






"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def 					_A			(__a    ,				__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(
        ('''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(__a							):
        os.makedirs(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		model.state_dict()

    def to_tf_var_name(__a							):
        for patt, repl in iter(__a							):
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		name.replace(__a    ,				__a							)
        return f'bert/{name}'

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

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

        SCREAMING_SNAKE_CASE_      :  str    						=		tf.train.Saver(tf.trainable_variables()							)
        saver.save(__a    ,				os.path.join(__a    ,				model_name.replace('''-'''    ,				'''_'''							) + '''.ckpt'''							)							)



def 					_A			(__a=None							) ->       Optional[Any]:
    """simple docstring"""






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

    SCREAMING_SNAKE_CASE_      :  Dict    						=		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=__a    ,				ckpt_dir=args.tf_cache_dir    ,				model_name=args.model_name							)


if __name__ == "__main__":
  main()










"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCAmelCase_   : Optional[Any]   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  List[str]						,	*lowercase_     :  Dict						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          warnings.warn(
              '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
              ''' use DeiTImageProcessor instead.'''						,	lowercase_						,	)
          super().__init__(*lowercase_						,	**lowercase_)


