



"""simple docstring"""



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCAmelCase  :  str   =			logging.get_logger(__name__)

_lowerCAmelCase  :  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   SCREAMING_SNAKE_CASE__					(     snake_case					:   Tuple ,				snake_case					:   Dict ,				snake_case					:   int ,				snake_case					:   int ,				snake_case					:   int							)->		Union[str, Any]:






     '''simple docstring'''



     for attribute in key.split("."							):
          UpperCAmelCase__	:						Any       =       getattr(snake_case ,				snake_case							)

     if weight_type is not None:
          UpperCAmelCase__	:						Dict       =       getattr(snake_case ,				snake_case							).shape
     else:
          UpperCAmelCase__	:						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":
          UpperCAmelCase__	:						Optional[Any]       =       value
     elif weight_type == "weight_g":
          UpperCAmelCase__	:						Tuple       =       value
     elif weight_type == "weight_v":
          UpperCAmelCase__	:						Tuple       =       value
     elif weight_type == "bias":
          UpperCAmelCase__	:						str       =       value
     else:
          UpperCAmelCase__	:						int       =       value

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


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any] ,				snake_case					:   Optional[int]							)->		Dict:






     '''simple docstring'''



     UpperCAmelCase__	:						int       =       []
     UpperCAmelCase__	:						str       =       fairseq_model.state_dict()

     UpperCAmelCase__	:						int       =       hf_model.feature_extractor

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

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

     logger.warning(f'Unused weights: {unused_weights}'							)


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Any ,				snake_case					:   Dict ,				snake_case					:   Dict ,				snake_case					:   List[str] ,				snake_case					:   int							)->		int:






     '''simple docstring'''



     UpperCAmelCase__	:						Any       =       full_name.split("conv_layers."							)[-1]
     UpperCAmelCase__	:						Optional[int]       =       name.split("."							)
     UpperCAmelCase__	:						Dict       =       int(items[0]							)
     UpperCAmelCase__	:						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.'
               )
               UpperCAmelCase__	:						str       =       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__	:						Tuple       =       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__	:						str       =       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__	:						Optional[int]       =       value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
     else:
          unused_weights.append(snake_case							)


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Tuple							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       HubertConfig()
     UpperCAmelCase__	:						Optional[Any]       =       model.config

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

     return config


@torch.no_grad()
def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any] ,				snake_case					:   Union[str, Any]=None							)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__	:						str       =       distilhubert().model.model

     if config_path is not None:
          UpperCAmelCase__	:						Optional[int]       =       HubertConfig.from_pretrained(snake_case							)
     else:
          UpperCAmelCase__	:						int       =       convert_config(snake_case							)
     UpperCAmelCase__	:						Any       =       model.eval()

     UpperCAmelCase__	:						List[str]       =       WavaVecaFeatureExtractor(
         feature_size=1 ,				sampling_rate=1_6000 ,				padding_value=0 ,				do_normalize=snake_case ,				return_attention_mask=snake_case ,				)
     UpperCAmelCase__	:						List[Any]       =       HubertModel(snake_case							)

     recursively_load_weights(snake_case ,				snake_case							)

     feature_extractor.save_pretrained(snake_case							)
     hf_model.save_pretrained(snake_case							)


if __name__ == "__main__":
 _lowerCAmelCase  :  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""")
 _lowerCAmelCase  :  List[str]   =			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,
)


_lowerCAmelCase  :  Optional[Any]   =			{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  str   =			[
     """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

 _lowerCAmelCase  :  List[str]   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


