





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  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Any:







			'''simple docstring'''

			UpperCAmelCase       		=    json.load(open(UpperCamelCase__      ,      '''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(UpperCamelCase__			)

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

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


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










def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    float:







			'''simple docstring'''

			if mass < 0:
						raise ValueError('''The mass of the body cannot be negative'''			)
			if radius <= 0:
						raise ValueError('''The radius is always a positive non zero integer'''			)
			return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)



