

import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	BertModel    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str      )							->      List[Any]:





    '''simple docstring'''






    A__          =   ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

    A__          =   (
        ('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(SCREAMING_SNAKE_CASE__      ):
        os.makedirs(SCREAMING_SNAKE_CASE__      )

    A__          =   model.state_dict()

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

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

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

        A__          =   tf.train.Saver(tf.trainable_variables()      )
        saver.save(SCREAMING_SNAKE_CASE__    ,			os.path.join(SCREAMING_SNAKE_CASE__    ,			model_name.replace('-'    ,			'_'      ) + '.ckpt'      )      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Any=None      )							->      Any:





    '''simple docstring'''






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

    A__          =   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=SCREAMING_SNAKE_CASE__    ,			ckpt_dir=args.tf_cache_dir    ,			model_name=args.model_name      )


if __name__ == "__main__":
     main()



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''

					if edge <= 0 or not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Length must be a positive.'''		)
					return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




