"""simple docstring"""



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

     __snake_case				=							(
         ('''layer.''', '''layer_'''),
         ('''word_embeddings.weight''', '''word_embeddings'''),
         ('''position_embeddings.weight''', '''position_embeddings'''),
         ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
         ('''.''', '''/'''),
         ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
         ('''LayerNorm/bias''', '''LayerNorm/beta'''),
         ('''weight''', '''kernel'''),
     )

     if not os.path.isdir(snake_case):
          os.makedirs(snake_case)

     __snake_case				=							model.state_dict()

     def to_tf_var_name(snake_case):
          for patt, repl in iter(snake_case):
               __snake_case				=							name.replace(snake_case,      snake_case)
          return f"bert/{name}"

     def create_tf_var(snake_case,      snake_case,      snake_case):
          __snake_case				=							tf.dtypes.as_dtype(tensor.dtype)
          __snake_case				=							tf.get_variable(dtype=snake_case,      shape=tensor.shape,      name=snake_case,      initializer=tf.zeros_initializer())
          session.run(tf.variables_initializer([tf_var]))
          session.run(snake_case)
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               __snake_case				=							to_tf_var_name(snake_case)
               __snake_case				=							state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose):
                    __snake_case				=							torch_tensor.T
               __snake_case				=							create_tf_var(tensor=snake_case,      name=snake_case,      session=snake_case)
               tf.keras.backend.set_value(snake_case,      snake_case)
               __snake_case				=							session.run(snake_case)
               print(f"Successfully created {tf_name}: {np.allclose(snake_case,      snake_case)}")

          __snake_case				=							tf.train.Saver(tf.trainable_variables())
          saver.save(snake_case,      os.path.join(snake_case,      model_name.replace('''-''',      '''_''') + '''.ckpt'''))





def    SCREAMING_SNAKE_CASE       (     snake_case=None):
     __snake_case				=							argparse.ArgumentParser()
     parser.add_argument('''--model_name''',      type=snake_case,      required=snake_case,      help='''model name e.g. bert-base-uncased''')
     parser.add_argument(
         '''--cache_dir''',      type=snake_case,      default=snake_case,      required=snake_case,      help='''Directory containing pytorch model''')
     parser.add_argument('''--pytorch_model_path''',      type=snake_case,      required=snake_case,      help='''/path/to/<pytorch-model-name>.bin''')
     parser.add_argument('''--tf_cache_dir''',      type=snake_case,      required=snake_case,      help='''Directory in which to save tensorflow model''')
     __snake_case				=							parser.parse_args(snake_case)

     __snake_case				=							BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name,      state_dict=torch.load(args.pytorch_model_path),      cache_dir=args.cache_dir,      )

     convert_pytorch_checkpoint_to_tf(model=snake_case,      ckpt_dir=args.tf_cache_dir,      model_name=args.model_name)


if __name__ == "__main__":
       main()
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     return 1 if digit in (0, 1) else (digit * factorial(digit - 1))





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							0
     __snake_case				=							number
     while duplicate > 0:
          __snake_case		,	__snake_case				=							divmod(snake_case,      10)
          fact_sum += factorial(snake_case)
     return fact_sum == number


if __name__ == "__main__":
       print("Program to check whether a number is a Krisnamurthy Number or not.")
       __lowercase		:		Optional[int]														=							int(input("Enter number: ").strip())
       print(
           F"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
       )