code: 
  
  
  
  '''simple docstring'''
  
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import MBartConfig, 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__						:			Dict										=			'platform'
  
   import jax
   import jax.numpy as jnp
  
   from transformers import AutoTokenizer
   from transformers.models.mbart.modeling_flax_mbart import (
       FlaxMBartForConditionalGeneration,
       FlaxMBartForQuestionAnswering,
       FlaxMBartForSequenceClassification,
       FlaxMBartModel,
       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": decoder_attention_mask,
   }
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
      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=2		,	lowercase=0.02		,	)  ->							Optional[Any]:
       __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				      =  decoder_start_token_id
       __UpperCamelCase				      =  initializer_range
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Optional[int]:
       __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			)
  
       __UpperCamelCase				      =  MBartConfig(
           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		,	decoder_start_token_id=self.decoder_start_token_id		,	initializer_range=self.initializer_range		,	use_cache=lowercase		,	)
       __UpperCamelCase				      =  prepare_mbart_inputs_dict(lowercase		,	lowercase		,	lowercase			)
       return config, inputs_dict
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Any:
       __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_config_and_inputs()
       return config, inputs_dict
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							List[str]:
       __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			)  ->							List[str]:
       __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__  (    unittest.TestCase):
      __SCREAMING_SNAKE_CASE					      =				9_9
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Tuple:
       __UpperCamelCase				      =  np.array(
           [
               [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
               [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
               [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
               [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
               [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
               [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
               [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
               [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
               [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
               [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
               [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
               [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
               [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
           ]		,	dtype=np.intaa		,	)
  
       __UpperCamelCase				      =  input_ids.shape[0]
       __UpperCamelCase				      =  MBartConfig(
           vocab_size=self.vocab_size		,	d_model=2_4		,	encoder_layers=2		,	decoder_layers=2		,	encoder_attention_heads=2		,	decoder_attention_heads=2		,	encoder_ffn_dim=3_2		,	decoder_ffn_dim=3_2		,	max_position_embeddings=4_8		,	eos_token_id=2		,	pad_token_id=1		,	bos_token_id=0		,	)
       return config, input_ids, batch_size
  
  
  
      def    __lowerCamelCase				(  self			)  ->							List[str]:
       __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self._get_config_and_data()
       __UpperCamelCase				      =  FlaxMBartForSequenceClassification(lowercase			)
       __UpperCamelCase				      =  model(input_ids=lowercase		,	decoder_input_ids=lowercase			)
       __UpperCamelCase				      =  (batch_size, config.num_labels)
       self.assertEqual(outputs["""logits"""].shape		,	lowercase			)
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Optional[int]:
       __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self._get_config_and_data()
       __UpperCamelCase				      =  FlaxMBartForQuestionAnswering(lowercase			)
       __UpperCamelCase				      =  model(input_ids=lowercase			)
  
       self.assertEqual(outputs["""start_logits"""].shape		,	input_ids.shape			)
       self.assertEqual(outputs["""end_logits"""].shape		,	input_ids.shape			)
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Dict:
       __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self._get_config_and_data()
       __UpperCamelCase				      =  FlaxMBartForConditionalGeneration(lowercase			)
       __UpperCamelCase				      =  lm_model(input_ids=lowercase			)
       __UpperCamelCase				      =  (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape		,	lowercase			)
  
  
  
      def    __lowerCamelCase				(  self			)  ->							List[str]:
       __UpperCamelCase				      =  MBartConfig(
           vocab_size=self.vocab_size		,	d_model=1_4		,	encoder_layers=2		,	decoder_layers=2		,	encoder_attention_heads=2		,	decoder_attention_heads=2		,	encoder_ffn_dim=8		,	decoder_ffn_dim=8		,	max_position_embeddings=4_8		,	)
       __UpperCamelCase				      =  FlaxMBartForConditionalGeneration(lowercase			)
       __UpperCamelCase				      =  np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]		,	dtype=np.intaa			)
       __UpperCamelCase				      =  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]		,	dtype=np.intaa			)
       __UpperCamelCase				      =  lm_model(input_ids=lowercase		,	decoder_input_ids=lowercase			)
       __UpperCamelCase				      =  (*summary.shape, config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape		,	lowercase			)
  
  
  
  
      def    __lowerCamelCase				(  self			)  ->							List[str]:
       __UpperCamelCase				      =  np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]		,	dtype=np.intaa			)
       __UpperCamelCase				      =  shift_tokens_right(lowercase		,	1			)
       __UpperCamelCase				      =  np.equal(lowercase		,	1			).astype(np.floataa			).sum()
       __UpperCamelCase				      =  np.equal(lowercase		,	1			).astype(np.floataa			).sum()
       self.assertEqual(shifted.shape		,	input_ids.shape			)
       self.assertEqual(lowercase		,	n_pad_before - 1			)
       self.assertTrue(np.equal(shifted[:, 0]		,	2			).all()			)
  
  
  @require_flax
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			unittest.TestCase							,			UpperCAmelCase_):
      __SCREAMING_SNAKE_CASE					      =				True
      __SCREAMING_SNAKE_CASE					      =				(
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE					      =				(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Optional[int]:
       __UpperCamelCase				      =  FlaxMBartModelTester(self			)
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Dict:
       __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			)  ->							Optional[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			)  ->							List[Any]:
       __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			)  ->							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				      =  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			)  ->							str:
       for model_class_name in self.all_model_classes:
        __UpperCamelCase				      =  model_class_name.from_pretrained("""facebook/mbart-large-cc25"""		,	from_pt=lowercase			)
        # FlaxMBartForSequenceClassification 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					      =				[
          ''' UN Chief Says There Is No Military Solution in Syria''',
      ]
      __SCREAMING_SNAKE_CASE					      =				[
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
      ]
      __SCREAMING_SNAKE_CASE					      =				'''facebook/mbart-large-en-ro'''
  
  
  
      @cached_property
      def    __lowerCamelCase				(  self			)  ->							int:
       return AutoTokenizer.from_pretrained(self.model_name			)
  
  
  
      @cached_property
      def    __lowerCamelCase				(  self			)  ->							Dict:
       __UpperCamelCase				      =  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name		,	from_pt=lowercase			)
       return model
  
  
  
      def    __lowerCamelCase				(  self		,	**lowercase			)  ->							str:
       __UpperCamelCase				      =  self.translate_src_text(**lowercase			)
       self.assertListEqual(self.expected_text		,	lowercase			)
  
  
  
      def    __lowerCamelCase				(  self		,	**lowercase			)  ->							Any:
       __UpperCamelCase				      =  self.tokenizer(self.src_text		,	**lowercase		,	return_tensors="""np"""			)
       __UpperCamelCase				      =  self.model.generate(
           model_inputs.input_ids		,	attention_mask=model_inputs.attention_mask		,	decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]		,	early_stopping=lowercase		,	num_beams=2		,	).sequences
       __UpperCamelCase				      =  self.tokenizer.batch_decode(lowercase		,	skip_special_tokens=lowercase			)
       return generated_words
  
  
  
  
      @slow
      def    __lowerCamelCase				(  self			)  ->							List[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)
  
  
  
  
  
  