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       ):
 lowercase				     =   MODEL_FOR_MASKED_LM_MAPPING
 lowercase				     =   TF_MODEL_FOR_MASKED_LM_MAPPING

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Tuple:

  """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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					pipeline(task='fill-mask'   ,      model='sshleifer/tiny-distilroberta-base'   ,      top_k=2   ,      framework='tf'			)
  UpperCamelCase_					:						Optional[int] =					unmasker('My name is <mask>'			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=6			)   ,      [
          {'sequence': 'My name is grouped', 'score': 2.1e-05, 'token': 3_8_0_1_5, 'token_str': ' grouped'},
          {'sequence': 'My name is accuser', 'score': 2.1e-05, 'token': 2_5_5_0_6, 'token_str': ' accuser'},
      ]   ,      )

  UpperCamelCase_					:						Any =					unmasker('The largest city in France is <mask>'			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=6			)   ,      [
          {
              'sequence': 'The largest city in France is grouped',
              'score': 2.1e-05,
              'token': 3_8_0_1_5,
              'token_str': ' grouped',
          },
          {
              'sequence': 'The largest city in France is accuser',
              'score': 2.1e-05,
              'token': 2_5_5_0_6,
              'token_str': ' accuser',
          },
      ]   ,      )

  UpperCamelCase_					:						Any =					unmasker('My name is <mask>'   ,      targets=[' Patrick', ' Clara', ' Teven']   ,      top_k=3			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=6			)   ,      [
          {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 1_3_6_0_6, 'token_str': ' Clara'},
          {'sequence': 'My name is Patrick', 'score': 2e-05, 'token': 3_4_9_9, 'token_str': ' Patrick'},
          {'sequence': 'My name is Te', 'score': 1.9e-05, 'token': 2_9_4_1, 'token_str': ' Te'},
      ]   ,      )

 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						int:

  """simple docstring"""





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

  UpperCamelCase_					:						List[str] =					unmasker('My name is <mask>'			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=6			)   ,      [
          {'sequence': 'My name is Maul', 'score': 2.2e-05, 'token': 3_5_6_7_6, 'token_str': ' Maul'},
          {'sequence': 'My name isELS', 'score': 2.2e-05, 'token': 1_6_4_1_6, 'token_str': 'ELS'},
      ]   ,      )

  UpperCamelCase_					:						str =					unmasker('The largest city in France is <mask>'			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=6			)   ,      [
          {
              'sequence': 'The largest city in France is Maul',
              'score': 2.2e-05,
              'token': 3_5_6_7_6,
              'token_str': ' Maul',
          },
          {'sequence': 'The largest city in France isELS', 'score': 2.2e-05, 'token': 1_6_4_1_6, 'token_str': 'ELS'},
      ]   ,      )

  UpperCamelCase_					:						List[Any] =					unmasker('My name is <mask>'   ,      targets=[' Patrick', ' Clara', ' Teven']   ,      top_k=3			)
  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=6			)   ,      [
          {'sequence': 'My name is Patrick', 'score': 2.1e-05, 'token': 3_4_9_9, 'token_str': ' Patrick'},
          {'sequence': 'My name is Te', 'score': 2e-05, 'token': 2_9_4_1, 'token_str': ' Te'},
          {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 1_3_6_0_6, 'token_str': ' Clara'},
      ]   ,      )

  UpperCamelCase_					:						Optional[Any] =					unmasker('My name is <mask> <mask>'   ,      top_k=2			)

  self.assertEqual(
      nested_simplify(snake_case   ,      decimals=6			)   ,      [
          [
              {
                  'score': 2.2e-05,
                  'token': 3_5_6_7_6,
                  'token_str': ' Maul',
                  'sequence': '<s>My name is Maul<mask></s>',
              },
              {'score': 2.2e-05, 'token': 1_6_4_1_6, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
          ],
          [
              {
                  'score': 2.2e-05,
                  'token': 3_5_6_7_6,
                  'token_str': ' Maul',
                  'sequence': '<s>My name is<mask> Maul</s>',
              },
              {'score': 2.2e-05, 'token': 1_6_4_1_6, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
          ],
      ]   ,      )

 @require_torch_gpu
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					pipeline('fill-mask'   ,      model='hf-internal-testing/tiny-random-distilbert'   ,      device=0   ,      framework='pt'			)

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

  UpperCamelCase_					:						Optional[int] =					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(snake_case   ,      snake_case			)

 @slow
 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					pipeline(task='fill-mask'   ,      model='distilroberta-base'   ,      top_k=2   ,      framework='pt'			)
  self.run_large_test(snake_case			)

 @slow
 @require_tf
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					pipeline(task='fill-mask'   ,      model='distilroberta-base'   ,      top_k=2   ,      framework='tf'			)
  self.run_large_test(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					unmasker('My name is <mask>'			)
  self.assertEqual(
      nested_simplify(snake_case			)   ,      [
          {'sequence': 'My name is John', 'score': 0.008, 'token': 6_1_0, 'token_str': ' John'},
          {'sequence': 'My name is Chris', 'score': 0.007, 'token': 1_5_7_3, 'token_str': ' Chris'},
      ]   ,      )
  UpperCamelCase_					:						Any =					unmasker('The largest city in France is <mask>'			)
  self.assertEqual(
      nested_simplify(snake_case			)   ,      [
          {
              'sequence': 'The largest city in France is Paris',
              'score': 0.251,
              'token': 2_2_0_1,
              'token_str': ' Paris',
          },
          {
              'sequence': 'The largest city in France is Lyon',
              'score': 0.214,
              'token': 1_2_7_9_0,
              'token_str': ' Lyon',
          },
      ]   ,      )

  UpperCamelCase_					:						Dict =					unmasker('My name is <mask>'   ,      targets=[' Patrick', ' Clara', ' Teven']   ,      top_k=3			)
  self.assertEqual(
      nested_simplify(snake_case			)   ,      [
          {'sequence': 'My name is Patrick', 'score': 0.005, 'token': 3_4_9_9, 'token_str': ' Patrick'},
          {'sequence': 'My name is Clara', 'score': 0.000, 'token': 1_3_6_0_6, 'token_str': ' Clara'},
          {'sequence': 'My name is Te', 'score': 0.000, 'token': 2_9_4_1, 'token_str': ' Te'},
      ]   ,      )

 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					pipeline(task='fill-mask'   ,      model='sshleifer/tiny-distilroberta-base'   ,      framework='pt'			)
  UpperCamelCase_					:						List[str] =					None
  UpperCamelCase_					:						List[str] =					None
  self.run_pipeline_test(snake_case   ,      []			)

 @require_tf
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						str =					pipeline(task='fill-mask'   ,      model='sshleifer/tiny-distilroberta-base'   ,      framework='tf'			)
  UpperCamelCase_					:						int =					None
  UpperCamelCase_					:						List[Any] =					None
  self.run_pipeline_test(snake_case   ,      []			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Dict   ,      snake_case	:							int   ,      snake_case	:							Tuple			)   ->						Any:

  """simple docstring"""





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

  UpperCamelCase_					:						Dict =					FillMaskPipeline(model=snake_case   ,      tokenizer=snake_case			)
  UpperCamelCase_					:						str =					[
      f"This is another {tokenizer.mask_token} test",
  ]
  return fill_masker, examples

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Dict   ,      snake_case	:							Optional[int]			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					fill_masker.tokenizer
  UpperCamelCase_					:						Optional[int] =					fill_masker.model

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

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

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

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

  self.run_test_top_k(snake_case   ,      snake_case			)
  self.run_test_targets(snake_case   ,      snake_case			)
  self.run_test_top_k_targets(snake_case   ,      snake_case			)
  self.fill_mask_with_duplicate_targets_and_top_k(snake_case   ,      snake_case			)
  self.fill_mask_with_multiple_masks(snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Dict   ,      snake_case	:							int			)   ->						Tuple:

  """simple docstring"""





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

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

  # Score equivalence
  UpperCamelCase_					:						Optional[int] =					fill_masker(f"This is a {tokenizer.mask_token}"   ,      targets=snake_case			)
  UpperCamelCase_					:						str =					[top_mask['token_str'] for top_mask in outputs]
  UpperCamelCase_					:						List[Any] =					[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(snake_case			) == set(snake_case			):
   UpperCamelCase_					:						Optional[int] =					fill_masker(f"This is a {tokenizer.mask_token}"   ,      targets=snake_case			)
   UpperCamelCase_					:						str =					[top_mask['score'] for top_mask in unmasked_targets]
   self.assertEqual(nested_simplify(snake_case			)   ,      nested_simplify(snake_case			)			)

  # Raises with invalid
  with self.assertRaises(snake_case			):
   UpperCamelCase_					:						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(snake_case			):
    UpperCamelCase_					:						List[Any] =					fill_masker(f"This is a {tokenizer.mask_token}"   ,      targets=['']			)
   with self.assertRaises(snake_case			):
    UpperCamelCase_					:						Optional[int] =					fill_masker(f"This is a {tokenizer.mask_token}"   ,      targets=''			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Dict   ,      snake_case	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					FillMaskPipeline(model=snake_case   ,      tokenizer=snake_case   ,      top_k=2			)
  UpperCamelCase_					:						List[str] =					fill_masker(f"This is a {tokenizer.mask_token}"			)
  self.assertEqual(
      snake_case   ,      [
          {'sequence': ANY(snake_case			), 'score': ANY(snake_case			), 'token': ANY(snake_case			), 'token_str': ANY(snake_case			)},
          {'sequence': ANY(snake_case			), 'score': ANY(snake_case			), 'token': ANY(snake_case			), 'token_str': ANY(snake_case			)},
      ]   ,      )

  UpperCamelCase_					:						List[Any] =					FillMaskPipeline(model=snake_case   ,      tokenizer=snake_case			)
  UpperCamelCase_					:						List[str] =					fill_masker(f"This is a {tokenizer.mask_token}"   ,      top_k=2			)
  self.assertEqual(
      snake_case   ,      [
          {'sequence': ANY(snake_case			), 'score': ANY(snake_case			), 'token': ANY(snake_case			), 'token_str': ANY(snake_case			)},
          {'sequence': ANY(snake_case			), 'score': ANY(snake_case			), 'token': ANY(snake_case			), 'token_str': ANY(snake_case			)},
      ]   ,      )
  self.assertEqual(nested_simplify(snake_case			)   ,      nested_simplify(snake_case			)			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							List[str]   ,      snake_case	:							Any			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					tokenizer.get_vocab()
  UpperCamelCase_					:						Optional[Any] =					FillMaskPipeline(model=snake_case   ,      tokenizer=snake_case			)

  # top_k=2, ntargets=3
  UpperCamelCase_					:						int =					sorted(vocab.keys()			)[:3]
  UpperCamelCase_					:						Any =					fill_masker(f"This is a {tokenizer.mask_token}"   ,      top_k=2   ,      targets=snake_case			)

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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							List[Any]   ,      snake_case	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Any =					FillMaskPipeline(model=snake_case   ,      tokenizer=snake_case			)
  UpperCamelCase_					:						List[str] =					tokenizer.get_vocab()
  # String duplicates + id duplicates
  UpperCamelCase_					:						List[str] =					sorted(vocab.keys()			)[:3]
  UpperCamelCase_					:						Tuple =					[targets[0], targets[1], targets[0], targets[2], targets[1]]
  UpperCamelCase_					:						Dict =					fill_masker(f"My name is {tokenizer.mask_token}"   ,      targets=snake_case   ,      top_k=1_0			)

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


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Union[str, Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					FillMaskPipeline(model=snake_case   ,      tokenizer=snake_case			)

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




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



