker(f'''This is a {tokenizer.mask_token}'''     ,       top_k=2		)
									self.assertEqual(
									    _SCREAMING_SNAKE_CASE     ,       [
									        {'sequence': ANY(_SCREAMING_SNAKE_CASE		), 'score': ANY(_SCREAMING_SNAKE_CASE		), 'token': ANY(_SCREAMING_SNAKE_CASE		), 'token_str': ANY(_SCREAMING_SNAKE_CASE		)},
									        {'sequence': ANY(_SCREAMING_SNAKE_CASE		), 'score': ANY(_SCREAMING_SNAKE_CASE		), 'token': ANY(_SCREAMING_SNAKE_CASE		), 'token_str': ANY(_SCREAMING_SNAKE_CASE		)},
									    ]     ,       )
									self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE		)     ,       nested_simplify(_SCREAMING_SNAKE_CASE		)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     tokenizer.get_vocab()
									_UpperCAmelCase											=     FillMaskPipeline(model=_SCREAMING_SNAKE_CASE     ,       tokenizer=_SCREAMING_SNAKE_CASE		)

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

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




									"""simple docstring"""





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

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






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





									_UpperCAmelCase											=     FillMaskPipeline(model=_SCREAMING_SNAKE_CASE     ,       tokenizer=_SCREAMING_SNAKE_CASE		)

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