


'''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)










'''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__)






