





"""simple docstring"""




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

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

def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any]     ):
   for attribute in key.split('.'     ):
      __lowerCAmelCase							     =     getattr(__lowerCAmelCase,     __lowerCAmelCase     )

   if weight_type is not None:
      __lowerCAmelCase							     =     getattr(__lowerCAmelCase,     __lowerCAmelCase     ).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   a_   (			lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      int     ):
   __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(
             __lowerCAmelCase,     __lowerCAmelCase,     __lowerCAmelCase,     __lowerCAmelCase,     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(__lowerCAmelCase     )[0].split('.'     )[-2]
                  __lowerCAmelCase							     =     mapped_key.replace('*',     __lowerCAmelCase     )
               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(__lowerCAmelCase,     __lowerCAmelCase,     __lowerCAmelCase,     __lowerCAmelCase,     __lowerCAmelCase     )
            continue
      if not is_used:
         unused_weights.append(__lowerCAmelCase     )

   logger.warning(F"""Unused weights: {unused_weights}"""     )

def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      Dict     ):
   __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(__lowerCAmelCase     )

@torch.no_grad()
def   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any]=None,     lowerCAmelCase_				:      Optional[Any]=None,     lowerCAmelCase_				:      Optional[Any]=True     ):
   if config_path is not None:
      __lowerCAmelCase							     =     UniSpeechSatConfig.from_pretrained(__lowerCAmelCase     )
   else:
      __lowerCAmelCase							     =     UniSpeechSatConfig()

   __lowerCAmelCase							     =     ''

   if is_finetuned:
      __lowerCAmelCase							     =     UniSpeechSatForCTC(__lowerCAmelCase     )
   else:
      __lowerCAmelCase							     =     UniSpeechSatForPreTraining(__lowerCAmelCase     )

   __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(__lowerCAmelCase,     __lowerCAmelCase     )

   hf_wavavec.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
     _snake_case  :   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  :   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
     )








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

