





"""simple docstring"""






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[int]:






       """simple docstring"""




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

       _UpperCAmelCase     =     (
           ('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							)

       _UpperCAmelCase     =     model.state_dict()

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

       def create_tf_var(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							):
              _UpperCAmelCase     =     tf.dtypes.as_dtype(tensor.dtype							)
              _UpperCAmelCase     =     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:
                     _UpperCAmelCase     =     to_tf_var_name(SCREAMING_SNAKE_CASE							)
                     _UpperCAmelCase     =     state_dict[var_name].numpy()
                     if any(x in var_name for x in tensors_to_transpose							):
                            _UpperCAmelCase     =     torch_tensor.T
                     _UpperCAmelCase     =     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							)
                     _UpperCAmelCase     =     session.run(SCREAMING_SNAKE_CASE							)
                     print(F"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)}"""							)

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE=None							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     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'							)
       _UpperCAmelCase     =     parser.parse_args(SCREAMING_SNAKE_CASE							)

       _UpperCAmelCase     =     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()













"""simple docstring"""






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_			=							{'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_			=							['''TimmBackbone''']


if TYPE_CHECKING:
    from .configuration_timm_backbone import TimmBackboneConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timm_backbone import TimmBackbone

else:
    import sys

    lowerCAmelCase_			=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






