



'''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()










'''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)





