


from __future__ import annotations







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   TypeError(
           '''Matrices must be formed from a list of zero or more lists containing at '''
           '''least one and the same number of values, each of which must be of type '''
           '''int or float.'''      )
       if len(_UpperCAmelCase      ) != 0:
             snake_case_ =   len(rows[0]      )
             if cols == 0:
                   raise error
             for row in rows:
                   if len(_UpperCAmelCase      ) != cols:
                         raise error
                   for value in row:
                         if not isinstance(_UpperCAmelCase	,   (int, float)      ):
                               raise error
             snake_case_ =   rows
       else:
             snake_case_ =   []



 def 		UpperCamelCase__  (			self      ):
       return [[row[i] for row in self.rows] for i in range(len(self.rows[0]      )      )]



 @property
 def 		UpperCamelCase__  (			self      ):
       return len(self.rows      )



 @property
 def 		UpperCamelCase__  (			self      ):
       return len(self.rows[0]      )



 @property
 def 		UpperCamelCase__  (			self      ):
       return (self.num_rows, self.num_columns)



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.order[0] == self.order[1]



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   [
           [0 if column_num != row_num else 1 for column_num in range(self.num_rows      )]
           for row_num in range(self.num_rows      )
       ]
       return Matrix(_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       if not self.is_square:
             return 0
       if self.order == (0, 0):
             return 1
       if self.order == (1, 1):
             return int(self.rows[0][0]      )
       if self.order == (2, 2):
             return int(
                 (self.rows[0][0] * self.rows[1][1])
                 - (self.rows[0][1] * self.rows[1][0])      )
       else:
             return sum(
                 self.rows[0][column] * self.cofactors().rows[0][column]
                 for column in range(self.num_columns      )      )



 def 		UpperCamelCase__  (			self      ):
       return bool(self.determinant()      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   [
           [
               self.rows[other_row][other_column]
               for other_column in range(self.num_columns      )
               if other_column != column
           ]
           for other_row in range(self.num_rows      )
           if other_row != row
       ]
       return Matrix(_UpperCAmelCase      ).determinant()



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       if (row + column) % 2 == 0:
             return self.get_minor(_UpperCAmelCase	,   _UpperCAmelCase      )
       return -1 * self.get_minor(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       return Matrix(
           [
               [self.get_minor(_UpperCAmelCase	,   _UpperCAmelCase      ) for column in range(self.num_columns      )]
               for row in range(self.num_rows      )
           ]      )



 def 		UpperCamelCase__  (			self      ):
       return Matrix(
           [
               [
                   self.minors().rows[row][column]
                   if (row + column) % 2 == 0
                   else self.minors().rows[row][column] * -1
                   for column in range(self.minors().num_columns      )
               ]
               for row in range(self.minors().num_rows      )
           ]      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   [
           [self.cofactors().rows[column][row] for column in range(self.num_columns      )]
           for row in range(self.num_rows      )
       ]
       return Matrix(_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.determinant()
       if not determinant:
             raise TypeError('''Only matrices with a non-zero determinant have an inverse'''      )
       return self.adjugate() * (1 / determinant)



 def __repr__(			self      ):
       return str(self.rows      )



 def __str__(			self      ):
       if self.num_rows == 0:
             return "[]"
       if self.num_rows == 1:
             return "[[" + ". ".join(str(self.rows[0]      )      ) + "]]"
       return (
           "["
           + "\n ".join(
               [
                   '''[''' + '''. '''.join([str(_UpperCAmelCase      ) for value in row]      ) + '''.]'''
                   for row in self.rows
               ]      )
           + "]"
       )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   TypeError('''Row must be a list containing all ints and/or floats'''      )
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             raise type_error
       for value in row:
             if not isinstance(_UpperCAmelCase	,   (int, float)      ):
                   raise type_error
       if len(_UpperCAmelCase      ) != self.num_columns:
             raise ValueError(
                 '''Row must be equal in length to the other rows in the matrix'''      )
       if position is None:
             self.rows.append(_UpperCAmelCase      )
       else:
             snake_case_ =   self.rows[0:position] + [row] + self.rows[position:]



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = None      ):
       snake_case_ =   TypeError(
           '''Column must be a list containing all ints and/or floats'''      )
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             raise type_error
       for value in column:
             if not isinstance(_UpperCAmelCase	,   (int, float)      ):
                   raise type_error
       if len(_UpperCAmelCase      ) != self.num_rows:
             raise ValueError(
                 '''Column must be equal in length to the other columns in the matrix'''      )
       if position is None:
             snake_case_ =   [self.rows[i] + [column[i]] for i in range(self.num_rows      )]
       else:
             snake_case_ =   [
                 self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                 for i in range(self.num_rows      )
             ]



 def __eq__(			self	,   _UpperCAmelCase      ):
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             return NotImplemented
       return self.rows == other.rows



 def __ne__(			self	,   _UpperCAmelCase      ):
       return not self == other



 def __neg__(			self      ):
       return self * -1



 def __add__(			self	,   _UpperCAmelCase      ):
       if self.order != other.order:
             raise ValueError('''Addition requires matrices of the same order'''      )
       return Matrix(
           [
               [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns      )]
               for i in range(self.num_rows      )
           ]      )



 def __sub__(			self	,   _UpperCAmelCase      ):
       if self.order != other.order:
             raise ValueError('''Subtraction requires matrices of the same order'''      )
       return Matrix(
           [
               [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns      )]
               for i in range(self.num_rows      )
           ]      )



 def __mul__(			self	,   _UpperCAmelCase      ):
       if isinstance(_UpperCAmelCase	,   (int, float)      ):
             return Matrix(
                 [[int(element * other      ) for element in row] for row in self.rows]      )
       elif isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             if self.num_columns != other.num_rows:
                   raise ValueError(
                       '''The number of columns in the first matrix must '''
                       '''be equal to the number of rows in the second'''      )
             return Matrix(
                 [
                     [Matrix.dot_product(_UpperCAmelCase	,   _UpperCAmelCase      ) for column in other.columns()]
                     for row in self.rows
                 ]      )
       else:
             raise TypeError(
                 '''A Matrix can only be multiplied by an int, float, or another matrix'''      )



 def __pow__(			self	,   _UpperCAmelCase      ):
       if not isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             raise TypeError('''A Matrix can only be raised to the power of an int'''      )
       if not self.is_square:
             raise ValueError('''Only square matrices can be raised to a power'''      )
       if other == 0:
             return self.identity()
       if other < 0:
             if self.is_invertable():
                   return self.inverse() ** (-other)
             raise ValueError(
                 '''Only invertable matrices can be raised to a negative power'''      )
       snake_case_ =   self
       for _ in range(other - 1      ):
             result *= self
       return result





 @classmethod
 def 		UpperCamelCase__  (			cls	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       return sum(row[i] * column[i] for i in range(len(_UpperCAmelCase      )      )      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """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""",
}
UpperCAmelCase    =					[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      for attribute in key.split('''.'''      ):
            snake_case_ =   getattr(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

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

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      snake_case_ =   []
      snake_case_ =   fairseq_model.state_dict()

      snake_case_ =   hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            snake_case_ =   False
            if "conv_layers" in name:
                  load_conv_layer(
                      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      hf_model.config.feat_extract_norm == '''group'''      ,      )
                  snake_case_ =   True
            else:
                  for key, mapped_key in MAPPING.items():
                        snake_case_ =   '''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
                              snake_case_ =   True
                              if "*" in mapped_key:
                                    snake_case_ =   name.split(SCREAMING_SNAKE_CASE      )[0].split('''.'''      )[-2]
                                    snake_case_ =   mapped_key.replace('''*'''      ,      SCREAMING_SNAKE_CASE      )
                              if "weight_g" in name:
                                    snake_case_ =   '''weight_g'''
                              elif "weight_v" in name:
                                    snake_case_ =   '''weight_v'''
                              elif "bias" in name:
                                    snake_case_ =   '''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    snake_case_ =   '''weight'''
                              else:
                                    snake_case_ =   None
                              set_recursively(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
                        continue
            if not is_used:
                  unused_weights.append(SCREAMING_SNAKE_CASE      )

      logger.warning(f'''Unused weights: {unused_weights}'''      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   full_name.split('''conv_layers.'''      )[-1]
      snake_case_ =   name.split('''.'''      )
      snake_case_ =   int(items[0]      )
      snake_case_ =   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.'''      )
                  snake_case_ =   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.'''      )
                  snake_case_ =   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.'''      )
                  snake_case_ =   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.'''      )
                  snake_case_ =   value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
      else:
            unused_weights.append(SCREAMING_SNAKE_CASE      )






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=True      )->						Dict:




      """simple docstring"""

      if config_path is not None:
            snake_case_ =   UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatConfig()

      snake_case_ =   ''''''

      if is_finetuned:
            snake_case_ =   UniSpeechSatForCTC(SCREAMING_SNAKE_CASE      )
      else:
            snake_case_ =   UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE      )

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

      recursively_load_weights(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )

      hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   UpperCAmelCase    =					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"""
   )
   UpperCAmelCase    =					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
   )