




"""simple docstring"""





def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       List[str]				      =       int((lower_bound + upper_bound) // 2		)
						if array[r] == value:
												return r
						if lower_bound >= upper_bound:
												return -1
						if array[r] < value:
												return binary_search(SCREAMING_SNAKE_CASE__   ,      r + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__		)
						else:
												return binary_search(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      r - 1   ,      SCREAMING_SNAKE_CASE__		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Tuple				      =       0
						if matrix[index][0] == value:
												return [index, 0]
						while index < len(SCREAMING_SNAKE_CASE__		) and matrix[index][0] < value:
												snake_case__							:       str				      =       binary_search(matrix[index]   ,      0   ,      len(matrix[index]		) - 1   ,      SCREAMING_SNAKE_CASE__		)
												if r != -1:
																		return [index, r]
												index += 1
						return [-1, -1]


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)





