

'''simple docstring'''





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def      lowerCAmelCase_    ( snake_case_	:			BertModel ,   snake_case_	:			str ,   snake_case_	:			str      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     UpperCAmelCase_	   =       (
         ("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_      )

     UpperCAmelCase_	   =       model.state_dict()

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

     def create_tf_var(snake_case_	:			np.ndarray ,   snake_case_	:			str ,   snake_case_	:			tf.Session      ):
          UpperCAmelCase_	   =       tf.dtypes.as_dtype(tensor.dtype      )
          UpperCAmelCase_	   =       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:
               UpperCAmelCase_	   =       to_tf_var_name(snake_case_      )
               UpperCAmelCase_	   =       state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    UpperCAmelCase_	   =       torch_tensor.T
               UpperCAmelCase_	   =       create_tf_var(tensor=snake_case_ ,   name=snake_case_ ,   session=snake_case_      )
               tf.keras.backend.set_value(snake_case_ ,   snake_case_      )
               UpperCAmelCase_	   =       session.run(snake_case_      )
               print(f"""Successfully created {tf_name}: {np.allclose(snake_case_ ,   snake_case_      )}"""      )

          UpperCAmelCase_	   =       tf.train.Saver(tf.trainable_variables()      )
          saver.save(snake_case_ ,   os.path.join(snake_case_ ,   model_name.replace("-" ,   "_"      ) + ".ckpt"      )      )





def      lowerCAmelCase_    ( snake_case_	:			Tuple=None      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       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"      )
     UpperCAmelCase_	   =       parser.parse_args(snake_case_      )

     UpperCAmelCase_	   =       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      lowerCAmelCase_    ( snake_case_	:			int = 1_00_00_00      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       limit + 1
     UpperCAmelCase_	   =       [0] * limit
     for first_term in range(1 ,   snake_case_      ):
          for n in range(snake_case_ ,   snake_case_ ,   snake_case_      ):
               UpperCAmelCase_	   =       first_term + n / first_term
               if common_difference % 4:  # d must be divisble by 4
                    continue
               else:
                    common_difference /= 4
                    if (
                        first_term > common_difference
                        and first_term < 4 * common_difference
                    ):  # since x,y,z are positive integers
                         frequency[n] += 1  # so z>0 and a>d ,also 4d<a

     UpperCAmelCase_	   =       sum(1 for x in frequency[1:limit] if x == 10      )

     return count


if __name__ == "__main__":
    print(f"{solution() = }")




