





'''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 __a			( unittest.TestCase      ):
   __UpperCamelCase :			str						   =						MODEL_FOR_MASKED_LM_MAPPING
   __UpperCamelCase :			Dict						   =						TF_MODEL_FOR_MASKED_LM_MAPPING





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''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					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""fill-mask"""					,model="""sshleifer/tiny-distilroberta-base"""					,top_k=2					,framework="""tf"""     )
      __SCREAMING_SNAKE_CASE   				=							unmasker("""My name is <mask>"""     )
      self.assertEqual(
          nested_simplify(lowerCamelCase					,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"""},
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							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-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""",
              },
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							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-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					:      str     ):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							unmasker("""My name is <mask>"""     )
      self.assertEqual(
          nested_simplify(lowerCamelCase					,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"""},
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							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-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"""},
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							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-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"""},
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							unmasker("""My name is <mask> <mask>"""					,top_k=2     )

      self.assertEqual(
          nested_simplify(lowerCamelCase					,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					:      Union[str, Any]     ):

      '''simple docstring'''

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

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

      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""fill-mask"""					,model="""distilroberta-base"""					,top_k=2					,framework="""pt"""     )
      self.run_large_test(lowerCamelCase     )





   @slow
   @require_tf
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""fill-mask"""					,model="""distilroberta-base"""					,top_k=2					,framework="""tf"""     )
      self.run_large_test(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							unmasker("""My name is <mask>"""     )
      self.assertEqual(
          nested_simplify(lowerCamelCase     )					,[
              {"""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"""},
          ]					,)
      __SCREAMING_SNAKE_CASE   				=							unmasker("""The largest city in France is <mask>"""     )
      self.assertEqual(
          nested_simplify(lowerCamelCase     )					,[
              {
                  """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""": 1_2790,
                  """token_str""": """ Lyon""",
              },
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							unmasker("""My name is <mask>"""					,targets=[""" Patrick""", """ Clara""", """ Teven"""]					,top_k=3     )
      self.assertEqual(
          nested_simplify(lowerCamelCase     )					,[
              {"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3499, """token_str""": """ Patrick"""},
              {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 1_3606, """token_str""": """ Clara"""},
              {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2941, """token_str""": """ Te"""},
          ]					,)





   @require_torch
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""fill-mask"""					,model="""sshleifer/tiny-distilroberta-base"""					,framework="""pt"""     )
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      self.run_pipeline_test(lowerCamelCase					,[]     )





   @require_tf
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							pipeline(task="""fill-mask"""					,model="""sshleifer/tiny-distilroberta-base"""					,framework="""tf"""     )
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      self.run_pipeline_test(lowerCamelCase					,[]     )





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      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)"""     )

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





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							fill_masker.tokenizer
      __SCREAMING_SNAKE_CASE   				=							fill_masker.model

      __SCREAMING_SNAKE_CASE   				=							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     )},
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							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     )},
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							tokenizer.get_vocab()
      __SCREAMING_SNAKE_CASE   				=							sorted(vocab.keys()     )[:2]
      # Pipeline argument
      __SCREAMING_SNAKE_CASE   				=							FillMaskPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase					,targets=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							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     )},
          ]					,)
      __SCREAMING_SNAKE_CASE   				=							{vocab[el] for el in targets}
      self.assertEqual({el["""token"""] for el in outputs}					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							[tokenizer.decode([x]     ) for x in target_ids]
      self.assertEqual({el["""token_str"""] for el in outputs}					,set(lowerCamelCase     )     )

      # Call argument
      __SCREAMING_SNAKE_CASE   				=							FillMaskPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							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     )},
          ]					,)
      __SCREAMING_SNAKE_CASE   				=							{vocab[el] for el in targets}
      self.assertEqual({el["""token"""] for el in outputs}					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							[tokenizer.decode([x]     ) for x in target_ids]
      self.assertEqual({el["""token_str"""] for el in outputs}					,set(lowerCamelCase     )     )

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

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





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							FillMaskPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase					,top_k=2     )
      __SCREAMING_SNAKE_CASE   				=							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     )},
          ]					,)

      __SCREAMING_SNAKE_CASE   				=							FillMaskPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							tokenizer.get_vocab()
      __SCREAMING_SNAKE_CASE   				=							FillMaskPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase     )

      # top_k=2, ntargets=3
      __SCREAMING_SNAKE_CASE   				=							sorted(vocab.keys()     )[:3]
      __SCREAMING_SNAKE_CASE   				=							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
      __SCREAMING_SNAKE_CASE   				=							[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     ):
         __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							FillMaskPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tokenizer.get_vocab()
      # String duplicates + id duplicates
      __SCREAMING_SNAKE_CASE   				=							sorted(vocab.keys()     )[:3]
      __SCREAMING_SNAKE_CASE   				=							[targets[0], targets[1], targets[0], targets[2], targets[1]]
      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							FillMaskPipeline(model=lowerCamelCase					,tokenizer=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							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     )},
              ],
          ]					,)











import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Dict      	=		'linear'
	__magic_name__   :       Dict      	=		'cosine'
	__magic_name__   :       Optional[int]      	=		'cosine_with_restarts'
	__magic_name__   :       List[str]      	=		'polynomial'
	__magic_name__   :       Any      	=		'constant'
	__magic_name__   :       Union[str, Any]      	=		'constant_with_warmup'
	__magic_name__   :       str      	=		'piecewise_constant'






def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int = -1				)     ->						Optional[Any]:
					return LambdaLR(snake_case		,	lambda snake_case				: 1		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int = -1				)     ->						List[Any]:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1.0		,	snake_case				)				)
										return 1.0

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		str		,	snake_case   :		int = -1				)     ->						Union[str, Any]:
					__UpperCamelCase					:	Optional[Any]             =      {}
					__UpperCamelCase					:	int             =      step_rules.split(""","""				)
					for rule_str in rule_list[:-1]:
										__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      rule_str.split(""":"""				)
										__UpperCamelCase					:	int             =      int(snake_case				)
										__UpperCamelCase					:	Union[str, Any]             =      float(snake_case				)
										__UpperCamelCase					:	Optional[int]             =      value
					__UpperCamelCase					:	Dict             =      float(rule_list[-1]				)

					def create_rules_function(snake_case   :		List[str]		,	snake_case   :		Any				):
										def rule_func(snake_case   :		int				) -> float:
															__UpperCamelCase					:	Any             =      sorted(rules_dict.keys()				)
															for i, sorted_step in enumerate(snake_case				):
																				if steps < sorted_step:
																									return rules_dict[sorted_steps[i]]
															return last_lr_multiple

										return rule_func

					__UpperCamelCase					:	Tuple             =      create_rules_function(snake_case		,	snake_case				)

					return LambdaLR(snake_case		,	snake_case		,	last_epoch=snake_case				)





def 				A__ (snake_case   :		int		,	snake_case   :		Optional[Any]		,	snake_case   :		Union[str, Any]		,	snake_case   :		str=-1				)     ->						str:

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										return max(
										    0.0		,	float(num_training_steps - current_step				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		float = 0.5		,	snake_case   :		int = -1				)     ->						List[str]:

					def lr_lambda(snake_case   :		Dict				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	Dict             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * float(snake_case				) * 2.0 * progress				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		Optimizer		,	snake_case   :		int		,	snake_case   :		int		,	snake_case   :		int = 1		,	snake_case   :		int = -1				)     ->						Tuple:

					def lr_lambda(snake_case   :		Optional[int]				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										__UpperCamelCase					:	List[str]             =      float(current_step - num_warmup_steps				) / float(max(1		,	num_training_steps - num_warmup_steps				)				)
										if progress >= 1.0:
															return 0.0
										return max(0.0		,	0.5 * (1.0 + math.cos(math.pi * ((float(snake_case				) * progress) % 1.0)				))				)

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)





def 				A__ (snake_case   :		List[str]		,	snake_case   :		Optional[Any]		,	snake_case   :		Optional[int]		,	snake_case   :		str=1e-7		,	snake_case   :		List[str]=1.0		,	snake_case   :		Dict=-1				)     ->						Tuple:
					__UpperCamelCase					:	Tuple             =      optimizer.defaults["""lr"""]
					if not (lr_init > lr_end):
										raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''				)

					def lr_lambda(snake_case   :		int				):
										if current_step < num_warmup_steps:
															return float(snake_case				) / float(max(1		,	snake_case				)				)
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															__UpperCamelCase					:	List[str]             =      lr_init - lr_end
															__UpperCamelCase					:	Any             =      num_training_steps - num_warmup_steps
															__UpperCamelCase					:	List[str]             =      1 - (current_step - num_warmup_steps) / decay_steps
															__UpperCamelCase					:	List[Any]             =      lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(snake_case		,	snake_case		,	snake_case				)


a__		     =     {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 				A__ (snake_case   :		Union[str, SchedulerType]		,	snake_case   :		Optimizer		,	snake_case   :		Optional[str] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		Optional[int] = None		,	snake_case   :		int = 1		,	snake_case   :		float = 1.0		,	snake_case   :		int = -1		,	)     ->						Dict:
					__UpperCamelCase					:	List[str]             =      SchedulerType(snake_case				)
					__UpperCamelCase					:	int             =      TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(snake_case		,	last_epoch=snake_case				)

					if name == SchedulerType.PIECEWISE_CONSTANT:
										return schedule_func(snake_case		,	step_rules=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_warmup_steps`
					if num_warmup_steps is None:
										raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''				)

					if name == SchedulerType.CONSTANT_WITH_WARMUP:
										return schedule_func(snake_case		,	num_warmup_steps=snake_case		,	last_epoch=snake_case				)

					# All other schedulers require `num_training_steps`
					if num_training_steps is None:
										raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''				)

					if name == SchedulerType.COSINE_WITH_RESTARTS:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	num_cycles=snake_case		,	last_epoch=snake_case		,	)

					if name == SchedulerType.POLYNOMIAL:
										return schedule_func(
										    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	power=snake_case		,	last_epoch=snake_case		,	)

					return schedule_func(
					    snake_case		,	num_warmup_steps=snake_case		,	num_training_steps=snake_case		,	last_epoch=snake_case				)






