code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  A__	:      List[str]				=						logging.get_logger(__name__)
  
  A__	:      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     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       Union[str, Any]   ,					UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       List[Any]							)  ->	Dict:
  
  
  
  
  
  
  
       for attribute in key.split('.'							):
            __lowerCamelCase     :			int              =	getattr(UpperCAmelCase_   ,					UpperCAmelCase_							)
  
       if weight_type is not None:
            __lowerCamelCase     :			Optional[Any]              =	getattr(UpperCAmelCase_   ,					UpperCAmelCase_							).shape
       else:
            __lowerCamelCase     :			str              =	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":
            __lowerCamelCase     :			int              =	value
       elif weight_type == "weight_g":
            __lowerCamelCase     :			Union[str, Any]              =	value
       elif weight_type == "weight_v":
            __lowerCamelCase     :			str              =	value
       elif weight_type == "bias":
            __lowerCamelCase     :			Optional[int]              =	value
       else:
            __lowerCamelCase     :			int              =	value
  
       logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       Union[str, Any]							)  ->	int:
       __lowerCamelCase     :			Dict              =	[]
       __lowerCamelCase     :			Union[str, Any]              =	fairseq_model.state_dict()
  
       __lowerCamelCase     :			Optional[int]              =	hf_model.feature_extractor
  
       for name, value in fairseq_dict.items():
            __lowerCamelCase     :			Dict              =	False
            if "conv_layers" in name:
                 load_conv_layer(
                     UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_   ,					hf_model.config.feat_extract_norm == 'group'   ,					)
                 __lowerCamelCase     :			str              =	True
            else:
                 for key, mapped_key in MAPPING.items():
                      __lowerCamelCase     :			List[str]              =	mapped_key
  
                      if key in name:
                           __lowerCamelCase     :			str              =	True
                           if "*" in mapped_key:
                                __lowerCamelCase     :			Optional[int]              =	name.split(UpperCAmelCase_							)[0].split('.'							)[-2]
                                __lowerCamelCase     :			Tuple              =	mapped_key.replace('*'   ,					UpperCAmelCase_							)
                           if "weight_g" in name:
                                __lowerCamelCase     :			Tuple              =	'weight_g'
                           elif "weight_v" in name:
                                __lowerCamelCase     :			Union[str, Any]              =	'weight_v'
                           elif "weight" in name:
                                __lowerCamelCase     :			Any              =	'weight'
                           elif "bias" in name:
                                __lowerCamelCase     :			List[str]              =	'bias'
                           else:
                                __lowerCamelCase     :			Optional[int]              =	None
                           set_recursively(UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_							)
                      continue
            if not is_used:
                 unused_weights.append(UpperCAmelCase_							)
  
       logger.warning(F'Unused weights: {unused_weights}'							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Tuple   ,					UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       str							)  ->	List[Any]:
       __lowerCamelCase     :			int              =	full_name.split('conv_layers.'							)[-1]
       __lowerCamelCase     :			int              =	name.split('.'							)
       __lowerCamelCase     :			Tuple              =	int(items[0]							)
       __lowerCamelCase     :			str              =	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.'
                 )
                 __lowerCamelCase     :			Union[str, Any]              =	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.'
                 )
                 __lowerCamelCase     :			List[str]              =	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."
                 )
                 __lowerCamelCase     :			Dict              =	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.'
                 )
                 __lowerCamelCase     :			Any              =	value
                 logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
       else:
            unused_weights.append(UpperCAmelCase_							)
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	Dict:
       __lowerCamelCase     :			List[str]              =	HubertConfig()
       __lowerCamelCase     :			Dict              =	model.config
  
       __lowerCamelCase     :			Union[str, Any]              =	fs_config.activation_dropout
       __lowerCamelCase     :			Any              =	False
       __lowerCamelCase     :			str              =	fs_config.attention_dropout
       __lowerCamelCase     :			List[str]              =	False
       __lowerCamelCase     :			Optional[Any]              =	eval(fs_config.extractor_conv_feature_layers							)
       __lowerCamelCase     :			Tuple              =	[x[0] for x in conv_layers]
       __lowerCamelCase     :			Tuple              =	[x[1] for x in conv_layers]
       __lowerCamelCase     :			Optional[Any]              =	[x[2] for x in conv_layers]
       __lowerCamelCase     :			Any              =	'gelu'
       __lowerCamelCase     :			Any              =	'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       __lowerCamelCase     :			List[str]              =	False
       __lowerCamelCase     :			List[str]              =	0.0
       __lowerCamelCase     :			int              =	0.0
       __lowerCamelCase     :			List[str]              =	fs_config.activation_fn
       __lowerCamelCase     :			List[Any]              =	fs_config.dropout
       __lowerCamelCase     :			str              =	fs_config.encoder_embed_dim
       __lowerCamelCase     :			Optional[int]              =	0.02
       __lowerCamelCase     :			Union[str, Any]              =	fs_config.encoder_ffn_embed_dim
       __lowerCamelCase     :			Optional[int]              =	1e-5
       __lowerCamelCase     :			Tuple              =	0.0
       __lowerCamelCase     :			int              =	fs_config.encoder_attention_heads
       __lowerCamelCase     :			Tuple              =	fs_config.conv_pos_groups
       __lowerCamelCase     :			Optional[int]              =	fs_config.conv_pos
       __lowerCamelCase     :			Optional[Any]              =	len(UpperCAmelCase_							)
       __lowerCamelCase     :			int              =	fs_config.encoder_layers
  
       return config
  
  
  
  @torch.no_grad()
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       List[str]=None							)  ->	Optional[int]:
       __lowerCamelCase     :			Any              =	distilhubert().model.model
  
       if config_path is not None:
            __lowerCamelCase     :			Any              =	HubertConfig.from_pretrained(UpperCAmelCase_							)
       else:
            __lowerCamelCase     :			Union[str, Any]              =	convert_config(UpperCAmelCase_							)
       __lowerCamelCase     :			Dict              =	model.eval()
  
       __lowerCamelCase     :			Optional[Any]              =	WavaVecaFeatureExtractor(
           feature_size=1   ,					sampling_rate=1_60_00   ,					padding_value=0   ,					do_normalize=UpperCAmelCase_   ,					return_attention_mask=UpperCAmelCase_   ,					)
       __lowerCamelCase     :			Tuple              =	HubertModel(UpperCAmelCase_							)
  
       recursively_load_weights(UpperCAmelCase_   ,					UpperCAmelCase_							)
  
       feature_extractor.save_pretrained(UpperCAmelCase_							)
       hf_model.save_pretrained(UpperCAmelCase_							)
  
  
  if __name__ == "__main__":
        A__	:      Any				=						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__	:      List[str]				=						parser.parse_args()
        convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  A__	:      List[str]				=						{
      """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
      """tokenization_flaubert""": ["""FlaubertTokenizer"""],
  }
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """FlaubertForMultipleChoice""",
            """FlaubertForQuestionAnswering""",
            """FlaubertForQuestionAnsweringSimple""",
            """FlaubertForSequenceClassification""",
            """FlaubertForTokenClassification""",
            """FlaubertModel""",
            """FlaubertWithLMHeadModel""",
            """FlaubertPreTrainedModel""",
        ]
  
  try:
        if not is_tf_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """TFFlaubertForMultipleChoice""",
            """TFFlaubertForQuestionAnsweringSimple""",
            """TFFlaubertForSequenceClassification""",
            """TFFlaubertForTokenClassification""",
            """TFFlaubertModel""",
            """TFFlaubertPreTrainedModel""",
            """TFFlaubertWithLMHeadModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
        from .tokenization_flaubert import FlaubertTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )
  
        try:
              if not is_tf_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )
  
  else:
        import sys
  
        A__	:      Tuple				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  