





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
      import torch





class   lowercase__     :







  def __init__(						self     ,    _lowercase     ,    _lowercase=14     ,    _lowercase=7     ,    _lowercase=True     ,    _lowercase=True     ,    _lowercase=False     ,    _lowercase=True     ,    _lowercase=99     ,    _lowercase=32     ,    _lowercase=4     ,    _lowercase=4     ,    _lowercase=[[["global", "local"], 2]]     ,    _lowercase=37     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0.1     ,    _lowercase=512     ,    _lowercase=7     ,    _lowercase=0.02     ,    ):
       lowerCAmelCase_       :							Dict			 =		parent
       lowerCAmelCase_       :							Dict			 =		batch_size
       lowerCAmelCase_       :							Optional[int]			 =		seq_length
       lowerCAmelCase_       :							List[Any]			 =		is_training
       lowerCAmelCase_       :							int			 =		use_input_mask
       lowerCAmelCase_       :							int			 =		use_token_type_ids
       lowerCAmelCase_       :							Any			 =		use_labels
       lowerCAmelCase_       :							Union[str, Any]			 =		vocab_size
       lowerCAmelCase_       :							List[str]			 =		hidden_size
       lowerCAmelCase_       :							int			 =		num_hidden_layers
       lowerCAmelCase_       :							Optional[int]			 =		num_attention_heads
       lowerCAmelCase_       :							str			 =		attention_types
       lowerCAmelCase_       :							Dict			 =		intermediate_size
       lowerCAmelCase_       :							Tuple			 =		hidden_act
       lowerCAmelCase_       :							Tuple			 =		hidden_dropout_prob
       lowerCAmelCase_       :							Dict			 =		attention_probs_dropout_prob
       lowerCAmelCase_       :							Any			 =		max_position_embeddings
       lowerCAmelCase_       :							Optional[Any]			 =		window_size
       lowerCAmelCase_       :							int			 =		initializer_range
       lowerCAmelCase_       :							List[str]			 =		None
       lowerCAmelCase_       :							Tuple			 =		vocab_size - 1
       lowerCAmelCase_       :							Union[str, Any]			 =		vocab_size - 1
       lowerCAmelCase_       :							str			 =		vocab_size - 1







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		ids_tensor([self.batch_size, self.seq_length]     ,    self.vocab_size    )

       lowerCAmelCase_       :							List[str]			 =		None
       if self.use_input_mask:
            lowerCAmelCase_       :							List[str]			 =		random_attention_mask([self.batch_size, self.seq_length]    )

       lowerCAmelCase_       :							Tuple			 =		GPTNeoConfig(
           vocab_size=self.vocab_size     ,    hidden_size=self.hidden_size     ,    num_layers=self.num_hidden_layers     ,    num_heads=self.num_attention_heads     ,    max_position_embeddings=self.max_position_embeddings     ,    use_cache=_lowercase     ,    bos_token_id=self.bos_token_id     ,    eos_token_id=self.eos_token_id     ,    pad_token_id=self.pad_token_id     ,    window_size=self.window_size     ,    attention_types=self.attention_types     ,    )

       return (config, input_ids, input_mask)







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Optional[Any]			 =		self.prepare_config_and_inputs()
       lowerCAmelCase_       :							List[Any]			 =		config_and_inputs
       lowerCAmelCase_       :							int			 =		{"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							List[Any]			 =		20
       lowerCAmelCase_       :							Tuple			 =		model_class_name(_lowercase    )

       lowerCAmelCase_       :							List[Any]			 =		model.init_cache(input_ids.shape[0]     ,    _lowercase    )
       lowerCAmelCase_       :							List[Any]			 =		jnp.ones((input_ids.shape[0], max_decoder_length)     ,    dtype="""i4"""    )

       lowerCAmelCase_       :							int			 =		jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1    )[None, :]     ,    (input_ids.shape[0], input_ids.shape[-1] - 1)    )
       lowerCAmelCase_       :							List[str]			 =		model(
           input_ids[:, :-1]     ,    attention_mask=_lowercase     ,    past_key_values=_lowercase     ,    position_ids=_lowercase     ,    )

       lowerCAmelCase_       :							Union[str, Any]			 =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,    dtype="""i4"""    )
       lowerCAmelCase_       :							Union[str, Any]			 =		model(
           input_ids[:, -1:]     ,    attention_mask=_lowercase     ,    past_key_values=outputs_cache.past_key_values     ,    position_ids=_lowercase     ,    )

       lowerCAmelCase_       :							Dict			 =		model(_lowercase    )

       lowerCAmelCase_       :							str			 =		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 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							Tuple			 =		20
       lowerCAmelCase_       :							Any			 =		model_class_name(_lowercase    )

       lowerCAmelCase_       :							List[Any]			 =		jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])    )]     ,    axis=-1     ,    )

       lowerCAmelCase_       :							List[str]			 =		model.init_cache(input_ids.shape[0]     ,    _lowercase    )
       lowerCAmelCase_       :							str			 =		jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1    )[None, :]     ,    (input_ids.shape[0], input_ids.shape[-1] - 1)    )

       lowerCAmelCase_       :							Union[str, Any]			 =		model(
           input_ids[:, :-1]     ,    attention_mask=_lowercase     ,    past_key_values=_lowercase     ,    position_ids=_lowercase     ,    )
       lowerCAmelCase_       :							str			 =		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,    dtype="""i4"""    )
       lowerCAmelCase_       :							Optional[int]			 =		model(
           input_ids[:, -1:]     ,    past_key_values=outputs_cache.past_key_values     ,    attention_mask=_lowercase     ,    position_ids=_lowercase     ,    )

       lowerCAmelCase_       :							Any			 =		model(_lowercase     ,    attention_mask=_lowercase    )

       lowerCAmelCase_       :							Any			 =		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   lowercase__     (				__A  ,       __A  ,       unittest.TestCase	):
  __UpperCamelCase						  =	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  __UpperCamelCase						  =	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		FlaxGPTNeoModelTester(self    )







  def 						UpperCAmelCase__		(						self    ):
       for model_class_name in self.all_model_classes:
            lowerCAmelCase_       :							List[str]			 =		self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       for model_class_name in self.all_model_classes:
            lowerCAmelCase_       :							Optional[Any]			 =		self.model_tester.prepare_config_and_inputs()
            self.model_tester.check_use_cache_forward_with_attn_mask(
                _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )







  @slow
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		GPTaTokenizer.from_pretrained("""gpt2"""     ,    pad_token="""<|endoftext|>"""     ,    padding_side="""left"""    )
       lowerCAmelCase_       :							str			 =		tokenizer(["""Hello this is a long string""", """Hey"""]     ,    return_tensors="""np"""     ,    padding=_lowercase     ,    truncation=_lowercase    )

       lowerCAmelCase_       :							str			 =		FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""    )
       lowerCAmelCase_       :							Tuple			 =		False
       lowerCAmelCase_       :							Optional[Any]			 =		model.config.eos_token_id

       lowerCAmelCase_       :							Tuple			 =		jax.jit(model.generate    )

       lowerCAmelCase_       :							List[Any]			 =		jit_generate(
           inputs["""input_ids"""]     ,    attention_mask=inputs["""attention_mask"""]     ,    pad_token_id=tokenizer.pad_token_id    ).sequences

       lowerCAmelCase_       :							Union[str, Any]			 =		tokenizer.batch_decode(_lowercase     ,    skip_special_tokens=_lowercase    )

       lowerCAmelCase_       :							List[Any]			 =		[
           "Hello this is a long string of text.\n\nI'm trying to get the text of the",
           "Hey, I'm a little late to the party. I'm going to",
       ]

       self.assertListEqual(_lowercase     ,    _lowercase    )







  @is_pt_flax_cross_test
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							List[str]			 =		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__    ):
                 # prepare inputs
                 lowerCAmelCase_       :							str			 =		self._prepare_for_class(_lowercase     ,    _lowercase    )
                 lowerCAmelCase_       :							Optional[Any]			 =		{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 lowerCAmelCase_       :							int			 =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 lowerCAmelCase_       :							Union[str, Any]			 =		getattr(_lowercase     ,    _lowercase    )

                 lowerCAmelCase_       :							Tuple			 =		pt_inputs["input_ids"].shape
                 lowerCAmelCase_       :							Optional[int]			 =		np.random.randint(0     ,    seq_length - 1     ,    size=(batch_size,)    )
                 for batch_idx, start_index in enumerate(_lowercase    ):
                      lowerCAmelCase_       :							int			 =		0
                      lowerCAmelCase_       :							str			 =		1
                      lowerCAmelCase_       :							Union[str, Any]			 =		0
                      lowerCAmelCase_       :							Optional[Any]			 =		1
                 lowerCAmelCase_       :							int			 =		pt_model_class(_lowercase    ).eval()
                 lowerCAmelCase_       :							int			 =		model_class(_lowercase     ,    dtype=jnp.floataa    )

                 lowerCAmelCase_       :							List[str]			 =		convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,    _lowercase    )
                 lowerCAmelCase_       :							List[str]			 =		fx_state

                 with torch.no_grad():
                      lowerCAmelCase_       :							int			 =		pt_model(**_lowercase    ).to_tuple()

                 lowerCAmelCase_       :							List[str]			 =		fx_model(**_lowercase    ).to_tuple()
                 self.assertEqual(len(_lowercase    )     ,    len(_lowercase    )     ,    """Output lengths differ between Flax and PyTorch"""    )
                 for fx_output, pt_output in zip(_lowercase     ,    _lowercase    ):
                      self.assert_almost_equals(fx_output[:, -1]     ,    pt_output[:, -1].numpy()     ,    4e-2    )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(_lowercase    )
                      lowerCAmelCase_       :							List[Any]			 =		model_class.from_pretrained(_lowercase     ,    from_pt=_lowercase    )

                 lowerCAmelCase_       :							List[Any]			 =		fx_model_loaded(**_lowercase    ).to_tuple()
                 self.assertEqual(
                     len(_lowercase    )     ,    len(_lowercase    )     ,    """Output lengths differ between Flax and PyTorch"""    )
                 for fx_output_loaded, pt_output in zip(_lowercase     ,    _lowercase    ):
                      self.assert_almost_equals(fx_output_loaded[:, -1]     ,    pt_output[:, -1].numpy()     ,    4e-2    )







  @is_pt_flax_cross_test
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							List[str]			 =		self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__    ):
                 # prepare inputs
                 lowerCAmelCase_       :							List[str]			 =		self._prepare_for_class(_lowercase     ,    _lowercase    )
                 lowerCAmelCase_       :							Optional[Any]			 =		{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 lowerCAmelCase_       :							int			 =		model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 lowerCAmelCase_       :							str			 =		getattr(_lowercase     ,    _lowercase    )

                 lowerCAmelCase_       :							Any			 =		pt_model_class(_lowercase    ).eval()
                 lowerCAmelCase_       :							List[str]			 =		model_class(_lowercase     ,    dtype=jnp.floataa    )

                 lowerCAmelCase_       :							Union[str, Any]			 =		load_flax_weights_in_pytorch_model(_lowercase     ,    fx_model.params    )
                 lowerCAmelCase_       :							str			 =		pt_inputs["input_ids"].shape
                 lowerCAmelCase_       :							Union[str, Any]			 =		np.random.randint(0     ,    seq_length - 1     ,    size=(batch_size,)    )
                 for batch_idx, start_index in enumerate(_lowercase    ):
                      lowerCAmelCase_       :							Any			 =		0
                      lowerCAmelCase_       :							Optional[int]			 =		1
                      lowerCAmelCase_       :							str			 =		0
                      lowerCAmelCase_       :							Dict			 =		1

                 # make sure weights are tied in PyTorch
                 pt_model.tie_weights()

                 with torch.no_grad():
                      lowerCAmelCase_       :							Optional[Any]			 =		pt_model(**_lowercase    ).to_tuple()

                 lowerCAmelCase_       :							Tuple			 =		fx_model(**_lowercase    ).to_tuple()
                 self.assertEqual(len(_lowercase    )     ,    len(_lowercase    )     ,    """Output lengths differ between Flax and PyTorch"""    )
                 for fx_output, pt_output in zip(_lowercase     ,    _lowercase    ):
                      self.assert_almost_equals(fx_output[:, -1]     ,    pt_output[:, -1].numpy()     ,    4e-2    )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      fx_model.save_pretrained(_lowercase    )
                      lowerCAmelCase_       :							Union[str, Any]			 =		pt_model_class.from_pretrained(_lowercase     ,    from_flax=_lowercase    )

                 with torch.no_grad():
                      lowerCAmelCase_       :							Dict			 =		pt_model_loaded(**_lowercase    ).to_tuple()

                 self.assertEqual(
                     len(_lowercase    )     ,    len(_lowercase    )     ,    """Output lengths differ between Flax and PyTorch"""    )
                 for fx_output, pt_output in zip(_lowercase     ,    _lowercase    ):
                      self.assert_almost_equals(fx_output[:, -1]     ,    pt_output[:, -1].numpy()     ,    4e-2    )







  @slow
  def 						UpperCAmelCase__		(						self    ):
       for model_class_name in self.all_model_classes:
            lowerCAmelCase_       :							int			 =		model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""    )
            lowerCAmelCase_       :							List[str]			 =		model(np.ones((1, 1)    )    )
            self.assertIsNotNone(_lowercase    )





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase         =	{
    """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 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





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

 if weight_type is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						getattr(__lowerCamelCase		,						__lowerCamelCase  ).shape
 else:
  __SCREAMING_SNAKE_CASE							:     Dict							=						hf_pointer.shape

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

 if weight_type == "weight":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_g":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_v":
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value
 elif weight_type == "bias":
  __SCREAMING_SNAKE_CASE							:     int							=						value
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 __SCREAMING_SNAKE_CASE							:     str							=						fairseq_model.state_dict()

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __SCREAMING_SNAKE_CASE							:     str							=						False
  if "conv_layers" in name:
   load_conv_layer(
       __lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						hf_model.config.feat_extract_norm == "group"		,						)
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						True
  else:
   for key, mapped_key in MAPPING.items():
    __SCREAMING_SNAKE_CASE							:     List[str]							=						mapped_key

    if key in name:
     __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						True
     if "*" in mapped_key:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						name.split(__lowerCamelCase  )[0].split("."  )[-2]
      __SCREAMING_SNAKE_CASE							:     int							=						mapped_key.replace("*"		,						__lowerCamelCase  )
     if "weight_g" in name:
      __SCREAMING_SNAKE_CASE							:     int							=						"weight_g"
     elif "weight_v" in name:
      __SCREAMING_SNAKE_CASE							:     str							=						"weight_v"
     elif "weight" in name:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						"weight"
     elif "bias" in name:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"bias"
     else:
      __SCREAMING_SNAKE_CASE							:     int							=						None
     set_recursively(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
    continue
  if not is_used:
   unused_weights.append(__lowerCamelCase  )

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						full_name.split("conv_layers."  )[-1]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						name.split("."  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						int(items[0]  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						int(items[1]  )

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						HubertConfig()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model.config

 __SCREAMING_SNAKE_CASE							:     str							=						fs_config.activation_dropout
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						fs_config.attention_dropout
 __SCREAMING_SNAKE_CASE							:     List[str]							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						eval(fs_config.extractor_conv_feature_layers  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[x[0] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Any							=						[x[1] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Dict							=						[x[2] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"gelu"
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"layer" if fs_config.extractor_mode == "layer_norm" else "group"
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Tuple							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						0.0
 __SCREAMING_SNAKE_CASE							:     Any							=						fs_config.activation_fn
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.dropout
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						fs_config.encoder_embed_dim
 __SCREAMING_SNAKE_CASE							:     int							=						0.02
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.encoder_ffn_embed_dim
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						1E-5
 __SCREAMING_SNAKE_CASE							:     Any							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_attention_heads
 __SCREAMING_SNAKE_CASE							:     Dict							=						fs_config.conv_pos_groups
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.conv_pos
 __SCREAMING_SNAKE_CASE							:     Tuple							=						len(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_layers

 return config






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Optional[Any]=None  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						distilhubert().model.model

 if config_path is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertConfig.from_pretrained(__lowerCamelCase  )
 else:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						convert_config(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model.eval()

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavaVecaFeatureExtractor(
     feature_size=1		,						sampling_rate=16000		,						padding_value=0		,						do_normalize=__lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						)
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertModel(__lowerCamelCase  )

 recursively_load_weights(__lowerCamelCase		,						__lowerCamelCase  )

 feature_extractor.save_pretrained(__lowerCamelCase  )
 hf_model.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
      _lowerCamelCase         =	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""")
      _lowerCamelCase         =	parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






