
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_:  Any				,	lowerCamelCase_:  Any				,	lowerCamelCase_:  List[Any]		):



				'''simple docstring'''







				A     :  Union[str, Any]   =				json.load(open(lowerCamelCase_				,	'''r'''		)		)
				A     :  Optional[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}"""		)
				A     :  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_  =	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_  =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

UpperCamelCase_  =	10_00





def 							_lowerCamelCase			(					lowerCamelCase_:  float		):



				'''simple docstring'''







				return x * x





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple				,	lowerCamelCase_:  float				,	lowerCamelCase_:  float				,	lowerCamelCase_:  int = 4		):



				'''simple docstring'''







				assert callable(
				    lowerCamelCase_		), f"""the function(object) passed should be callable your input : {function}"""
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""a should be float or integer your input : {a}"""
				assert isinstance(function(lowerCamelCase_		)				,	(float, int)		), (
				    "the function should return integer or float return type of your function, "
				    f"""{type(lowerCamelCase_		)}"""
				)
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""b should be float or integer your input : {b}"""
				assert (
				    isinstance(lowerCamelCase_				,	lowerCamelCase_		) and precision > 0
				), f"""precision should be positive integer your input : {precision}"""

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				A     :  Tuple   =				(b - a) / N_STEPS
				A     :  List[str]   =				function(lowerCamelCase_		) + function(lowerCamelCase_		)

				for i in range(1				,	lowerCamelCase_		):
								A     :  int   =				a + h * i
								result += function(lowerCamelCase_		) * (4 if i % 2 else 2)

				result *= h / 3
				return round(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()