





import math

from numpy import inf
from scipy.integrate import quad





def 					lowerCAmelCase_			(	__a		)					->		float:







  """simple docstring"""





  if num <= 0:
    raise ValueError("math domain error"		)

  return quad(__a     ,   0     ,   __a     ,   args=(__a)		)[0]





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		float:







  """simple docstring"""





  return math.pow(__a     ,   z - 1		) * math.exp(-x		)


if __name__ == "__main__":
     from doctest import testmod

     testmod()








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				int        =("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

  lowerCamelCase__:				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		)

  lowerCamelCase__:				Tuple        =model.state_dict()

  def to_tf_var_name(__a		):
    for patt, repl in iter(__a		):
      lowerCamelCase__:				Tuple        =name.replace(__a     ,   __a		)
    return F"""bert/{name}"""

  def create_tf_var(__a     ,   __a     ,   __a		):
    lowerCamelCase__:				Union[str, Any]        =tf.dtypes.as_dtype(tensor.dtype		)
    lowerCamelCase__:				Any        =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:
      lowerCamelCase__:				Tuple        =to_tf_var_name(__a		)
      lowerCamelCase__:				str        =state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose		):
        lowerCamelCase__:				List[Any]        =torch_tensor.T
      lowerCamelCase__:				Optional[int]        =create_tf_var(tensor=__a     ,   name=__a     ,   session=__a		)
      tf.keras.backend.set_value(__a     ,   __a		)
      lowerCamelCase__:				Union[str, Any]        =session.run(__a		)
      print(F"""Successfully created {tf_name}: {np.allclose(__a     ,   __a		)}"""		)

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





def 					lowerCAmelCase_			(	__a=None		)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =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"		)
  lowerCamelCase__:				Optional[Any]        =parser.parse_args(__a		)

  lowerCamelCase__:				Dict        =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()

