


'''simple docstring'''

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def lowerCamelCase      (      lowerCAmelCase    :     BertModel		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

	__magic_name__	:   List[str]					     =					(
	    ('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(lowerCAmelCase		):
		os.makedirs(lowerCAmelCase		)

	__magic_name__	:   Tuple					     =					model.state_dict()

	def to_tf_var_name(lowerCAmelCase    :     str		):
		for patt, repl in iter(lowerCAmelCase		):
			__magic_name__	:   Any					     =					name.replace(lowerCAmelCase		,		lowerCAmelCase		)
		return f'bert/{name}'

	def create_tf_var(lowerCAmelCase    :     np.ndarray		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     tf.Session		):
		__magic_name__	:   List[Any]					     =					tf.dtypes.as_dtype(tensor.dtype		)
		__magic_name__	:   List[Any]					     =					tf.get_variable(dtype=lowerCAmelCase		,		shape=tensor.shape		,		name=lowerCAmelCase		,		initializer=tf.zeros_initializer()		)
		session.run(tf.variables_initializer([tf_var]		)		)
		session.run(lowerCAmelCase		)
		return tf_var

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

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





def lowerCamelCase      (      lowerCAmelCase    :     Any=None		):





	"""simple docstring"""







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

	__magic_name__	:   Any					     =					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=lowerCAmelCase		,		ckpt_dir=args.tf_cache_dir		,		model_name=args.model_name		)


if __name__ == "__main__":
				main()



'''simple docstring'''

import math





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     float		):





	"""simple docstring"""







	return math.pow(lowerCAmelCase		,		2		) - a





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	return 2 * x





def lowerCamelCase      (      lowerCAmelCase    :     float		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					2.0

	while start <= a:
		__magic_name__	:   List[str]					     =					math.pow(lowerCAmelCase		,		2		)

	return start





def lowerCamelCase      (      lowerCAmelCase    :     float		,		lowerCAmelCase    :     int = 9999		,		lowerCAmelCase    :     float = 0.00_0000_0000_0001		):





	"""simple docstring"""







	if a < 0:
		raise ValueError('math domain error'		)

	__magic_name__	:   Any					     =					get_initial_point(lowerCAmelCase		)

	for _ in range(lowerCAmelCase		):
		__magic_name__	:   List[str]					     =					value
		__magic_name__	:   Optional[int]					     =					value - fx(lowerCAmelCase		,		lowerCAmelCase		) / fx_derivative(lowerCAmelCase		)
		if abs(prev_value - value		) < tolerance:
			return value

	return value


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

							testmod()