"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

SCREAMING_SNAKE_CASE									=						{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	for attribute in key.split('.'			):
		_lowercase :    List[str]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			)

	if weight_type is not None:
		_lowercase :    List[str]							=							getattr(__UpperCAmelCase     ,__UpperCAmelCase			).shape
	else:
		_lowercase :    List[str]							=							hf_pointer.shape

	assert hf_shape == value.shape, (
	    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":
		_lowercase :    Optional[Any]							=							value
	elif weight_type == "weight_g":
		_lowercase :    Tuple							=							value
	elif weight_type == "weight_v":
		_lowercase :    List[Any]							=							value
	elif weight_type == "bias":
		_lowercase :    List[Any]							=							value
	else:
		_lowercase :    str							=							value

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[Any]							=							[]
	_lowercase :    int							=							fairseq_model.state_dict()

	_lowercase :    str							=							hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_lowercase :    Dict							=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,hf_model.config.feat_extract_norm == 'group'     ,)
			_lowercase :    str							=							True
		else:
			for key, mapped_key in MAPPING.items():
				_lowercase :    Optional[int]							=							'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

				if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
					_lowercase :    List[Any]							=							True
					if "*" in mapped_key:
						_lowercase :    Dict							=							name.split(__UpperCAmelCase			)[0].split('.'			)[-2]
						_lowercase :    List[Any]							=							mapped_key.replace('*'     ,__UpperCAmelCase			)
					if "weight_g" in name:
						_lowercase :    List[Any]							=							'weight_g'
					elif "weight_v" in name:
						_lowercase :    Union[str, Any]							=							'weight_v'
					elif "weight" in name:
						_lowercase :    Optional[Any]							=							'weight'
					elif "bias" in name:
						_lowercase :    str							=							'bias'
					else:
						_lowercase :    int							=							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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							full_name.split('conv_layers.'			)[-1]
	_lowercase :    Tuple							=							name.split('.'			)
	_lowercase :    Union[str, Any]							=							int(items[0]			)
	_lowercase :    List[str]							=							int(items[1]			)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
			)
			_lowercase :    Dict							=							value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
			)
			_lowercase :    Tuple							=							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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
			    " found."
			)
			_lowercase :    Any							=							value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
			)
			_lowercase :    Any							=							value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
	else:
		unused_weights.append(__UpperCAmelCase			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    str							=							SEWConfig()
	if is_finetuned:
		_lowercase :    Dict							=							model.wav_encoder.wav_model.cfg
	else:
		_lowercase :    Tuple							=							model.cfg

	_lowercase :    Dict							=							fs_config.conv_bias
	_lowercase :    int							=							eval(fs_config.conv_feature_layers			)
	_lowercase :    Dict							=							[x[0] for x in conv_layers]
	_lowercase :    int							=							[x[1] for x in conv_layers]
	_lowercase :    Tuple							=							[x[2] for x in conv_layers]
	_lowercase :    int							=							'gelu'
	_lowercase :    str							=							'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_lowercase :    Optional[int]							=							0.0
	_lowercase :    Dict							=							fs_config.activation_fn.name
	_lowercase :    Tuple							=							fs_config.encoder_embed_dim
	_lowercase :    Dict							=							0.0_2
	_lowercase :    Union[str, Any]							=							fs_config.encoder_ffn_embed_dim
	_lowercase :    Dict							=							1E-5
	_lowercase :    Tuple							=							fs_config.encoder_layerdrop
	_lowercase :    List[str]							=							fs_config.encoder_attention_heads
	_lowercase :    List[Any]							=							fs_config.conv_pos_groups
	_lowercase :    Dict							=							fs_config.conv_pos
	_lowercase :    Union[str, Any]							=							len(__UpperCAmelCase			)
	_lowercase :    Union[str, Any]							=							fs_config.encoder_layers
	_lowercase :    Tuple							=							fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		_lowercase :    Optional[Any]							=							model.cfg
		_lowercase :    int							=							fs_config.final_dropout
		_lowercase :    Tuple							=							fs_config.layerdrop
	_lowercase :    Optional[int]							=							fs_config.activation_dropout
	_lowercase :    List[Any]							=							fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	_lowercase :    Tuple							=							fs_config.attention_dropout
	_lowercase :    int							=							fs_config.dropout_input
	_lowercase :    Union[str, Any]							=							fs_config.dropout
	_lowercase :    Optional[int]							=							fs_config.mask_channel_length
	_lowercase :    List[str]							=							fs_config.mask_channel_prob
	_lowercase :    Optional[Any]							=							fs_config.mask_length
	_lowercase :    List[str]							=							fs_config.mask_prob

	_lowercase :    Any							=							'Wav2Vec2FeatureExtractor'
	_lowercase :    Optional[int]							=							'Wav2Vec2CTCTokenizer'

	return config


@torch.no_grad()
def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=None     ,__UpperCAmelCase=None     ,__UpperCAmelCase=True			):






	"""simple docstring"""





	if is_finetuned:
		_lowercase				,      _lowercase				,      _lowercase :    Optional[Any]							=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]     ,arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
	else:
		_lowercase				,      _lowercase				,      _lowercase :    str							=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

	if config_path is not None:
		_lowercase :    Union[str, Any]							=							SEWConfig.from_pretrained(__UpperCAmelCase			)
	else:
		_lowercase :    Optional[Any]							=							convert_config(model[0]     ,__UpperCAmelCase			)
	_lowercase :    str							=							model[0].eval()

	_lowercase :    Tuple							=							True if config.feat_extract_norm == 'layer' else False
	_lowercase :    List[str]							=							WavaVecaFeatureExtractor(
	    feature_size=1     ,sampling_rate=16_000     ,padding_value=0     ,do_normalize=__UpperCAmelCase     ,return_attention_mask=__UpperCAmelCase     ,)

	if is_finetuned:
		if dict_path:
			_lowercase :    Dict							=							Dictionary.load(__UpperCAmelCase			)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_lowercase :    Tuple							=							target_dict.pad_index
			_lowercase :    str							=							target_dict.bos_index
			_lowercase :    List[str]							=							target_dict.pad_index
			_lowercase :    List[Any]							=							target_dict.bos_index
			_lowercase :    Any							=							target_dict.eos_index
			_lowercase :    int							=							len(target_dict.symbols			)
			_lowercase :    Optional[int]							=							os.path.join(__UpperCAmelCase     ,'vocab.json'			)
			if not os.path.isdir(__UpperCAmelCase			):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__UpperCAmelCase			)			)
				return
			os.makedirs(__UpperCAmelCase     ,exist_ok=__UpperCAmelCase			)
			with open(__UpperCAmelCase     ,'w'     ,encoding='utf-8'			) as vocab_handle:
				json.dump(target_dict.indices     ,__UpperCAmelCase			)
			_lowercase :    List[Any]							=							WavaVecaCTCTokenizer(
			    __UpperCAmelCase     ,unk_token=target_dict.unk_word     ,pad_token=target_dict.pad_word     ,bos_token=target_dict.bos_word     ,eos_token=target_dict.eos_word     ,word_delimiter_token='|'     ,do_lower_case=__UpperCAmelCase     ,)
			_lowercase :    List[str]							=							WavaVecaProcessor(feature_extractor=__UpperCAmelCase     ,tokenizer=__UpperCAmelCase			)
			processor.save_pretrained(__UpperCAmelCase			)

		_lowercase :    str							=							SEWForCTC(__UpperCAmelCase			)
	else:
		_lowercase :    int							=							SEWModel(__UpperCAmelCase			)
		feature_extractor.save_pretrained(__UpperCAmelCase			)

	recursively_load_weights(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			)

	hf_model.save_pretrained(__UpperCAmelCase			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						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(
						    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						SCREAMING_SNAKE_CASE									=						parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)




"""simple docstring"""







from __future__ import annotations

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



class  _lowerCamelCase			:







				def __init__( self    :       str			,					lowerCamelCase_    :       Collection[float] | None = None						):

					"""simple docstring"""






					if components is None:
						_lowercase :    Any							=							[]
					_lowercase :    int							=							list(lowerCamelCase_						)







				def __len__( self    :       int						):

					"""simple docstring"""






					return len(self.__components						)







				def __str__( self    :       List[Any]						):

					"""simple docstring"""






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







				def __add__( self    :       Dict			,					lowerCamelCase_    :       Vector						):

					"""simple docstring"""






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







				def __sub__( self    :       int			,					lowerCamelCase_    :       Vector						):

					"""simple docstring"""






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







				@overload
				def __mul__( self    :       Tuple			,					lowerCamelCase_    :       float						):

					"""simple docstring"""






					...







				@overload
				def __mul__( self    :       Tuple			,					lowerCamelCase_    :       Vector						):

					"""simple docstring"""






					...







				def __mul__( self    :       int			,					lowerCamelCase_    :       float | Vector						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return Vector(self.__components						)







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       float						):

					"""simple docstring"""






					assert -len(self.__components						) <= pos < len(self.__components						)
					_lowercase :    Tuple							=							value







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					if len(self.__components						) == 0:
						raise Exception('Vector is empty'						)
					_lowercase :    Optional[int]							=							[c**2 for c in self.__components]
					return math.sqrt(sum(lowerCamelCase_						)						)







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       Vector			,					lowerCamelCase_    :       bool = False						):

					"""simple docstring"""






					_lowercase :    int							=							self * other
					_lowercase :    Any							=							self.euclidean_length() * other.euclidean_length()
					if deg:
						return math.degrees(math.acos(num / den						)						)
					else:
						return math.acos(num / den						)



def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	assert isinstance(__UpperCAmelCase     ,__UpperCAmelCase			)
	return Vector([0] * dimension			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	assert isinstance(__UpperCAmelCase     ,__UpperCAmelCase			) and (isinstance(__UpperCAmelCase     ,__UpperCAmelCase			))
	_lowercase :    str							=							[0] * dimension
	_lowercase :    List[Any]							=							1
	return Vector(__UpperCAmelCase			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	assert (
	    isinstance(__UpperCAmelCase     ,__UpperCAmelCase			)
	    and isinstance(__UpperCAmelCase     ,__UpperCAmelCase			)
	    and (isinstance(__UpperCAmelCase     ,(int, float)			))
	)
	return x * scalar + y


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	random.seed(__UpperCAmelCase			)
	_lowercase :    Tuple							=							[random.randint(__UpperCAmelCase     ,__UpperCAmelCase			) for _ in range(__UpperCAmelCase			)]
	return Vector(__UpperCAmelCase			)



class  _lowerCamelCase			:







				def __init__( self    :       Dict			,					lowerCamelCase_    :       list[list[float]]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






					_lowercase :    Tuple							=							matrix
					_lowercase :    str							=							w
					_lowercase :    Tuple							=							h







				def __str__( self    :       int						):

					"""simple docstring"""






					_lowercase :    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    :       Dict			,					lowerCamelCase_    :       Matrix						):

					"""simple docstring"""






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







				def __sub__( self    :       str			,					lowerCamelCase_    :       Matrix						):

					"""simple docstring"""






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







				@overload
				def __mul__( self    :       Union[str, Any]			,					lowerCamelCase_    :       float						):

					"""simple docstring"""






					...







				@overload
				def __mul__( self    :       List[str]			,					lowerCamelCase_    :       Vector						):

					"""simple docstring"""






					...







				def __mul__( self    :       int			,					lowerCamelCase_    :       float | Vector						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					return self.__height







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					return self.__width







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""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 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       float						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int						):

					"""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(lowerCamelCase_			,					lowerCamelCase_						)
					else:
						raise Exception('Indices out of bounds'						)







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""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:
						_lowercase :    Union[str, Any]							=							[
						    self.__matrix[0][y] * self.cofactor(0			,					lowerCamelCase_						) for y in range(self.__width						)
						]
						return sum(lowerCamelCase_						)



def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    list[list[float]]							=							[[0] * n for _ in range(__UpperCAmelCase			)]
	return Matrix(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			)







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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



