
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       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				int   ,							SCREAMING_SNAKE_CASE	:				Optional[Any]      )					->						Optional[int]:
				# Initialise PyTorch model
				__lowercase								= json.load(open(SCREAMING_SNAKE_CASE   ,							'r'      )      )
				__lowercase								= 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}"""      )
				__lowercase								= 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__":
							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)








from math import pi




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float      )					->						float:
				return degree / (180 / pi)


if __name__ == "__main__":
							from doctest import testmod

							testmod()





