
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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


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







from math import sqrt


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      [1]
   for i in range(2,  int(sqrt(__lowerCamelCase						)						) + 1,  1						):
      if number % i == 0:
         values.append(__lowerCamelCase						)
         if int(number // i						) != i:
            values.append(int(number // i						)						)
   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   return sum(factors(__lowerCamelCase						)						) > n


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      factors(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      [[0 for i in range(number + 1						)] for j in range(r + 1						)]
   for i in range(r + 1						):
      SCREAMING_SNAKE_CASE_	=      True

   for i in range(1,  number + 1						):
      SCREAMING_SNAKE_CASE_	=      False

   for i in range(1,  r + 1						):
      for j in range(1,  number + 1						):
         if j < values[i - 1]:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j]
         else:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0


def  A__ (       __lowerCamelCase						):
   return abundant(__lowerCamelCase						) and not semi_perfect(__lowerCamelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F"""{number} is {'' if weird(number) else 'not '}weird.""")


