



from __future__ import annotations







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						assert len(SCREAMING_SNAKE_CASE__  ) == len(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   List[str]					     =					len(SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   Union[str, Any]					     =					list(range(SCREAMING_SNAKE_CASE__  )  )
						lowerCAmelCase	:   int					     =					[-1] * n  # who the donor has donated to
						lowerCAmelCase	:   List[str]					     =					[-1] * n  # who the recipient has received from
						lowerCAmelCase	:   Tuple					     =					[0] * n

						while unmatched_donors:
												lowerCAmelCase	:   Any					     =					unmatched_donors[0]
												lowerCAmelCase	:   int					     =					donor_pref[donor]
												lowerCAmelCase	:   List[Any]					     =					donor_preference[num_donations[donor]]
												num_donations[donor] += 1
												lowerCAmelCase	:   Union[str, Any]					     =					recipient_pref[recipient]
												lowerCAmelCase	:   str					     =					rec_record[recipient]

												if prev_donor != -1:
																		if rec_preference.index(SCREAMING_SNAKE_CASE__  ) > rec_preference.index(SCREAMING_SNAKE_CASE__  ):
																								lowerCAmelCase	:   Any					     =					donor
																								lowerCAmelCase	:   Dict					     =					recipient
																								unmatched_donors.append(SCREAMING_SNAKE_CASE__  )
																								unmatched_donors.remove(SCREAMING_SNAKE_CASE__  )
												else:
																		lowerCAmelCase	:   Any					     =					donor
																		lowerCAmelCase	:   List[Any]					     =					recipient
																		unmatched_donors.remove(SCREAMING_SNAKE_CASE__  )
						return donor_record






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__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					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}"""  )
						lowerCAmelCase	:   str					     =					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__":
		lowerCAmelCase					:				Optional[Any]   	=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.'
		)
		lowerCAmelCase					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

