




'''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				):
       lowercase	:    Optional[int]      						=MODEL_FOR_MASKED_LM_MAPPING
       lowercase	:    Any      						=TF_MODEL_FOR_MASKED_LM_MAPPING






       def      lowercase__     (							self  ):



              """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  ):



              """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  ):



              """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  ):



              """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  ):



              """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  ):



              """simple docstring"""







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






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=unmasker('''My name is <mask>'''  )
              self.assertEqual(
                  nested_simplify(lowerCAmelCase  ),		[
                      {'''sequence''': '''My name is John''', '''score''': 0.0_0_8, '''token''': 610, '''token_str''': ''' John'''},
                      {'''sequence''': '''My name is Chris''', '''score''': 0.0_0_7, '''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_5_1,
                          '''token''': 2_201,
                          '''token_str''': ''' Paris''',
                      },
                      {
                          '''sequence''': '''The largest city in France is Lyon''',
                          '''score''': 0.2_1_4,
                          '''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_0_5, '''token''': 3_499, '''token_str''': ''' Patrick'''},
                      {'''sequence''': '''My name is Clara''', '''score''': 0.0_0_0, '''token''': 13_606, '''token_str''': ''' Clara'''},
                      {'''sequence''': '''My name is Te''', '''score''': 0.0_0_0, '''token''': 2_941, '''token_str''': ''' Te'''},
                  ],		)






       @require_torch
       def      lowercase__     (							self  ):



              """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  ):



              """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  ):



              """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  ):



              """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  ):



              """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  ):



              """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  ):



              """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  ):



              """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  ):



              """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  )},
                      ],
                  ],		)











"""simple docstring"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




