





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    lowerCamelCase__					:   Union[str, Any]      				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

    lowerCamelCase__					:   Optional[int]      				=	(
        ('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(_UpperCAmelCase				):
        os.makedirs(_UpperCAmelCase				)

    lowerCamelCase__					:   Optional[Any]      				=	model.state_dict()

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

    def create_tf_var(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				):
        lowerCamelCase__					:   Optional[int]      				=	tf.dtypes.as_dtype(tensor.dtype				)
        lowerCamelCase__					:   List[Any]      				=	tf.get_variable(dtype=_UpperCAmelCase		,			shape=tensor.shape		,			name=_UpperCAmelCase		,			initializer=tf.zeros_initializer()				)
        session.run(tf.variables_initializer([tf_var]				)				)
        session.run(_UpperCAmelCase				)
        return tf_var

    tf.reset_default_graph()
    with tf.Session() as session:
        for var_name in state_dict:
            lowerCamelCase__					:   Tuple      				=	to_tf_var_name(_UpperCAmelCase				)
            lowerCamelCase__					:   Any      				=	state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose				):
                lowerCamelCase__					:   str      				=	torch_tensor.T
            lowerCamelCase__					:   Optional[Any]      				=	create_tf_var(tensor=_UpperCAmelCase		,			name=_UpperCAmelCase		,			session=_UpperCAmelCase				)
            tf.keras.backend.set_value(_UpperCAmelCase		,			_UpperCAmelCase				)
            lowerCamelCase__					:   Dict      				=	session.run(_UpperCAmelCase				)
            print(F"""Successfully created {tf_name}: {np.allclose(_UpperCAmelCase		,			_UpperCAmelCase				)}"""				)

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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase=None				)    ->  Optional[int]:
    lowerCamelCase__					:   Optional[int]      				=	argparse.ArgumentParser()
    parser.add_argument('--model_name'		,			type=_UpperCAmelCase		,			required=_UpperCAmelCase		,			help='model name e.g. bert-base-uncased'				)
    parser.add_argument(
        '--cache_dir'		,			type=_UpperCAmelCase		,			default=_UpperCAmelCase		,			required=_UpperCAmelCase		,			help='Directory containing pytorch model'				)
    parser.add_argument('--pytorch_model_path'		,			type=_UpperCAmelCase		,			required=_UpperCAmelCase		,			help='/path/to/<pytorch-model-name>.bin'				)
    parser.add_argument('--tf_cache_dir'		,			type=_UpperCAmelCase		,			required=_UpperCAmelCase		,			help='Directory in which to save tensorflow model'				)
    lowerCamelCase__					:   List[str]      				=	parser.parse_args(_UpperCAmelCase				)

    lowerCamelCase__					:   Optional[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=_UpperCAmelCase		,			ckpt_dir=args.tf_cache_dir		,			model_name=args.model_name				)


if __name__ == "__main__":
    main()












_UpperCAmelCase       : str			     =       """Tobias Carryer"""

from time import time

class      lowerCAmelCase							:
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   str=int(time()			)			)    ->			Optional[int]:  # noqa: B008
     lowerCamelCase__					:   List[Any]      				=	multiplier
     lowerCamelCase__					:   Tuple      				=	increment
     lowerCamelCase__					:   int      				=	modulo
     lowerCamelCase__					:   Any      				=	seed







 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	(self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
    # Show the LCG in action.
    _UpperCAmelCase       : List[Any]			     =       LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
    while True:
        print(lcg.next_number())





