




'''simple docstring'''

from __future__ import annotations




def    snake_case__     (  a	)			->       int | float:





			'''simple docstring'''






			snake_case__          =						sorted(a	)
			snake_case__          =						len(a	)
			snake_case__          =						length >> 1
			return (
			    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
			    if length % 2 == 0
			    else sorted_list[mid_index]
			)




def    snake_case__     (  )			->       Optional[Any]:





			'''simple docstring'''






			import doctest

			doctest.testmod()


if __name__ == "__main__":
		main()





"""simple docstring"""



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     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 # Initialise PyTorch model
 A__			=							json.load(open(lowercase_     ,					"r"		)		)
 A__			=							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}"""		)
 A__			=							GPTNeoForCausalLM(lowercase_		)

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

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=			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."
      )
      SCREAMING_SNAKE_CASE							=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





