





from __future__ import annotations

from typing import Any



class  A__       :





      def __init__(			self	,							A_	,							A_	,							A_ = 0       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Dict	     =       row, column
       UpperCamelCase     : List[Any]	     =       [[default_value for c in range(A_       )] for r in range(A_       )]





      def __str__(			self       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

       # Make string identifier
       UpperCamelCase     : str	     =       0
       for row_vector in self.array:
        for obj in row_vector:
         UpperCamelCase     : Optional[Any]	     =       max(A_	,							len(str(A_       )       )       )
       UpperCamelCase     : Tuple	     =       F"""%{max_element_length}s"""

       # Make string and return
       def single_line(A_       ) -> str:
        nonlocal string_format_identifier
        UpperCamelCase     : Dict	     =       "["
        line += ", ".join(string_format_identifier % (obj,) for obj in row_vector       )
        line += "]"
        return line

       s += "\n".join(single_line(A_       ) for row_vector in self.array       )
       return s





      def __repr__(			self       ):
       '''simple docstring'''




       return str(self       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if not (isinstance(A_	,							(list, tuple)       ) and len(A_       ) == 2):
        return False
       elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
        return False
       else:
        return True





      def __getitem__(			self	,							A_       ):
       '''simple docstring'''




       assert self.validate_indicies(A_       )
       return self.array[loc[0]][loc[1]]





      def __setitem__(			self	,							A_	,							A_       ):
       '''simple docstring'''




       assert self.validate_indicies(A_       )
       UpperCamelCase     : Optional[Any]	     =       value





      def __add__(			self	,							A_       ):
       '''simple docstring'''




       assert isinstance(A_	,							A_       )
       assert self.row == another.row and self.column == another.column

       # Add
       UpperCamelCase     : List[Any]	     =       Matrix(self.row	,							self.column       )
       for r in range(self.row       ):
        for c in range(self.column       ):
         UpperCamelCase     : Tuple	     =       self[r, c] + another[r, c]
       return result





      def __neg__(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       Matrix(self.row	,							self.column       )
       for r in range(self.row       ):
        for c in range(self.column       ):
         UpperCamelCase     : Optional[int]	     =       -self[r, c]
       return result





      def __sub__(			self	,							A_       ):
       '''simple docstring'''




       return self + (-another)





      def __mul__(			self	,							A_       ):
       '''simple docstring'''




       if isinstance(A_	,							(int, float)       ):  # Scalar multiplication
        UpperCamelCase     : Dict	     =       Matrix(self.row	,							self.column       )
        for r in range(self.row       ):
         for c in range(self.column       ):
          UpperCamelCase     : Union[str, Any]	     =       self[r, c] * another
        return result
       elif isinstance(A_	,							A_       ):  # Matrix multiplication
        assert self.column == another.row
        UpperCamelCase     : Optional[Any]	     =       Matrix(self.row	,							another.column       )
        for r in range(self.row       ):
         for c in range(another.column       ):
          for i in range(self.column       ):
           result[r, c] += self[r, i] * another[i, c]
        return result
       else:
        UpperCamelCase     : List[Any]	     =       F"""Unsupported type given for another ({type(A_       )})"""
        raise TypeError(A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       Matrix(self.column	,							self.row       )
       for r in range(self.row       ):
        for c in range(self.column       ):
         UpperCamelCase     : int	     =       self[r, c]
       return result





      def 	__UpperCamelCase(			self	,							A_	,							A_       ):
       '''simple docstring'''




       assert isinstance(A_	,							A_       ) and isinstance(A_	,							A_       )
       assert self.row == self.column == u.row == v.row  # u, v should be column vector
       assert u.column == v.column == 1  # u, v should be column vector

       # Calculate
       UpperCamelCase     : Union[str, Any]	     =       v.transpose()
       UpperCamelCase     : Dict	     =       (v_t * self * u)[0, 0] + 1
       if numerator_factor == 0:
        return None  # It's not invertable
       return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



    def 		A_    (					)						->			None:
     # a^(-1)
     UpperCamelCase     : List[str]	     =       Matrix(3							,       3							,       0					)
     for i in range(3					):
      UpperCamelCase     : Union[str, Any]	     =       1
     print(F"""a^(-1) is {ainv}"""					)
     # u, v
     UpperCamelCase     : Any	     =       Matrix(3							,       1							,       0					)
     UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : int	     =       1, 2, -3
     UpperCamelCase     : List[Any]	     =       Matrix(3							,       1							,       0					)
     UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Optional[Any]	     =       4, -2, 5
     print(F"""u is {u}"""					)
     print(F"""v is {v}"""					)
     print(F"""uv^T is {u * v.transpose()}"""					)
     # Sherman Morrison
     print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowerCAmelCase							,       _lowerCAmelCase					)}"""					)



    def 		A_    (					)						->			None:
     import doctest

     doctest.testmod()


    testa()







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates					)					):
  UpperCamelCase     : Any	     =       checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCamelCase     : List[Any]	     =       checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
  for j in range(len(config.resblock_dilation_sizes					)					):
   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCamelCase     : str	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()

 model.save_pretrained(_lowerCAmelCase					)

 if repo_id:
  print("Pushing to the hub..."					)
  model.push_to_hub(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
    parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
    parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
