



'''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	:       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	:       List[Any]      	=	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    )       ->	List[Any]:






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

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

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







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Dict:






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

   UpperCamelCase					=  hf_model.unispeech_sat.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCamelCase					=  False
      if "conv_layers" in name:
         load_conv_layer(
             A__    ,						A__    ,						A__    ,						A__    ,						hf_model.config.feat_extract_norm == 'group'    ,						)
         UpperCamelCase					=  True
      else:
         for key, mapped_key in MAPPING.items():
            UpperCamelCase					=  '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					=  True
               if "*" in mapped_key:
                  UpperCamelCase					=  name.split(A__    )[0].split('.'    )[-2]
                  UpperCamelCase					=  mapped_key.replace('*'    ,						A__    )
               if "weight_g" in name:
                  UpperCamelCase					=  'weight_g'
               elif "weight_v" in name:
                  UpperCamelCase					=  'weight_v'
               elif "bias" in name:
                  UpperCamelCase					=  'bias'
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  UpperCamelCase					=  'weight'
               else:
                  UpperCamelCase					=  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 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    )       ->	Dict:






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







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__=None    ,						A__=None    ,						A__=True    )       ->	int:






   """simple docstring"""
   if config_path is not None:
      UpperCamelCase					=  UniSpeechSatConfig.from_pretrained(A__    )
   else:
      UpperCamelCase					=  UniSpeechSatConfig()

   UpperCamelCase					=  ''

   if is_finetuned:
      UpperCamelCase					=  UniSpeechSatForCTC(A__    )
   else:
      UpperCamelCase					=  UniSpeechSatForPreTraining(A__    )

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

   recursively_load_weights(A__    ,						A__    )

   hf_wavavec.save_pretrained(A__    )


if __name__ == "__main__":
   _lowerCamelCase	:       Tuple      	=	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	:       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 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ):

							def get_matched_characters(__lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ) -> str:
														a__             =     []
														a__             =     min(len(_stra   )					,    len(_stra   )   ) // 2
														for i, l in enumerate(_stra   ):
																					a__             =     int(max(0					,    i - limit   )   )
																					a__             =     int(min(i + limit + 1					,    len(_stra   )   )   )
																					if l in _stra[left:right]:
																												matched.append(__lowerCAmelCase   )
																												a__             =     F'{_stra[0:_stra.index(__lowerCAmelCase   )]} {_stra[_stra.index(__lowerCAmelCase   ) + 1:]}'

														return "".join(__lowerCAmelCase   )

							# matching characters
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     len(__lowerCAmelCase   )

							# transposition
							a__             =     (
							    len([(ca, ca) for ca, ca in zip(__lowerCAmelCase					,    __lowerCAmelCase   ) if ca != ca]   ) // 2
							)

							if not match_count:
														a__             =     0.0
							else:
														a__             =     (
														    1
														    / 3
														    * (
														        match_count / len(__lowerCAmelCase   )
														        + match_count / len(__lowerCAmelCase   )
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							a__             =     0
							for ca, ca in zip(stra[:4]					,    stra[:4]   ):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler('''hello''', '''world'''))




