
import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def 						A_			(				snake_case				: NDArray[floataa]			,   snake_case				: NDArray[floataa]			)   ->     NDArray[floataa]:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase  	=      np.shape(snake_case			)

						__UpperCamelCase  	=      np.zeros((rows, 1)			,   dtype=snake_case			)
						for row in reversed(range(snake_case			)			):
												__UpperCamelCase  	=      0
												for col in range(row + 1			,   snake_case			):
																		total += coefficients[row, col] * x[col]

												__UpperCamelCase  	=      (vector[row] - total) / coefficients[row, row]

						return x




def 						A_			(				snake_case				: NDArray[floataa]			,   snake_case				: NDArray[floataa]			)   ->     NDArray[floataa]:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase  	=      np.shape(snake_case			)
						if rows != columns:
												return np.array(()			,   dtype=snake_case			)

						# augmented matrix
						__UpperCamelCase  	=      np.concatenate((coefficients, vector)			,   axis=1			)
						__UpperCamelCase  	=      augmented_mat.astype('''float64'''			)

						# scale the matrix leaving it triangular
						for row in range(rows - 1			):
												__UpperCamelCase  	=      augmented_mat[row, row]
												for col in range(row + 1			,   snake_case			):
																		__UpperCamelCase  	=      augmented_mat[col, row] / pivot
																		augmented_mat[col, :] -= factor * augmented_mat[row, :]

						__UpperCamelCase  	=      retroactive_resolution(
						    augmented_mat[:, 0:columns]			,   augmented_mat[:, columns : columns + 1]			)

						return x


if __name__ == "__main__":
				import doctest

				doctest.testmod()








import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





