
"""simple docstring"""



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Union[str, Any]     							=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

    UpperCAmelCase__	:      Dict     							=					(
        ('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(_snake_case ):
        os.makedirs(_snake_case )

    UpperCAmelCase__	:      Tuple     							=					model.state_dict()

    def to_tf_var_name(_snake_case ):
        for patt, repl in iter(_snake_case ):
            UpperCAmelCase__	:      int     							=					name.replace(_snake_case    ,_snake_case )
        return F'''bert/{name}'''

    def create_tf_var(_snake_case    ,_snake_case    ,_snake_case ):
        UpperCAmelCase__	:      Any     							=					tf.dtypes.as_dtype(tensor.dtype )
        UpperCAmelCase__	:      Union[str, Any]     							=					tf.get_variable(dtype=_snake_case    ,shape=tensor.shape    ,name=_snake_case    ,initializer=tf.zeros_initializer() )
        session.run(tf.variables_initializer([tf_var] ) )
        session.run(_snake_case )
        return tf_var

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

        UpperCAmelCase__	:      List[str]     							=					tf.train.Saver(tf.trainable_variables() )
        saver.save(_snake_case    ,os.path.join(_snake_case    ,model_name.replace('-'    ,'_' ) + '.ckpt' ) )
def 					lowerCamelCase   (     _snake_case=None ):
    UpperCAmelCase__	:      Any     							=					argparse.ArgumentParser()
    parser.add_argument('--model_name'    ,type=_snake_case    ,required=_snake_case    ,help='model name e.g. bert-base-uncased' )
    parser.add_argument(
        '--cache_dir'    ,type=_snake_case    ,default=_snake_case    ,required=_snake_case    ,help='Directory containing pytorch model' )
    parser.add_argument('--pytorch_model_path'    ,type=_snake_case    ,required=_snake_case    ,help='/path/to/<pytorch-model-name>.bin' )
    parser.add_argument('--tf_cache_dir'    ,type=_snake_case    ,required=_snake_case    ,help='Directory in which to save tensorflow model' )
    UpperCAmelCase__	:      Optional[Any]     							=					parser.parse_args(_snake_case )

    UpperCAmelCase__	:      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=_snake_case    ,ckpt_dir=args.tf_cache_dir    ,model_name=args.model_name )


if __name__ == "__main__":
    main()



"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    return int((input_a, input_a).count(0 ) == 0 )
def 					lowerCamelCase   (     ):
    assert and_gate(0    ,0 ) == 0
    assert and_gate(0    ,1 ) == 0
    assert and_gate(1    ,0 ) == 0
    assert and_gate(1    ,1 ) == 1


if __name__ == "__main__":
    test_and_gate()
    print(and_gate(1, 0))
    print(and_gate(0, 0))
    print(and_gate(0, 1))
    print(and_gate(1, 1))

