
import hashlib
from typing import Union




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       bytes:


		'''simple docstring'''


		if isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
				# Data already in bytes, returns as it as
				return hash_data
		elif isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
				# We keep the data as it as for it ot be later encoded to UTF-8
				# However replace `\\` with `/` for Windows compatibility
				snake_case     :					Any     =	hash_data.replace('''\\'''   ,					'''/''' )
		elif isinstance(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ ):
				snake_case     :					Any     =	str(SCREAMING_SNAKE_CASE__ )
		else:
				# If data is not of the required type, raise error
				raise InvalidKeyError(SCREAMING_SNAKE_CASE__ )

		return hash_data.encode('''utf-8''' )






class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: Union[str, Any]			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					Dict     =	'''\nFAILURE TO GENERATE DATASET: Invalid key type detected'''
				snake_case     :					List[Any]     =	f'\nFound Key {hash_data} of type {type(UpperCamelCase__			)}'
				snake_case     :					Dict     =	'''\nKeys should be either str, int or bytes type'''
				super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)







class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		def __init__(			self							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: int=""			)	-> List[Any]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	key
				snake_case     :					Any     =	duplicate_key_indices
				snake_case     :					Union[str, Any]     =	fix_msg
				snake_case     :					Optional[int]     =	'''Found multiple examples generated with the same key'''
				if len(UpperCamelCase__			) <= 20:
						snake_case     :					str     =	f'\nThe examples at index {", ".join(UpperCamelCase__			)} have the key {key}'
				else:
						snake_case     :					List[Any]     =	f'\nThe examples at index {", ".join(duplicate_key_indices[:20]			)}... ({len(UpperCamelCase__			) - 20} more) have the key {key}'
				snake_case     :					Tuple     =	'''\n''' + fix_msg if fix_msg else ''''''
				super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)







class   snake_case__					:







		"""simple docstring"""

		def __init__(			self							: List[str]     ,  UpperCamelCase__							: str			)	-> Union[str, Any]:






				"""simple docstring"""







				snake_case     :					Dict     =	hashlib.mda(_as_bytes(UpperCamelCase__			)			)



		def 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: Union[str, int, bytes]			)	-> int:






				"""simple docstring"""







				snake_case     :					int     =	self._split_mda.copy()
				snake_case     :					List[str]     =	_as_bytes(UpperCamelCase__			)
				mda.update(UpperCamelCase__			)
				# Convert to integer with hexadecimal conversion
				return int(mda.hexdigest()     ,  16			)




'''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       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:


		'''simple docstring'''


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


