


'''simple docstring'''
import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
UpperCAmelCase_ :     int								=      logging.get_logger('transformers.models.encodec')

UpperCAmelCase_ :     List[Any]								=      {
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
UpperCAmelCase_ :     str								=      {
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
UpperCAmelCase_ :     List[str]								=      {
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
UpperCAmelCase_ :     List[Any]								=      {
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
UpperCAmelCase_ :     List[str]								=      {
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
UpperCAmelCase_ :     List[Any]								=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
UpperCAmelCase_ :     List[str]								=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
UpperCAmelCase_ :     Optional[int]								=      []
UpperCAmelCase_ :     Dict								=      []


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					str,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					List[Any]			):
 for attribute in key.split('.'			):
  __A  :		str       =			getattr(UpperCamelCase__,				UpperCamelCase__			)

 if weight_type is not None:
  __A  :		Tuple       =			getattr(UpperCamelCase__,				UpperCamelCase__			).shape
 else:
  __A  :		str       =			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":
  __A  :		int       =			value
 elif weight_type == "weight_g":
  __A  :		List[str]       =			value
 elif weight_type == "weight_v":
  __A  :		Optional[int]       =			value
 elif weight_type == "bias":
  __A  :		Optional[Any]       =			value
 elif weight_type == "running_mean":
  __A  :		int       =			value
 elif weight_type == "running_var":
  __A  :		Any       =			value
 elif weight_type == "num_batches_tracked":
  __A  :		List[Any]       =			value
 elif weight_type == "weight_ih_l0":
  __A  :		Any       =			value
 elif weight_type == "weight_hh_l0":
  __A  :		Union[str, Any]       =			value
 elif weight_type == "bias_ih_l0":
  __A  :		int       =			value
 elif weight_type == "bias_hh_l0":
  __A  :		List[Any]       =			value
 elif weight_type == "weight_ih_l1":
  __A  :		Union[str, Any]       =			value
 elif weight_type == "weight_hh_l1":
  __A  :		List[Any]       =			value
 elif weight_type == "bias_ih_l1":
  __A  :		Tuple       =			value
 elif weight_type == "bias_hh_l1":
  __A  :		int       =			value
 else:
  __A  :		Union[str, Any]       =			value

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


def 			_lowercase       (				UpperCamelCase__       :					str,				UpperCamelCase__       :					Any			):
 for key in ignore_keys:
  if key.endswith('.*'			):
   if name.startswith(key[:-1]			):
    return True
  elif ".*." in key:
   __A  :		Any       =			key.split('.*.'			)
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False


def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					List[Any]			):
 __A  :		Dict       =			[]

 if model_name == "encodec_24khz" or "encodec_32khz":
  __A  :		Optional[Any]       =			MAPPING_24K
 elif model_name == "encodec_48khz":
  __A  :		Union[str, Any]       =			MAPPING_48K
 else:
  raise ValueError(f"""Unsupported model: {model_name}"""			)

 for name, value in orig_dict.items():
  if should_ignore(UpperCamelCase__,				UpperCamelCase__			):
   logger.info(f"""{name} was ignored"""			)
   continue

  __A  :		int       =			False
  for key, mapped_key in MAPPING.items():
   if "*" in key:
    __A  :		str       =			key.split('.*.'			)
    if prefix in name and suffix in name:
     __A  :		Dict       =			suffix

   if key in name:
    # HACK otherwise .embed gets initialized with .embed_avg too
    if key.endswith('embed'			) and name.endswith('embed_avg'			):
     continue

    __A  :		List[Any]       =			True
    if "*" in mapped_key:
     __A  :		List[str]       =			name.split(UpperCamelCase__			)[0].split('.'			)[-2]
     __A  :		Tuple       =			mapped_key.replace('*',				UpperCamelCase__			)
    if "weight_g" in name:
     __A  :		Any       =			'weight_g'
    elif "weight_v" in name:
     __A  :		Optional[int]       =			'weight_v'
    elif "weight_ih_l0" in name:
     __A  :		Optional[Any]       =			'weight_ih_l0'
    elif "weight_hh_l0" in name:
     __A  :		List[Any]       =			'weight_hh_l0'
    elif "bias_ih_l0" in name:
     __A  :		Optional[Any]       =			'bias_ih_l0'
    elif "bias_hh_l0" in name:
     __A  :		Optional[int]       =			'bias_hh_l0'
    elif "weight_ih_l1" in name:
     __A  :		Tuple       =			'weight_ih_l1'
    elif "weight_hh_l1" in name:
     __A  :		Optional[Any]       =			'weight_hh_l1'
    elif "bias_ih_l1" in name:
     __A  :		Tuple       =			'bias_ih_l1'
    elif "bias_hh_l1" in name:
     __A  :		Dict       =			'bias_hh_l1'
    elif "bias" in name:
     __A  :		Optional[Any]       =			'bias'
    elif "weight" in name:
     __A  :		Tuple       =			'weight'
    elif "running_mean" in name:
     __A  :		List[str]       =			'running_mean'
    elif "running_var" in name:
     __A  :		Any       =			'running_var'
    elif "num_batches_tracked" in name:
     __A  :		Dict       =			'num_batches_tracked'
    else:
     __A  :		Union[str, Any]       =			None
    set_recursively(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
   continue
  if not is_used:
   unused_weights.append(UpperCamelCase__			)

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


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str=None,				UpperCamelCase__       :					str=None,				):
 if config_path is not None:
  __A  :		List[Any]       =			EncodecConfig.from_pretrained(UpperCamelCase__			)
 else:
  __A  :		Any       =			EncodecConfig()

 if model_name == "encodec_24khz":
  pass  # config is already correct
 elif model_name == "encodec_32khz":
  __A  :		Tuple       =			[8, 5, 4, 4]
  __A  :		int       =			[2.2]
  __A  :		str       =			64
  __A  :		List[Any]       =			32000
  __A  :		Dict       =			2048
  __A  :		int       =			False
  __A  :		str       =			False
  __A  :		str       =			False
 elif model_name == "encodec_48khz":
  __A  :		List[str]       =			[8, 5, 4, 2]
  __A  :		Union[str, Any]       =			[3.0, 6.0, 12.0, 24.0]
  __A  :		Optional[int]       =			48000
  __A  :		Tuple       =			2
  __A  :		Tuple       =			False
  __A  :		List[Any]       =			'time_group_norm'
  __A  :		Tuple       =			True
  __A  :		Optional[Any]       =			1.0
  __A  :		str       =			0.01
 else:
  raise ValueError(f"""Unknown model name: {model_name}"""			)

 __A  :		Tuple       =			EncodecModel(UpperCamelCase__			)

 __A  :		int       =			EncodecFeatureExtractor(
     feature_size=config.audio_channels,				sampling_rate=config.sampling_rate,				chunk_length_s=config.chunk_length_s,				overlap=config.overlap,				)
 feature_extractor.save_pretrained(UpperCamelCase__			)

 __A  :		Union[str, Any]       =			torch.load(UpperCamelCase__			)
 if "best_state" in original_checkpoint:
  # we might have a training state saved, in which case discard the yaml results and just retain the weights
  __A  :		str       =			original_checkpoint['best_state']
 recursively_load_weights(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 model.save_pretrained(UpperCamelCase__			)

 if repo_id:
  print('Pushing to the hub...'			)
  feature_extractor.push_to_hub(UpperCamelCase__			)
  model.push_to_hub(UpperCamelCase__			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      argparse.ArgumentParser()
 parser.add_argument(
     '--model',
     default='encodec_24khz',
     type=str,
     help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
 )
 parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
 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.'
 )

 UpperCAmelCase_ :     Tuple								=      parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )










'''simple docstring'''
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase = None						):

  """simple docstring"""







  if components is None:
   __A  :		Optional[int]       =			[]
  __A  :		Dict       =			list(__lowercase						)


 def __len__(      self						):

  """simple docstring"""







  return len(self.__components						)


 def __str__(      self						):

  """simple docstring"""







  return "(" + ",".join(map(__lowercase      ,      self.__components						)						) + ")"


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			len(self						)
  if size == len(__lowercase						):
   __A  :		Dict       =			[self.__components[i] + other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:
   raise Exception('must have the same size'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		int       =			len(self						)
  if size == len(__lowercase						):
   __A  :		str       =			[self.__components[i] - other.component(__lowercase						) for i in range(__lowercase						)]
   return Vector(__lowercase						)
  else:  # error case
   raise Exception('must have the same size'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      (float, int)						):
   __A  :		Dict       =			[c * other for c in self.__components]
   return Vector(__lowercase						)
  elif isinstance(__lowercase      ,      __lowercase						) and len(self						) == len(__lowercase						):
   __A  :		Optional[Any]       =			len(self						)
   __A  :		Optional[int]       =			[self.__components[i] * other.component(__lowercase						) for i in range(__lowercase						)]
   return sum(__lowercase						)
  else:  # error case
   raise Exception('invalid operand!'						)


 def  snake_case__   (      self						):

  """simple docstring"""







  return Vector(self.__components						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						) and -len(self.__components						) <= i < len(self.__components						):
   return self.__components[i]
  else:
   raise Exception('index out of range'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  assert -len(self.__components						) <= pos < len(self.__components						)
  __A  :		str       =			value


 def  snake_case__   (      self						):

  """simple docstring"""







  if len(self.__components						) == 0:
   raise Exception('Vector is empty'						)
  __A  :		List[Any]       =			[c**2 for c in self.__components]
  return math.sqrt(sum(__lowercase						)						)







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		List[Any]       =			self * other
  __A  :		Any       =			self.euclidean_length() * other.euclidean_length()
  if deg:
   return math.degrees(math.acos(num / den						)						)
  else:
   return math.acos(num / den						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			)
 return Vector([0] * dimension			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 assert isinstance(UpperCamelCase__,				UpperCamelCase__			) and (isinstance(UpperCamelCase__,				UpperCamelCase__			))
 __A  :		List[str]       =			[0] * dimension
 __A  :		Tuple       =			1
 return Vector(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					float,				UpperCamelCase__       :					Vector,				UpperCamelCase__       :					Vector			):
 assert (
     isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and isinstance(UpperCamelCase__,				UpperCamelCase__			)
     and (isinstance(UpperCamelCase__,				(int, float)			))
 )
 return x * scalar + y


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		Any       =			[random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)]
 return Vector(UpperCamelCase__			)

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			matrix
  __A  :		str       =			w
  __A  :		Dict       =			h


 def __str__(      self						):

  """simple docstring"""







  __A  :		Tuple       =			''
  for i in range(self.__height						):
   ans += "|"
   for j in range(self.__width						):
    if j < self.__width - 1:
     ans += str(self.__matrix[i][j]						) + ","
    else:
     ans += str(self.__matrix[i][j]						) + "|\n"
  return ans


 def __add__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Union[str, Any]       =			[]
   for i in range(self.__height						):
    __A  :		List[Any]       =			[
        self.__matrix[i][j] + other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrix must have the same dimension!'						)


 def __sub__(      self      ,      __lowercase						):

  """simple docstring"""







  if self.__width == other.width() and self.__height == other.height():
   __A  :		Dict       =			[]
   for i in range(self.__height						):
    __A  :		str       =			[
        self.__matrix[i][j] - other.component(__lowercase      ,      __lowercase						)
        for j in range(self.__width						)
    ]
    matrix.append(__lowercase						)
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  else:
   raise Exception('matrices must have the same dimension!'						)


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 @overload
 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  ...


 def __mul__(      self      ,      __lowercase						):

  """simple docstring"""







  if isinstance(__lowercase      ,      __lowercase						):  # matrix-vector
   if len(__lowercase						) == self.__width:
    __A  :		Tuple       =			zero_vector(self.__height						)
    for i in range(self.__height						):
     __A  :		Any       =			[
         self.__matrix[i][j] * other.component(__lowercase						)
         for j in range(self.__width						)
     ]
     ans.change_component(__lowercase      ,      sum(__lowercase						)						)
    return ans
   else:
    raise Exception(
        'vector must have the same size as the '
        'number of columns of the matrix!'						)
  elif isinstance(__lowercase      ,      (int, float)						):  # matrix-scalar
   __A  :		Optional[Any]       =			[
       [self.__matrix[i][j] * other for j in range(self.__width						)]
       for i in range(self.__height						)
   ]
   return Matrix(__lowercase      ,      self.__width      ,      self.__height						)
  return None


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__height


 def  snake_case__   (      self						):

  """simple docstring"""







  return self.__width


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   return self.__matrix[x][y]
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if 0 <= x < self.__height and 0 <= y < self.__width:
   __A  :		Dict       =			value
  else:
   raise Exception('change_component: indices out of bounds'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  __A  :		Union[str, Any]       =			self.__matrix[:x] + self.__matrix[x + 1 :]
  for i in range(len(__lowercase						)						):
   __A  :		Dict       =			minor[i][:y] + minor[i][y + 1 :]
  return Matrix(__lowercase      ,      self.__width - 1      ,      self.__height - 1						).determinant()


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if 0 <= x < self.__height and 0 <= y < self.__width:
   return (-1) ** (x + y) * self.minor(__lowercase      ,      __lowercase						)
  else:
   raise Exception('Indices out of bounds'						)







 def  snake_case__   (      self						):

  """simple docstring"""







  if self.__height != self.__width:
   raise Exception('Matrix is not square'						)
  if self.__height < 1:
   raise Exception('Matrix has no element'						)
  elif self.__height == 1:
   return self.__matrix[0][0]
  elif self.__height == 2:
   return (
       self.__matrix[0][0] * self.__matrix[1][1]
       - self.__matrix[0][1] * self.__matrix[1][0]
   )
  else:
   __A  :		List[str]       =			[
       self.__matrix[0][y] * self.cofactor(0      ,      __lowercase						) for y in range(self.__width						)
   ]
   return sum(__lowercase						)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		list[list[float]]       =			[[0] * n for _ in range(UpperCamelCase__			)]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 random.seed(UpperCamelCase__			)
 __A  :		list[list[float]]       =			[
     [random.randint(UpperCamelCase__,				UpperCamelCase__			) for _ in range(UpperCamelCase__			)] for _ in range(UpperCamelCase__			)
 ]
 return Matrix(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)






