code: 
  
  
  
  '''simple docstring'''
  
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import MarianConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
  
  
  if is_flax_available():
   import os
  
   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   a__						:			List[Any]										=			'platform'
  
   import jax
   import jax.numpy as jnp
  
   from transformers import MarianTokenizer
   from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A=None							,__A=None							,__A=None							,__A=None							,__A=None							,__A=None							,):
  
  
  
   '''simple docstring'''
  
  
  
   if attention_mask is None:
    __UpperCamelCase				      =  np.where(input_ids != config.pad_token_id							,1							,0       )
   if decoder_attention_mask is None:
    __UpperCamelCase				      =  np.where(decoder_input_ids != config.pad_token_id							,1							,0       )
   if head_mask is None:
    __UpperCamelCase				      =  np.ones((config.encoder_layers, config.encoder_attention_heads)       )
   if decoder_head_mask is None:
    __UpperCamelCase				      =  np.ones((config.decoder_layers, config.decoder_attention_heads)       )
   if cross_attn_head_mask is None:
    __UpperCamelCase				      =  np.ones((config.decoder_layers, config.decoder_attention_heads)       )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }
  class        UpperCAmelCase__  :
  
  
  
   def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=False		,	lowercase=9_9		,	lowercase=1_6		,	lowercase=2		,	lowercase=4		,	lowercase=4		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=3_2		,	lowercase=2		,	lowercase=1		,	lowercase=0		,	lowercase=0.02		,	)  ->							int:
    __UpperCamelCase				      =  parent
    __UpperCamelCase				      =  batch_size
    __UpperCamelCase				      =  seq_length
    __UpperCamelCase				      =  is_training
    __UpperCamelCase				      =  use_labels
    __UpperCamelCase				      =  vocab_size
    __UpperCamelCase				      =  hidden_size
    __UpperCamelCase				      =  num_hidden_layers
    __UpperCamelCase				      =  num_attention_heads
    __UpperCamelCase				      =  intermediate_size
    __UpperCamelCase				      =  hidden_act
    __UpperCamelCase				      =  hidden_dropout_prob
    __UpperCamelCase				      =  attention_probs_dropout_prob
    __UpperCamelCase				      =  max_position_embeddings
    __UpperCamelCase				      =  eos_token_id
    __UpperCamelCase				      =  pad_token_id
    __UpperCamelCase				      =  bos_token_id
    __UpperCamelCase				      =  initializer_range
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,	self.vocab_size			)		,	3		,	self.vocab_size			)
    __UpperCamelCase				      =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,	dtype=np.intaa			))		,	-1			)
  
    __UpperCamelCase				      =  shift_tokens_right(lowercase		,	1		,	2			)
  
    __UpperCamelCase				      =  MarianConfig(
        vocab_size=self.vocab_size		,	d_model=self.hidden_size		,	encoder_layers=self.num_hidden_layers		,	decoder_layers=self.num_hidden_layers		,	encoder_attention_heads=self.num_attention_heads		,	decoder_attention_heads=self.num_attention_heads		,	encoder_ffn_dim=self.intermediate_size		,	decoder_ffn_dim=self.intermediate_size		,	dropout=self.hidden_dropout_prob		,	attention_dropout=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	eos_token_id=self.eos_token_id		,	bos_token_id=self.bos_token_id		,	pad_token_id=self.pad_token_id		,	initializer_range=self.initializer_range		,	use_cache=lowercase		,	)
    __UpperCamelCase				      =  prepare_marian_inputs_dict(lowercase		,	lowercase		,	lowercase			)
    return config, inputs_dict
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_config_and_inputs()
    return config, inputs_dict
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Optional[Any]:
    __UpperCamelCase				      =  2_0
    __UpperCamelCase				      =  model_class_name(lowercase			)
  
    __UpperCamelCase				      =  model.encode(inputs_dict["""input_ids"""]			)
  
    __UpperCamelCase      ,		__UpperCamelCase				      =  (
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )
  
    __UpperCamelCase				      =  model.init_cache(decoder_input_ids.shape[0]		,	lowercase		,	lowercase			)
    __UpperCamelCase				      =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,	dtype="""i4"""			)
  
    __UpperCamelCase				      =  jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]		,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,	)
    __UpperCamelCase				      =  model.decode(
        decoder_input_ids[:, :-1]		,	lowercase		,	decoder_attention_mask=lowercase		,	past_key_values=lowercase		,	decoder_position_ids=lowercase		,	)
  
    __UpperCamelCase				      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,	dtype="""i4"""			)
    __UpperCamelCase				      =  model.decode(
        decoder_input_ids[:, -1:]		,	lowercase		,	decoder_attention_mask=lowercase		,	past_key_values=outputs_cache.past_key_values		,	decoder_position_ids=lowercase		,	)
  
    __UpperCamelCase				      =  model.decode(lowercase		,	lowercase			)
  
    __UpperCamelCase				      =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
    self.parent.assertTrue(diff < 1E-3		,	msg=f"Max diff is {diff}"			)
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Dict:
    __UpperCamelCase				      =  2_0
    __UpperCamelCase				      =  model_class_name(lowercase			)
  
    __UpperCamelCase				      =  model.encode(inputs_dict["""input_ids"""]			)
  
    __UpperCamelCase      ,		__UpperCamelCase				      =  (
        inputs_dict["""decoder_input_ids"""],
        inputs_dict["""decoder_attention_mask"""],
    )
  
    __UpperCamelCase				      =  jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
        ]		,	axis=-1		,	)
  
    __UpperCamelCase				      =  model.init_cache(decoder_input_ids.shape[0]		,	lowercase		,	lowercase			)
    __UpperCamelCase				      =  jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]		,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,	)
  
    __UpperCamelCase				      =  model.decode(
        decoder_input_ids[:, :-1]		,	lowercase		,	decoder_attention_mask=lowercase		,	past_key_values=lowercase		,	decoder_position_ids=lowercase		,	)
    __UpperCamelCase				      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,	dtype="""i4"""			)
    __UpperCamelCase				      =  model.decode(
        decoder_input_ids[:, -1:]		,	lowercase		,	past_key_values=outputs_cache.past_key_values		,	decoder_attention_mask=lowercase		,	decoder_position_ids=lowercase		,	)
  
    __UpperCamelCase				      =  model.decode(lowercase		,	lowercase		,	decoder_attention_mask=lowercase			)
  
    __UpperCamelCase				      =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
    self.parent.assertTrue(diff < 1E-3		,	msg=f"Max diff is {diff}"			)
  
  
  @require_flax
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase							,			UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				True
   __SCREAMING_SNAKE_CASE					      =				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   __SCREAMING_SNAKE_CASE					      =				(FlaxMarianMTModel,) if is_flax_available() else ()
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[Any]:
    __UpperCamelCase				      =  FlaxMarianModelTester(self			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[Any]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(lowercase		,	lowercase		,	lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Any:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(lowercase		,	lowercase		,	lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__			):
      __UpperCamelCase				      =  self._prepare_for_class(lowercase		,	lowercase			)
      __UpperCamelCase				      =  model_class(lowercase			)
  
      @jax.jit
      def encode_jitted(lowercase		,	lowercase=None		,	**lowercase			):
       return model.encode(input_ids=lowercase		,	attention_mask=lowercase			)
  
      with self.subTest("""JIT Enabled"""			):
       __UpperCamelCase				      =  encode_jitted(**lowercase			).to_tuple()
  
      with self.subTest("""JIT Disabled"""			):
       with jax.disable_jit():
        __UpperCamelCase				      =  encode_jitted(**lowercase			).to_tuple()
  
      self.assertEqual(len(lowercase			)		,	len(lowercase			)			)
      for jitted_output, output in zip(lowercase		,	lowercase			):
       self.assertEqual(jitted_output.shape		,	output.shape			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs_for_common()
  
    for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__			):
      __UpperCamelCase				      =  model_class(lowercase			)
      __UpperCamelCase				      =  model.encode(inputs_dict["""input_ids"""]		,	inputs_dict["""attention_mask"""]			)
  
      __UpperCamelCase				      =  {
          """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
          """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
          """encoder_outputs""": encoder_outputs,
      }
  
      @jax.jit
      def decode_jitted(lowercase		,	lowercase		,	lowercase			):
       return model.decode(
           decoder_input_ids=lowercase		,	decoder_attention_mask=lowercase		,	encoder_outputs=lowercase		,	)
  
      with self.subTest("""JIT Enabled"""			):
       __UpperCamelCase				      =  decode_jitted(**lowercase			).to_tuple()
  
      with self.subTest("""JIT Disabled"""			):
       with jax.disable_jit():
        __UpperCamelCase				      =  decode_jitted(**lowercase			).to_tuple()
  
      self.assertEqual(len(lowercase			)		,	len(lowercase			)			)
      for jitted_output, output in zip(lowercase		,	lowercase			):
       self.assertEqual(jitted_output.shape		,	output.shape			)
  
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    for model_class_name in self.all_model_classes:
     __UpperCamelCase				      =  model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""			)
     # FlaxMarianForSequenceClassification expects eos token in input_ids
     __UpperCamelCase				      =  np.ones((1, 1)			) * model.config.eos_token_id
     __UpperCamelCase				      =  model(lowercase			)
     self.assertIsNotNone(lowercase			)
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				None
   __SCREAMING_SNAKE_CASE					      =				None
  
  
  
   @classmethod
   def    __lowerCamelCase				(  cls			)  ->							None:
    __UpperCamelCase				      =  f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
    return cls
  
  
  
   @cached_property
   def    __lowerCamelCase				(  self			)  ->							List[Any]:
    return MarianTokenizer.from_pretrained(self.model_name			)
  
  
  
   @property
   def    __lowerCamelCase				(  self			)  ->							int:
    return self.tokenizer.eos_token_id
  
  
  
   @cached_property
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  FlaxMarianMTModel.from_pretrained(self.model_name			)
    self.assertEqual(model.config.decoder_start_token_id		,	model.config.pad_token_id			)
    return model
  
  
  
   def    __lowerCamelCase				(  self		,	**lowercase			)  ->							List[str]:
    __UpperCamelCase				      =  self.translate_src_text(**lowercase			)
    self.assertListEqual(self.expected_text		,	lowercase			)
  
  
  
  
   def    __lowerCamelCase				(  self		,	**lowercase			)  ->							Optional[int]:
    __UpperCamelCase				      =  self.tokenizer(self.src_text		,	padding=lowercase		,	return_tensors="""np"""		,	**lowercase			)
    __UpperCamelCase				      =  self.model.generate(
        model_inputs.input_ids		,	attention_mask=model_inputs.attention_mask		,	num_beams=2		,	max_length=1_2_8		,	).sequences
    __UpperCamelCase				      =  self.tokenizer.batch_decode(lowercase		,	skip_special_tokens=lowercase			)
    return generated_words
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''en'''
   __SCREAMING_SNAKE_CASE					      =				'''fr'''
   __SCREAMING_SNAKE_CASE					      =				[
       '''I am a small frog.''',
       '''Now I can forget the 100 words of german that I know.''',
   ]
   __SCREAMING_SNAKE_CASE					      =				[
       '''Je suis une petite grenouille.''',
       '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
   ]
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    self._assert_generated_batch_equal_expected()
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''fr'''
   __SCREAMING_SNAKE_CASE					      =				'''en'''
   __SCREAMING_SNAKE_CASE					      =				[
       '''Donnez moi le micro.''',
       '''Tom et Mary étaient assis à une table.''',  # Accents
   ]
   __SCREAMING_SNAKE_CASE					      =				[
       '''Give me the microphone.''',
       '''Tom and Mary were sitting at a table.''',
   ]
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							List[Any]:
    self._assert_generated_batch_equal_expected()
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''mt'''
   __SCREAMING_SNAKE_CASE					      =				'''en'''
   __SCREAMING_SNAKE_CASE					      =				['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
   __SCREAMING_SNAKE_CASE					      =				['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    self._assert_generated_batch_equal_expected()
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''en'''
   __SCREAMING_SNAKE_CASE					      =				'''de'''
   __SCREAMING_SNAKE_CASE					      =				[
       '''I am a small frog.''',
       '''Now I can forget the 100 words of german that I know.''',
       '''Tom asked his teacher for advice.''',
       '''That\'s how I would do it.''',
       '''Tom really admired Mary\'s courage.''',
       '''Turn around and close your eyes.''',
   ]
   __SCREAMING_SNAKE_CASE					      =				[
       '''Ich bin ein kleiner Frosch.''',
       '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
       '''Tom bat seinen Lehrer um Rat.''',
       '''So würde ich das machen.''',
       '''Tom bewunderte Marias Mut wirklich.''',
       '''Drehen Sie sich um und schließen Sie die Augen.''',
   ]
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    self._assert_generated_batch_equal_expected()
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''en'''
   __SCREAMING_SNAKE_CASE					      =				'''zh'''
   __SCREAMING_SNAKE_CASE					      =				['''My name is Wolfgang and I live in Berlin''']
   __SCREAMING_SNAKE_CASE					      =				['''我叫沃尔夫冈 我住在柏林''']
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Any:
    self._assert_generated_batch_equal_expected()
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''ru'''
   __SCREAMING_SNAKE_CASE					      =				'''fr'''
   __SCREAMING_SNAKE_CASE					      =				['''Он показал мне рукопись своей новой пьесы.''']
   __SCREAMING_SNAKE_CASE					      =				['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    self._assert_generated_batch_equal_expected()
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				'''en'''
   __SCREAMING_SNAKE_CASE					      =				'''ROMANCE'''
   __SCREAMING_SNAKE_CASE					      =				[
       '''>>fr<< Don\'t spend so much time watching TV.''',
       '''>>pt<< Your message has been sent.''',
       '''>>es<< He\'s two years older than me.''',
   ]
   __SCREAMING_SNAKE_CASE					      =				[
       '''Ne passez pas autant de temps à regarder la télé.''',
       '''A sua mensagem foi enviada.''',
       '''Es dos años más viejo que yo.''',
   ]
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							str:
    self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  a__						:			str										=			logging.get_logger(__name__)
  
  a__						:			Optional[int]										=			{
      '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',
      'mask_emb': 'masked_spec_embed',
  }
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   for attribute in key.split("""."""       ):
    __UpperCamelCase				      =  getattr(__A							,__A       )
  
   if weight_type is not None:
    __UpperCamelCase				      =  getattr(__A							,__A       ).shape
   else:
    __UpperCamelCase				      =  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":
    __UpperCamelCase				      =  value
   elif weight_type == "weight_g":
    __UpperCamelCase				      =  value
   elif weight_type == "weight_v":
    __UpperCamelCase				      =  value
   elif weight_type == "bias":
    __UpperCamelCase				      =  value
   else:
    __UpperCamelCase				      =  value
  
   logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  []
   __UpperCamelCase				      =  fairseq_model.state_dict()
  
   __UpperCamelCase				      =  hf_model.feature_extractor
  
   for name, value in fairseq_dict.items():
    __UpperCamelCase				      =  False
    if "conv_layers" in name:
     load_conv_layer(
         __A							,__A							,__A							,__A							,hf_model.config.feat_extract_norm == """group"""							,)
     __UpperCamelCase				      =  True
    else:
     for key, mapped_key in MAPPING.items():
      __UpperCamelCase				      =  mapped_key
  
      if key in name:
       __UpperCamelCase				      =  True
       if "*" in mapped_key:
        __UpperCamelCase				      =  name.split(__A       )[0].split("""."""       )[-2]
        __UpperCamelCase				      =  mapped_key.replace("""*"""							,__A       )
       if "weight_g" in name:
        __UpperCamelCase				      =  """weight_g"""
       elif "weight_v" in name:
        __UpperCamelCase				      =  """weight_v"""
       elif "weight" in name:
        __UpperCamelCase				      =  """weight"""
       elif "bias" in name:
        __UpperCamelCase				      =  """bias"""
       else:
        __UpperCamelCase				      =  None
       set_recursively(__A							,__A							,__A							,__A							,__A       )
      continue
    if not is_used:
     unused_weights.append(__A       )
  
   logger.warning(f"Unused weights: {unused_weights}"       )
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  full_name.split("""conv_layers."""       )[-1]
   __UpperCamelCase				      =  name.split("""."""       )
   __UpperCamelCase				      =  int(items[0]       )
   __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  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."
     )
     __UpperCamelCase				      =  value
     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
   else:
    unused_weights.append(__A       )
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  HubertConfig()
   __UpperCamelCase				      =  model.config
  
   __UpperCamelCase				      =  fs_config.activation_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  fs_config.attention_dropout
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  eval(fs_config.extractor_conv_feature_layers       )
   __UpperCamelCase				      =  [x[0] for x in conv_layers]
   __UpperCamelCase				      =  [x[1] for x in conv_layers]
   __UpperCamelCase				      =  [x[2] for x in conv_layers]
   __UpperCamelCase				      =  """gelu"""
   __UpperCamelCase				      =  """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   __UpperCamelCase				      =  False
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.activation_fn
   __UpperCamelCase				      =  fs_config.dropout
   __UpperCamelCase				      =  fs_config.encoder_embed_dim
   __UpperCamelCase				      =  0.02
   __UpperCamelCase				      =  fs_config.encoder_ffn_embed_dim
   __UpperCamelCase				      =  1E-5
   __UpperCamelCase				      =  0.0
   __UpperCamelCase				      =  fs_config.encoder_attention_heads
   __UpperCamelCase				      =  fs_config.conv_pos_groups
   __UpperCamelCase				      =  fs_config.conv_pos
   __UpperCamelCase				      =  len(__A       )
   __UpperCamelCase				      =  fs_config.encoder_layers
  
   return config
  
  
  
  
  
  
  
  @torch.no_grad()
  def _lowercase   (  __A							,__A=None       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  distilhubert().model.model
  
   if config_path is not None:
    __UpperCamelCase				      =  HubertConfig.from_pretrained(__A       )
   else:
    __UpperCamelCase				      =  convert_config(__A       )
   __UpperCamelCase				      =  model.eval()
  
   __UpperCamelCase				      =  WavaVecaFeatureExtractor(
       feature_size=1							,sampling_rate=16_000							,padding_value=0							,do_normalize=__A							,return_attention_mask=__A							,)
   __UpperCamelCase				      =  HubertModel(__A       )
  
   recursively_load_weights(__A							,__A       )
  
   feature_extractor.save_pretrained(__A       )
   hf_model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			List[str]										=			argparse.ArgumentParser()
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
  
  
  
  
  
  