

"""simple docstring"""




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_  :      Dict							=      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  :      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 						UpperCAmelCase__ (					A__						,     A__						,     A__						,     A__						,     A__ )   ->   List[str]:




   """simple docstring"""
   for attribute in key.split("." ):
      lowerCamelCase__						       =      getattr(A__						,     A__ )

   if weight_type is not None:
      lowerCamelCase__						       =      getattr(A__						,     A__ ).shape
   else:
      lowerCamelCase__						       =      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__						       =      value
   elif weight_type == "weight_g":
      lowerCamelCase__						       =      value
   elif weight_type == "weight_v":
      lowerCamelCase__						       =      value
   elif weight_type == "bias":
      lowerCamelCase__						       =      value
   else:
      lowerCamelCase__						       =      value

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



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   Union[str, Any]:




   """simple docstring"""
   lowerCamelCase__						       =      []
   lowerCamelCase__						       =      fairseq_model.state_dict()

   lowerCamelCase__						       =      hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      lowerCamelCase__						       =      False
      if "conv_layers" in name:
         load_conv_layer(
             A__						,     A__						,     A__						,     A__						,     hf_model.config.feat_extract_norm == "group"						,     )
         lowerCamelCase__						       =      True
      else:
         for key, mapped_key in MAPPING.items():
            lowerCamelCase__						       =      mapped_key

            if key in name:
               lowerCamelCase__						       =      True
               if "*" in mapped_key:
                  lowerCamelCase__						       =      name.split(A__ )[0].split("." )[-2]
                  lowerCamelCase__						       =      mapped_key.replace("*"						,     A__ )
               if "weight_g" in name:
                  lowerCamelCase__						       =      "weight_g"
               elif "weight_v" in name:
                  lowerCamelCase__						       =      "weight_v"
               elif "weight" in name:
                  lowerCamelCase__						       =      "weight"
               elif "bias" in name:
                  lowerCamelCase__						       =      "bias"
               else:
                  lowerCamelCase__						       =      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 						UpperCAmelCase__ (					A__						,     A__						,     A__						,     A__						,     A__ )   ->   Optional[Any]:




   """simple docstring"""
   lowerCamelCase__						       =      full_name.split("conv_layers." )[-1]
   lowerCamelCase__						       =      name.split("." )
   lowerCamelCase__						       =      int(items[0] )
   lowerCamelCase__						       =      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__						       =      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__						       =      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__						       =      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__						       =      value
         logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
   else:
      unused_weights.append(A__ )



def 						UpperCAmelCase__ (					A__ )   ->   List[str]:




   """simple docstring"""
   lowerCamelCase__						       =      HubertConfig()
   lowerCamelCase__						       =      model.config

   lowerCamelCase__						       =      fs_config.activation_dropout
   lowerCamelCase__						       =      False
   lowerCamelCase__						       =      fs_config.attention_dropout
   lowerCamelCase__						       =      False
   lowerCamelCase__						       =      eval(fs_config.extractor_conv_feature_layers )
   lowerCamelCase__						       =      [x[0] for x in conv_layers]
   lowerCamelCase__						       =      [x[1] for x in conv_layers]
   lowerCamelCase__						       =      [x[2] for x in conv_layers]
   lowerCamelCase__						       =      "gelu"
   lowerCamelCase__						       =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
   lowerCamelCase__						       =      False
   lowerCamelCase__						       =      0.0
   lowerCamelCase__						       =      0.0
   lowerCamelCase__						       =      fs_config.activation_fn
   lowerCamelCase__						       =      fs_config.dropout
   lowerCamelCase__						       =      fs_config.encoder_embed_dim
   lowerCamelCase__						       =      0.02
   lowerCamelCase__						       =      fs_config.encoder_ffn_embed_dim
   lowerCamelCase__						       =      1E-5
   lowerCamelCase__						       =      0.0
   lowerCamelCase__						       =      fs_config.encoder_attention_heads
   lowerCamelCase__						       =      fs_config.conv_pos_groups
   lowerCamelCase__						       =      fs_config.conv_pos
   lowerCamelCase__						       =      len(A__ )
   lowerCamelCase__						       =      fs_config.encoder_layers

   return config



@torch.no_grad()
def 						UpperCAmelCase__ (					A__						,     A__=None )   ->   Union[str, Any]:




   """simple docstring"""
   lowerCamelCase__						       =      distilhubert().model.model

   if config_path is not None:
      lowerCamelCase__						       =      HubertConfig.from_pretrained(A__ )
   else:
      lowerCamelCase__						       =      convert_config(A__ )
   lowerCamelCase__						       =      model.eval()

   lowerCamelCase__						       =      WavaVecaFeatureExtractor(
       feature_size=1						,     sampling_rate=1_6000						,     padding_value=0						,     do_normalize=A__						,     return_attention_mask=A__						,     )
   lowerCamelCase__						       =      HubertModel(A__ )

   recursively_load_weights(A__						,     A__ )

   feature_extractor.save_pretrained(A__ )
   hf_model.save_pretrained(A__ )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_  :      Optional[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''')
     SCREAMING_SNAKE_CASE_  :      List[Any]							=      parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




