'''simple docstring'''






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def    __UpperCAmelCase			(  a_:       BertModel,					a_:       str,					a_:       str							):
   _UpperCAmelCase							:	Optional[Any]							=   ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

   _UpperCAmelCase							:	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(lowerCAmelCase_							):
      os.makedirs(lowerCAmelCase_							)

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

   def to_tf_var_name(a_:       str							):
      for patt, repl in iter(lowerCAmelCase_							):
         _UpperCAmelCase							:	Dict							=   name.replace(lowerCAmelCase_,					lowerCAmelCase_							)
      return f"""bert/{name}"""

   def create_tf_var(a_:       np.ndarray,					a_:       str,					a_:       tf.Session							):
      _UpperCAmelCase							:	int							=   tf.dtypes.as_dtype(tensor.dtype							)
      _UpperCAmelCase							:	Optional[Any]							=   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							:	str							=   to_tf_var_name(lowerCAmelCase_							)
         _UpperCAmelCase							:	Optional[Any]							=   state_dict[var_name].numpy()
         if any(x in var_name for x in tensors_to_transpose							):
            _UpperCAmelCase							:	Any							=   torch_tensor.T
         _UpperCAmelCase							:	Tuple							=   create_tf_var(tensor=lowerCAmelCase_,					name=lowerCAmelCase_,					session=lowerCAmelCase_							)
         tf.keras.backend.set_value(lowerCAmelCase_,					lowerCAmelCase_							)
         _UpperCAmelCase							:	Optional[Any]							=   session.run(lowerCAmelCase_							)
         print(f"""Successfully created {tf_name}: {np.allclose(lowerCAmelCase_,					lowerCAmelCase_							)}"""							)

      _UpperCAmelCase							:	str							=   tf.train.Saver(tf.trainable_variables()							)
      saver.save(lowerCAmelCase_,					os.path.join(lowerCAmelCase_,					model_name.replace("-",					"_"							) + ".ckpt"							)							)






def    __UpperCAmelCase			(  a_:       str=None							):
   _UpperCAmelCase							:	Union[str, Any]							=   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							:	Any							=   parser.parse_args(lowerCAmelCase_							)

   _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=lowerCAmelCase_,					ckpt_dir=args.tf_cache_dir,					model_name=args.model_name							)


if __name__ == "__main__":
     main()






# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    Dict						    =  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MraForMaskedLM',
         'MraForMultipleChoice',
         'MraForQuestionAnswering',
         'MraForSequenceClassification',
         'MraForTokenClassification',
         'MraLayer',
         'MraModel',
         'MraPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mra import (
              MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
              MraLayer,
              MraModel,
              MraPreTrainedModel,
          )
else:
     import sys

     _snake_case  :    Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure)

