


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   Optional[Any]:
      a__	:  Union[str, Any]     =   ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

      a__	:  Union[str, Any]     =   (
          ("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(__a      ):
            os.makedirs(__a      )

      a__	:  Any     =   model.state_dict()

      def to_tf_var_name(__a      ):
            for patt, repl in iter(__a      ):
                  a__	:  Tuple     =   name.replace(__a			,				__a      )
            return f'''bert/{name}'''

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

      tf.reset_default_graph()
      with tf.Session() as session:
            for var_name in state_dict:
                  a__	:  int     =   to_tf_var_name(__a      )
                  a__	:  Union[str, Any]     =   state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose      ):
                        a__	:  int     =   torch_tensor.T
                  a__	:  Optional[Any]     =   create_tf_var(tensor=__a			,				name=__a			,				session=__a      )
                  tf.keras.backend.set_value(__a			,				__a      )
                  a__	:  int     =   session.run(__a      )
                  print(f'''Successfully created {tf_name}: {np.allclose(__a			,				__a      )}'''      )

            a__	:  Any     =   tf.train.Saver(tf.trainable_variables()      )
            saver.save(__a			,				os.path.join(__a			,				model_name.replace("-"			,				"_"      ) + ".ckpt"      )      )



def UpperCamelCase_				(				__a=None      )	->   int:
      a__	:  Dict     =   argparse.ArgumentParser()
      parser.add_argument("--model_name"			,				type=__a			,				required=__a			,				help="model name e.g. bert-base-uncased"      )
      parser.add_argument(
          "--cache_dir"			,				type=__a			,				default=__a			,				required=__a			,				help="Directory containing pytorch model"      )
      parser.add_argument("--pytorch_model_path"			,				type=__a			,				required=__a			,				help="/path/to/<pytorch-model-name>.bin"      )
      parser.add_argument("--tf_cache_dir"			,				type=__a			,				required=__a			,				help="Directory in which to save tensorflow model"      )
      a__	:  Optional[Any]     =   parser.parse_args(__a      )

      a__	:  Tuple     =   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=__a			,				ckpt_dir=args.tf_cache_dir			,				model_name=args.model_name      )


if __name__ == "__main__":
    main()









'''simple docstring'''

def 				snake_case       (  a_				:			str     , a_				:			Optional[int]						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				(boundary[1] - boundary[0]) / steps
      UpperCamelCase_					: Dict            =				boundary[0]
      UpperCamelCase_					: Any            =				boundary[1]
      UpperCamelCase_					: Union[str, Any]            =				make_points(a_     , a_     , a_						)
      UpperCamelCase_					: Any            =				0.0
      y += (h / 2.0) * f(a_						)
      for i in x_i:
            # print(i)
            y += h * f(a_						)
      y += (h / 2.0) * f(a_						)
      return y



def 				snake_case       (  a_				:			Tuple     , a_				:			Any     , a_				:			Tuple						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				a + h
      while x < (b - h):
            yield x
            UpperCamelCase_					: List[str]            =				x + h



def 				snake_case       (  a_				:			List[str]						)							->					Tuple:  # enter your function here





      """simple docstring"""

      UpperCamelCase_					: int            =				(x - 0) * (x - 0)
      return y



def 				snake_case       (  )							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				0.0  # Lower bound of integration
      UpperCamelCase_					: Optional[int]            =				1.0  # Upper bound of integration
      UpperCamelCase_					: Optional[Any]            =				10.0  # define number of steps or resolution
      UpperCamelCase_					: Optional[Any]            =				[a, b]  # define boundary of integration
      UpperCamelCase_					: Any            =				method_a(a_     , a_						)
      print(f"y = {y}"						)


if __name__ == "__main__":
      main()






