





'''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     )},
              ],
          ]					,)












'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a   	=    logging.get_logger(__name__)

a   	=    {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   for attribute in key.split("""."""   ):
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   )

   if weight_type is not None:
      __SCREAMING_SNAKE_CASE   				=							getattr(__UpperCAmelCase				,						__UpperCAmelCase   ).shape
   else:
      __SCREAMING_SNAKE_CASE   				=							hf_pointer.shape

   assert hf_shape == value.shape, (
       f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
       f""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "weight_g":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "weight_v":
      __SCREAMING_SNAKE_CASE   				=							value
   elif weight_type == "bias":
      __SCREAMING_SNAKE_CASE   				=							value
   else:
      __SCREAMING_SNAKE_CASE   				=							value

   logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   __SCREAMING_SNAKE_CASE   				=							fairseq_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __SCREAMING_SNAKE_CASE   				=							False
      if "conv_layers" in name:
         load_conv_layer(
             __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						hf_model.config.feat_extract_norm == """group"""				,						)
         __SCREAMING_SNAKE_CASE   				=							True
      else:
         for key, mapped_key in MAPPING.items():
            __SCREAMING_SNAKE_CASE   				=							"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

            if key in name or (key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0] and not is_finetuned):
               __SCREAMING_SNAKE_CASE   				=							True
               if "*" in mapped_key:
                  __SCREAMING_SNAKE_CASE   				=							name.split(__UpperCAmelCase   )[0].split("""."""   )[-2]
                  __SCREAMING_SNAKE_CASE   				=							mapped_key.replace("""*"""				,						__UpperCAmelCase   )
               if "weight_g" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_g"""
               elif "weight_v" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight_v"""
               elif "weight" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""weight"""
               elif "bias" in name:
                  __SCREAMING_SNAKE_CASE   				=							"""bias"""
               else:
                  __SCREAMING_SNAKE_CASE   				=							None
               set_recursively(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
            continue
      if not is_used:
         unused_weights.append(__UpperCAmelCase   )

   logger.warning(f"""Unused weights: {unused_weights}"""   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							full_name.split("""conv_layers."""   )[-1]
   __SCREAMING_SNAKE_CASE   				=							name.split("""."""   )
   __SCREAMING_SNAKE_CASE   				=							int(items[0]   )
   __SCREAMING_SNAKE_CASE   				=							int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             f"""{full_name} has size {value.shape}, but"""
             f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         __SCREAMING_SNAKE_CASE   				=							value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(__UpperCAmelCase   )

@torch.no_grad()
def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase=None				,						__UpperCAmelCase=True   )    ->       Tuple:

   '''simple docstring'''







   if config_path is not None:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig.from_pretrained(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertConfig()

   if is_finetuned:
      if dict_path:
         __SCREAMING_SNAKE_CASE   				=							Dictionary.load(__UpperCAmelCase   )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __SCREAMING_SNAKE_CASE   				=							target_dict.pad_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.bos_index
         __SCREAMING_SNAKE_CASE   				=							target_dict.eos_index
         __SCREAMING_SNAKE_CASE   				=							len(target_dict.symbols   )
         __SCREAMING_SNAKE_CASE   				=							os.path.join(__UpperCAmelCase				,						"""vocab.json"""   )
         if not os.path.isdir(__UpperCAmelCase   ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCAmelCase   )   )
            return
         os.makedirs(__UpperCAmelCase				,						exist_ok=__UpperCAmelCase   )
         with open(__UpperCAmelCase				,						"""w"""				,						encoding="""utf-8"""   ) as vocab_handle:
            json.dump(target_dict.indices				,						__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							WavaVecaCTCTokenizer(
             __UpperCAmelCase				,						unk_token=target_dict.unk_word				,						pad_token=target_dict.pad_word				,						bos_token=target_dict.bos_word				,						eos_token=target_dict.eos_word				,						word_delimiter_token="""|"""				,						do_lower_case=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							True if config.feat_extract_norm == """layer""" else False
         __SCREAMING_SNAKE_CASE   				=							WavaVecaFeatureExtractor(
             feature_size=1				,						sampling_rate=16000				,						padding_value=0				,						do_normalize=__UpperCAmelCase				,						return_attention_mask=__UpperCAmelCase				,						)
         __SCREAMING_SNAKE_CASE   				=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase				,						tokenizer=__UpperCAmelCase   )
         processor.save_pretrained(__UpperCAmelCase   )

      __SCREAMING_SNAKE_CASE   				=							HubertForCTC(__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							HubertModel(__UpperCAmelCase   )

   if is_finetuned:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]				,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""   )[:-1]   )}   )
   else:
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

   __SCREAMING_SNAKE_CASE   				=							model[0].eval()

   recursively_load_weights(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )

   hf_wavavec.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    a   	=    parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





