


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       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)->			Dict:
				# Initialise PyTorch model
				lowerCAmelCase_			:   int     					=					json.load(open(lowerCAmelCase_			, '''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_			:   Any     					=					GPTNeoForCausalLM(lowerCAmelCase_			)

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

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


if __name__ == "__main__":
						_UpperCAmelCase      :	Optional[int]  =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."""
						)
						_UpperCAmelCase      :	Tuple  =parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)



import numpy as np

_UpperCAmelCase      :	int  =[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]







class 					snake_case__:







						'''simple docstring'''




						def __init__(					self				)       ->     None:
										lowerCAmelCase_			:   List[Any]     					=					np.array(__lowercase				)




						def     lowercase_			(					self ,    __lowercase				)       ->     np.ndarray:
										lowerCAmelCase_					,	lowerCAmelCase_			:   List[str]     					=					np.where(letter == self.SQUARE				)
										lowerCAmelCase_			:   Union[str, Any]     					=					np.concatenate([indexa + 1, indexa + 1]				)
										return indexes




						def     lowercase_			(					self ,    __lowercase ,    __lowercase				)       ->     str:
										return self.SQUARE[indexa - 1, indexa - 1]




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   Dict     					=					message.lower()
										lowerCAmelCase_			:   Any     					=					message.replace('''j''' ,    '''i'''				)

										lowerCAmelCase_			:   Optional[Any]     					=					''''''
										for letter_index in range(len(__lowercase				)				):
														if message[letter_index] != " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					self.letter_to_numbers(message[letter_index]				)
																		lowerCAmelCase_			:   List[Any]     					=					encoded_message + str(numbers[0]				) + str(numbers[1]				)
														elif message[letter_index] == " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					encoded_message + ''' '''

										return encoded_message




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   List[str]     					=					message.replace(''' ''' ,    '''  '''				)
										lowerCAmelCase_			:   Union[str, Any]     					=					''''''
										for numbers_index in range(int(len(__lowercase				) / 2				)				):
														if message[numbers_index * 2] != " ":
																		lowerCAmelCase_			:   Any     					=					message[numbers_index * 2]
																		lowerCAmelCase_			:   Dict     					=					message[numbers_index * 2 + 1]

																		lowerCAmelCase_			:   Dict     					=					self.numbers_to_letter(int(__lowercase				) ,    int(__lowercase				)				)
																		lowerCAmelCase_			:   Tuple     					=					decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		lowerCAmelCase_			:   List[str]     					=					decoded_message + ''' '''

										return decoded_message