

'''simple docstring'''
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCamelCase :      Any					 =     logging.get_logger(__name__)

lowerCamelCase :      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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
lowerCamelCase :      Any					 =     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Any							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any				)      ->			Any:
     """simple docstring"""




     for attribute in key.split('.'				):
          _SCREAMING_SNAKE_CASE							    =getattr(_UpperCamelCase							, _UpperCamelCase				)

     if weight_type is not None:
          _SCREAMING_SNAKE_CASE							    =getattr(_UpperCamelCase							, _UpperCamelCase				).shape
     else:
          _SCREAMING_SNAKE_CASE							    =hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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							    =value
     elif weight_type == "weight_g":
          _SCREAMING_SNAKE_CASE							    =value
     elif weight_type == "weight_v":
          _SCREAMING_SNAKE_CASE							    =value
     elif weight_type == "bias":
          _SCREAMING_SNAKE_CASE							    =value
     else:
          _SCREAMING_SNAKE_CASE							    =value

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     Dict				)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =fairseq_model.state_dict()

     _SCREAMING_SNAKE_CASE							    =hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          _SCREAMING_SNAKE_CASE							    =False
          if "conv_layers" in name:
               load_conv_layer(
                   _UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase							, hf_model.config.feat_extract_norm == 'group'							, )
               _SCREAMING_SNAKE_CASE							    =True
          else:
               for key, mapped_key in MAPPING.items():
                    _SCREAMING_SNAKE_CASE							    ='unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('w2v_model.'				)[-1] == name.split('.'				)[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'				)[:-1]				) != key):
                              # special case since naming is very similar
                              continue
                         _SCREAMING_SNAKE_CASE							    =True
                         if "*" in mapped_key:
                              _SCREAMING_SNAKE_CASE							    =name.split(_UpperCamelCase				)[0].split('.'				)[-2]
                              _SCREAMING_SNAKE_CASE							    =mapped_key.replace('*'							, _UpperCamelCase				)
                         if "weight_g" in name:
                              _SCREAMING_SNAKE_CASE							    ='weight_g'
                         elif "weight_v" in name:
                              _SCREAMING_SNAKE_CASE							    ='weight_v'
                         elif "bias" in name:
                              _SCREAMING_SNAKE_CASE							    ='bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _SCREAMING_SNAKE_CASE							    ='weight'
                         else:
                              _SCREAMING_SNAKE_CASE							    =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        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]				)      ->			List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =full_name.split('conv_layers.'				)[-1]
     _SCREAMING_SNAKE_CASE							    =name.split('.'				)
     _SCREAMING_SNAKE_CASE							    =int(items[0]				)
     _SCREAMING_SNAKE_CASE							    =int(items[1]				)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."				)
               _SCREAMING_SNAKE_CASE							    =value
               logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."				)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."				)
               _SCREAMING_SNAKE_CASE							    =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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."				)
               _SCREAMING_SNAKE_CASE							    =value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."				)
               _SCREAMING_SNAKE_CASE							    =value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
     else:
          unused_weights.append(_UpperCamelCase				)


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     List[str]							, _UpperCamelCase						:     List[Any]=None							, _UpperCamelCase						:     str=None							, _UpperCamelCase						:     Tuple=True				)      ->			str:
     """simple docstring"""




     if config_path is not None:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatConfig.from_pretrained(_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatConfig()

     _SCREAMING_SNAKE_CASE							    =''

     if is_finetuned:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatForCTC(_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =UniSpeechSatForPreTraining(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]							, arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
     _SCREAMING_SNAKE_CASE							    =model[0].eval()

     recursively_load_weights(_UpperCamelCase							, _UpperCamelCase				)

     hf_wavavec.save_pretrained(_UpperCamelCase				)


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("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
     parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
     parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
     parser.add_argument(
         "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
     )
     lowerCamelCase :      Union[str, Any]					 =     parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 							A__    (       A__     ,   A__  ):
  A__		 =				'convnextv2'





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=3							,					_a      :	Any=4							,					_a      :	int=4							,					_a      :	Union[str, Any]=None							,					_a      :	List[str]=None							,					_a      :	Optional[Any]="gelu"							,					_a      :	Any=0.02							,					_a      :	Any=1e-12							,					_a      :	Tuple=0.0							,					_a      :	int=224							,					_a      :	Any=None							,					_a      :	Optional[int]=None							,					**_a      :	List[str]							,					)	->    Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a )

       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =patch_size
       _SCREAMING_SNAKE_CASE							    =num_stages
       _SCREAMING_SNAKE_CASE							    =[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       _SCREAMING_SNAKE_CASE							    =[3, 3, 9, 3] if depths is None else depths
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =layer_norm_eps
       _SCREAMING_SNAKE_CASE							    =drop_path_rate
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =['stem'] + [f"stage{idx}" for idx in range(1							,					len(self.depths ) + 1 )]
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_aligned_output_features_output_indices(
           out_features=_a							,					out_indices=_a							,					stage_names=self.stage_names )
