


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       List[Any]										=	{
    '''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''',
}
snake_case							:       Optional[Any]										=	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						str		):




      """simple docstring"""





      for attribute in key.split('''.'''		):
            a		:Optional[Any]									=			getattr(UpperCAmelCase_					,    UpperCAmelCase_		)

      if weight_type is not None:
            a		:Optional[Any]									=			getattr(UpperCAmelCase_					,    UpperCAmelCase_		).shape
      else:
            a		:Dict									=			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":
            a		:List[str]									=			value
      elif weight_type == "weight_g":
            a		:Union[str, Any]									=			value
      elif weight_type == "weight_v":
            a		:List[Any]									=			value
      elif weight_type == "bias":
            a		:Optional[int]									=			value
      else:
            a		:List[str]									=			value

      logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      a		:str									=			[]
      a		:Optional[int]									=			fairseq_model.state_dict()

      a		:List[Any]									=			hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            a		:int									=			False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    hf_model.config.feat_extract_norm == '''group'''					,    )
                  a		:Tuple									=			True
            else:
                  for key, mapped_key in MAPPING.items():
                        a		:Optional[Any]									=			'''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
                              a		:Any									=			True
                              if "*" in mapped_key:
                                    a		:Tuple									=			name.split(UpperCAmelCase_		)[0].split('''.'''		)[-2]
                                    a		:Optional[Any]									=			mapped_key.replace('''*'''					,    UpperCAmelCase_		)
                              if "weight_g" in name:
                                    a		:Dict									=			'''weight_g'''
                              elif "weight_v" in name:
                                    a		:str									=			'''weight_v'''
                              elif "bias" in name:
                                    a		:int									=			'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    a		:Optional[int]									=			'''weight'''
                              else:
                                    a		:str									=			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_		:						List[Any]					,    UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





      a		:Tuple									=			full_name.split('''conv_layers.'''		)[-1]
      a		:List[str]									=			name.split('''.'''		)
      a		:List[str]									=			int(items[0]		)
      a		:Any									=			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.'''		)
                  a		:Any									=			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.'''		)
                  a		: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:
                  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.'''		)
                  a		:List[Any]									=			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.'''		)
                  a		:Union[str, Any]									=			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_		:						Dict					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						List[str]=None					,    UpperCAmelCase_		:						Any=None					,    UpperCAmelCase_		:						int=True		):




      """simple docstring"""





      if config_path is not None:
            a		:List[str]									=			UniSpeechSatConfig.from_pretrained(UpperCAmelCase_		)
      else:
            a		:List[str]									=			UniSpeechSatConfig()

      a		:Any									=			''''''

      if is_finetuned:
            a		:Union[str, Any]									=			UniSpeechSatForCTC(UpperCAmelCase_		)
      else:
            a		:str									=			UniSpeechSatForPreTraining(UpperCAmelCase_		)

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

      recursively_load_weights(UpperCAmelCase_					,    UpperCAmelCase_		)

      hf_wavavec.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     snake_case							:       List[Any]										=	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'''
     )
     snake_case							:       str										=	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
     )









def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b == 0:
            return 1
      if (b % 2) == 0:
            return actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
      else:
            return a * actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b < 0:
            return 1 / actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)
      return actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)


if __name__ == "__main__":
     print(power(-2, -3))





