






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

A_						:						Tuple						      =					{
    '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',
}
A_						:						Any						      =					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:
  for attribute in key.split('.'    ):
    UpperCamelCase_:   List[Any]        =							getattr(UpperCAmelCase__      ,       UpperCAmelCase__    )

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

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							List[str]:
  UpperCamelCase_:   List[str]        =							[]
  UpperCamelCase_:   Dict        =							fairseq_model.state_dict()

  UpperCamelCase_:   Optional[int]        =							hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    UpperCamelCase_:   Tuple        =							False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       hf_model.config.feat_extract_norm == 'group'      ,       )
      UpperCamelCase_:   str        =							True
    else:
      for key, mapped_key in MAPPING.items():
        UpperCamelCase_:   int        =							'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
          UpperCamelCase_:   List[Any]        =							True
          if "*" in mapped_key:
            UpperCamelCase_:   Union[str, Any]        =							name.split(UpperCAmelCase__    )[0].split('.'    )[-2]
            UpperCamelCase_:   int        =							mapped_key.replace('*'      ,       UpperCAmelCase__    )
          if "weight_g" in name:
            UpperCamelCase_:   Union[str, Any]        =							'weight_g'
          elif "weight_v" in name:
            UpperCamelCase_:   List[str]        =							'weight_v'
          elif "bias" in name:
            UpperCamelCase_:   Dict        =							'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            UpperCamelCase_:   List[str]        =							'weight'
          else:
            UpperCamelCase_:   List[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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Optional[Any]:
  UpperCamelCase_:   Any        =							full_name.split('conv_layers.'    )[-1]
  UpperCamelCase_:   Optional[int]        =							name.split('.'    )
  UpperCamelCase_:   Union[str, Any]        =							int(items[0]    )
  UpperCamelCase_:   Dict        =							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.'''    )
      UpperCamelCase_:   Dict        =							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.'''    )
      UpperCamelCase_:   Optional[Any]        =							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.'''    )
      UpperCamelCase_:   List[str]        =							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.'''    )
      UpperCamelCase_:   str        =							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     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=True    )			->							str:
  if config_path is not None:
    UpperCamelCase_:   int        =							UniSpeechSatConfig.from_pretrained(UpperCAmelCase__    )
  else:
    UpperCamelCase_:   Tuple        =							UniSpeechSatConfig()

  UpperCamelCase_:   List[str]        =							''

  if is_finetuned:
    UpperCamelCase_:   List[str]        =							UniSpeechSatForCTC(UpperCAmelCase__    )
  else:
    UpperCamelCase_:   int        =							UniSpeechSatForPreTraining(UpperCAmelCase__    )

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

  recursively_load_weights(UpperCAmelCase__      ,       UpperCAmelCase__    )

  hf_wavavec.save_pretrained(UpperCAmelCase__    )


if __name__ == "__main__":
   A_						:						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'
   )
   A_						:						Optional[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
   )



def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     assert column_title.isupper()
     __UpperCamelCase =			0
     __UpperCamelCase =			len(_lowercase ) - 1
     __UpperCamelCase =			0

     while index >= 0:
          __UpperCamelCase =			(ord(column_title[index] ) - 64) * pow(26					,  _lowercase )
          answer += value
          power += 1
          index -= 1

     return answer


if __name__ == "__main__":
    from doctest import testmod

    testmod()


