




'''simple docstring'''




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel




def 						UpperCamelCase	(			a		,						a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    ("""dense.weight""", """attention.self.query""", """attention.self.key""", """attention.self.value""")

     __magic_name__						       =    (
         ("""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(lowercase__			):
          os.makedirs(lowercase__			)

     __magic_name__						       =    model.state_dict()

     def to_tf_var_name(a			):
          for patt, repl in iter(lowercase__			):
               __magic_name__						       =    name.replace(lowercase__		,						lowercase__			)
          return F'''bert/{name}'''

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

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               __magic_name__						       =    to_tf_var_name(lowercase__			)
               __magic_name__						       =    state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose			):
                    __magic_name__						       =    torch_tensor.T
               __magic_name__						       =    create_tf_var(tensor=lowercase__		,						name=lowercase__		,						session=lowercase__			)
               tf.keras.backend.set_value(lowercase__		,						lowercase__			)
               __magic_name__						       =    session.run(lowercase__			)
               print(F'''Successfully created {tf_name}: {np.allclose(lowercase__		,						lowercase__			)}'''			)

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




def 						UpperCamelCase	(			a=None			)				->     Tuple:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--model_name'''		,						type=lowercase__		,						required=lowercase__		,						help='''model name e.g. bert-base-uncased'''			)
     parser.add_argument(
         '''--cache_dir'''		,						type=lowercase__		,						default=lowercase__		,						required=lowercase__		,						help='''Directory containing pytorch model'''			)
     parser.add_argument('''--pytorch_model_path'''		,						type=lowercase__		,						required=lowercase__		,						help='''/path/to/<pytorch-model-name>.bin'''			)
     parser.add_argument('''--tf_cache_dir'''		,						type=lowercase__		,						required=lowercase__		,						help='''Directory in which to save tensorflow model'''			)
     __magic_name__						       =    parser.parse_args(lowercase__			)

     __magic_name__						       =    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=lowercase__		,						ckpt_dir=args.tf_cache_dir		,						model_name=args.model_name			)


if __name__ == "__main__":
      main()









'''simple docstring'''




from __future__ import annotations

from fractions import Fraction




def 						UpperCamelCase	(			a		,						a			)				->     bool:






     '''simple docstring'''
     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def 						UpperCamelCase	(			a			)				->     list[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    11
     __magic_name__						       =    int('''1''' + '''0''' * digit_len			)
     for num in range(a		,						a			):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(a		,						a			):
                         solutions.append(F'''{num}/{den}'''			)
               den += 1
          num += 1
          __magic_name__						       =    10
     return solutions




def 						UpperCamelCase	(			a = 2			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    1.0
     for fraction in fraction_list(a			):
          __magic_name__						       =    Fraction(a			)
          result *= frac.denominator / frac.numerator
     return int(a			)


if __name__ == "__main__":
      print(solution())






