



"""simple docstring"""
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Dict							):
    _UpperCAmelCase							:  Any											=    ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

    _UpperCAmelCase							:  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_							)

    _UpperCAmelCase							:  Optional[int]											=    model.state_dict()

    def to_tf_var_name(UpperCamelCase__		:		List[str]							):
        for patt, repl in iter(lowerCamelCase_							):
            _UpperCAmelCase							:  List[Any]											=    name.replace(lowerCamelCase_     ,       lowerCamelCase_							)
        return F'bert/{name}'

    def create_tf_var(UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Dict							):
        _UpperCAmelCase							:  List[Any]											=    tf.dtypes.as_dtype(tensor.dtype							)
        _UpperCAmelCase							:  Optional[int]											=    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:
            _UpperCAmelCase							:  Optional[Any]											=    to_tf_var_name(lowerCamelCase_							)
            _UpperCAmelCase							:  Any											=    state_dict[var_name].numpy()
            if any(x in var_name for x in tensors_to_transpose							):
                _UpperCAmelCase							:  str											=    torch_tensor.T
            _UpperCAmelCase							:  Tuple											=    create_tf_var(tensor=lowerCamelCase_     ,       name=lowerCamelCase_     ,       session=lowerCamelCase_							)
            tf.keras.backend.set_value(lowerCamelCase_     ,       lowerCamelCase_							)
            _UpperCAmelCase							:  List[str]											=    session.run(lowerCamelCase_							)
            print(F'Successfully created {tf_name}: {np.allclose(lowerCamelCase_     ,       lowerCamelCase_							)}'							)

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





def  lowerCamelCase_						(UpperCamelCase__		:		Tuple=None							):
    _UpperCAmelCase							:  Optional[int]											=    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'''							)
    _UpperCAmelCase							:  Tuple											=    parser.parse_args(lowerCamelCase_							)

    _UpperCAmelCase							:  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=lowerCamelCase_     ,       ckpt_dir=args.tf_cache_dir     ,       model_name=args.model_name							)


if __name__ == "__main__":
  main()










import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




