


"""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 	lowercase__				(					unittest.TestCase				):
   __UpperCAmelCase							       =					MODEL_FOR_MASKED_LM_MAPPING
   __UpperCAmelCase							       =					TF_MODEL_FOR_MASKED_LM_MAPPING





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      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)  ->  Union[str, Any]:
      _lowerCamelCase	:					int   =		pipeline(task="""fill-mask"""      ,   model="""sshleifer/tiny-distilroberta-base"""      ,   top_k=2      ,   framework="""tf""")
      _lowerCamelCase	:					List[Any]   =		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""": 3_8015, """token_str""": """ grouped"""},
              {"""sequence""": """My name is accuser""", """score""": 2.1e-0_5, """token""": 2_5506, """token_str""": """ accuser"""},
          ]      ,   )

      _lowerCamelCase	:					Union[str, Any]   =		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""": 3_8015,
                  """token_str""": """ grouped""",
              },
              {
                  """sequence""": """The largest city in France is accuser""",
                  """score""": 2.1e-0_5,
                  """token""": 2_5506,
                  """token_str""": """ accuser""",
              },
          ]      ,   )

      _lowerCamelCase	:					Optional[int]   =		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""": 1_3606, """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)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		pipeline(task="""fill-mask"""      ,   model="""sshleifer/tiny-distilroberta-base"""      ,   top_k=2      ,   framework="""pt""")

      _lowerCamelCase	:					Dict   =		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""": 3_5676, """token_str""": """ Maul"""},
              {"""sequence""": """My name isELS""", """score""": 2.2e-0_5, """token""": 1_6416, """token_str""": """ELS"""},
          ]      ,   )

      _lowerCamelCase	:					Tuple   =		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""": 3_5676,
                  """token_str""": """ Maul""",
              },
              {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-0_5, """token""": 1_6416, """token_str""": """ELS"""},
          ]      ,   )

      _lowerCamelCase	:					Dict   =		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""": 1_3606, """token_str""": """ Clara"""},
          ]      ,   )

      _lowerCamelCase	:					Union[str, Any]   =		unmasker("""My name is <mask> <mask>"""      ,   top_k=2)

      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE      ,   decimals=6)      ,   [
              [
                  {
                      """score""": 2.2e-0_5,
                      """token""": 3_5676,
                      """token_str""": """ Maul""",
                      """sequence""": """<s>My name is Maul<mask></s>""",
                  },
                  {"""score""": 2.2e-0_5, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
              ],
              [
                  {
                      """score""": 2.2e-0_5,
                      """token""": 3_5676,
                      """token_str""": """ Maul""",
                      """sequence""": """<s>My name is<mask> Maul</s>""",
                  },
                  {"""score""": 2.2e-0_5, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
              ],
          ]      ,   )





   @require_torch_gpu
   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[int]   =		pipeline("""fill-mask"""      ,   model="""hf-internal-testing/tiny-random-distilbert"""      ,   device=0      ,   framework="""pt""")

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

      _lowerCamelCase	:					List[str]   =		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]:
      _lowerCamelCase	:					Any   =		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:
      _lowerCamelCase	:					Tuple   =		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)  ->  Tuple:
      _lowerCamelCase	:					Optional[int]   =		unmasker("""My name is <mask>""")
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE)      ,   [
              {"""sequence""": """My name is John""", """score""": 0.0_08, """token""": 610, """token_str""": """ John"""},
              {"""sequence""": """My name is Chris""", """score""": 0.0_07, """token""": 1573, """token_str""": """ Chris"""},
          ]      ,   )
      _lowerCamelCase	:					Tuple   =		unmasker("""The largest city in France is <mask>""")
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE)      ,   [
              {
                  """sequence""": """The largest city in France is Paris""",
                  """score""": 0.2_51,
                  """token""": 2201,
                  """token_str""": """ Paris""",
              },
              {
                  """sequence""": """The largest city in France is Lyon""",
                  """score""": 0.2_14,
                  """token""": 1_2790,
                  """token_str""": """ Lyon""",
              },
          ]      ,   )

      _lowerCamelCase	:					int   =		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""": 0.0_05, """token""": 3499, """token_str""": """ Patrick"""},
              {"""sequence""": """My name is Clara""", """score""": 0.0_00, """token""": 1_3606, """token_str""": """ Clara"""},
              {"""sequence""": """My name is Te""", """score""": 0.0_00, """token""": 2941, """token_str""": """ Te"""},
          ]      ,   )





   @require_torch
   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					int   =		pipeline(task="""fill-mask"""      ,   model="""sshleifer/tiny-distilroberta-base"""      ,   framework="""pt""")
      _lowerCamelCase	:					int   =		None
      _lowerCamelCase	:					int   =		None
      self.run_pipeline_test(SCREAMING_SNAKE_CASE      ,   [])





   @require_tf
   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Union[str, Any]   =		pipeline(task="""fill-mask"""      ,   model="""sshleifer/tiny-distilroberta-base"""      ,   framework="""tf""")
      _lowerCamelCase	:					Dict   =		None
      _lowerCamelCase	:					int   =		None
      self.run_pipeline_test(SCREAMING_SNAKE_CASE      ,   [])





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      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	:					List[Any]   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		[
          F'This is another {tokenizer.mask_token} test',
      ]
      return fill_masker, examples





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					Dict   =		fill_masker.tokenizer
      _lowerCamelCase	:					Union[str, Any]   =		fill_masker.model

      _lowerCamelCase	:					int   =		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)},
          ]      ,   )

      _lowerCamelCase	:					Optional[int]   =		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)},
          ]      ,   )

      _lowerCamelCase	:					Optional[int]   =		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)  ->  Optional[Any]:
      _lowerCamelCase	:					List[str]   =		tokenizer.get_vocab()
      _lowerCamelCase	:					Union[str, Any]   =		sorted(vocab.keys())[:2]
      # Pipeline argument
      _lowerCamelCase	:					int   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE      ,   targets=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		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)},
          ]      ,   )
      _lowerCamelCase	:					Optional[int]   =		{vocab[el] for el in targets}
      self.assertEqual({el["""token"""] for el in outputs}      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		[tokenizer.decode([x]) for x in target_ids]
      self.assertEqual({el["""token_str"""] for el in outputs}      ,   set(SCREAMING_SNAKE_CASE))

      # Call argument
      _lowerCamelCase	:					int   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		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)},
          ]      ,   )
      _lowerCamelCase	:					Dict   =		{vocab[el] for el in targets}
      self.assertEqual({el["""token"""] for el in outputs}      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		[tokenizer.decode([x]) for x in target_ids]
      self.assertEqual({el["""token_str"""] for el in outputs}      ,   set(SCREAMING_SNAKE_CASE))

      # Score equivalence
      _lowerCamelCase	:					List[str]   =		fill_masker(F'This is a {tokenizer.mask_token}'      ,   targets=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		[top_mask["""token_str"""] for top_mask in outputs]
      _lowerCamelCase	:					Dict   =		[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):
         _lowerCamelCase	:					Optional[int]   =		fill_masker(F'This is a {tokenizer.mask_token}'      ,   targets=SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[str]   =		[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):
         _lowerCamelCase	:					str   =		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):
            _lowerCamelCase	:					Union[str, Any]   =		fill_masker(F'This is a {tokenizer.mask_token}'      ,   targets=[""""""])
         with self.assertRaises(SCREAMING_SNAKE_CASE):
            _lowerCamelCase	:					List[str]   =		fill_masker(F'This is a {tokenizer.mask_token}'      ,   targets="""""")





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE      ,   top_k=2)
      _lowerCamelCase	:					Any   =		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)},
          ]      ,   )

      _lowerCamelCase	:					List[str]   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		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)  ->  str:
      _lowerCamelCase	:					Optional[int]   =		tokenizer.get_vocab()
      _lowerCamelCase	:					str   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE)

      # top_k=2, ntargets=3
      _lowerCamelCase	:					List[str]   =		sorted(vocab.keys())[:3]
      _lowerCamelCase	:					Optional[int]   =		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
      _lowerCamelCase	:					Optional[Any]   =		[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):
         _lowerCamelCase	:					Any   =		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)  ->  Any:
      _lowerCamelCase	:					Tuple   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		tokenizer.get_vocab()
      # String duplicates + id duplicates
      _lowerCamelCase	:					List[Any]   =		sorted(vocab.keys())[:3]
      _lowerCamelCase	:					str   =		[targets[0], targets[1], targets[0], targets[2], targets[1]]
      _lowerCamelCase	:					Union[str, Any]   =		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)  ->  List[Any]:
      _lowerCamelCase	:					Optional[int]   =		FillMaskPipeline(model=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		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)},
              ],
          ]      ,   )





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		len(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		len(__snake_case   )
   _lowerCamelCase	:					int   =		[[False for _ in range(m + 1   )] for _ in range(n + 1   )]
   _lowerCamelCase	:					Union[str, Any]   =		True
   for i in range(__snake_case   ):
      for j in range(m + 1   ):
         if dp[i][j]:
            if j < m and a[i].upper() == b[j]:
               _lowerCamelCase	:					Tuple   =		True
            if a[i].islower():
               _lowerCamelCase	:					Tuple   =		True
   return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

