
'''simple docstring'''
import math
from timeit import timeit







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							Tuple:





  """simple docstring"""

  __A		:  Tuple       				=		0
  __A		:  str       				=		abs(SCREAMING_SNAKE_CASE__			)
  while True:
    __A		:  int       				=		n // 10
    digits += 1
    if n == 0:
      break
  return digits







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]			)					->							int:





  """simple docstring"""

  return 1 if n == 0 else math.floor(math.log(abs(SCREAMING_SNAKE_CASE__			)      ,  10			) + 1			)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Optional[Any]			)					->							List[Any]:





  """simple docstring"""

  return len(str(abs(SCREAMING_SNAKE_CASE__			)			)			)







def 							A_  (    )					->							int:





  """simple docstring"""

  from collections.abc import Callable

  def benchmark_a_function(__SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	int			) -> None:
    __A		:  Dict       				=		F"{func.__name__}({value})"
    __A		:  int       				=		timeit(F"__main__.{call}"      ,  setup="""import __main__"""			)
    print(F"{call}: {func(SCREAMING_SNAKE_CASE__			)} -- {timing} seconds"			)

  for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__			)
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()








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)

