


'''simple docstring'''

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a_						:       int      			=				logging.get_logger(__name__)

a_						:       Optional[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""",
}
a_						:       Optional[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_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Union[str, Any]	):
 for attribute in key.split("."	):
  lowerCamelCase_  				=						getattr(UpperCAmelCase_      ,       UpperCAmelCase_	)

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

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





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Any	):
 lowerCamelCase_  				=						[]
 lowerCamelCase_  				=						fairseq_model.state_dict()

 lowerCamelCase_  				=						hf_model.unispeech_sat.feature_extractor

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

 lowerCamelCase_  				=						""

 if is_finetuned:
  lowerCamelCase_  				=						UniSpeechSatForCTC(UpperCAmelCase_	)
 else:
  lowerCamelCase_  				=						UniSpeechSatForPreTraining(UpperCAmelCase_	)

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

 recursively_load_weights(UpperCAmelCase_      ,       UpperCAmelCase_	)

 hf_wavavec.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
     a_						:       Optional[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"""
     )
     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
     )







'''simple docstring'''

from __future__ import annotations





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 if b == 0:
  return (1, 0)
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       a % b	)
 lowerCamelCase_  				=						a // b
 return (y, x - k * y)





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						na * na
 lowerCamelCase_  				=						ra * x * na + ra * y * na
 return (n % m + m) % m





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 ((lowerCamelCase_)				,(lowerCamelCase_))  				=						extended_euclid(UpperCAmelCase_      ,       UpperCAmelCase_	)
 if b < 0:
  lowerCamelCase_  				=						(b % n + n) % n
 return b





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_				,lowerCamelCase_  				=						invert_modulo(UpperCAmelCase_      ,       UpperCAmelCase_	), invert_modulo(UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						na * na
 lowerCamelCase_  				=						ra * x * na + ra * y * na
 return (n % m + m) % m


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

     testmod(name="""chinese_remainder_theorem""", verbose=True)
     testmod(name="""chinese_remainder_theorem2""", verbose=True)
     testmod(name="""invert_modulo""", verbose=True)
     testmod(name="""extended_euclid""", verbose=True)



