import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	int:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

						_snake_case						:					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(__lowercase						):
												os.makedirs(__lowercase						)

						_snake_case						:					Union[str, Any]    =   model.state_dict()

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

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

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		_snake_case						:					List[str]    =   to_tf_var_name(__lowercase						)
																		_snake_case						:					Optional[Any]    =   state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose						):
																								_snake_case						:					List[Any]    =   torch_tensor.T
																		_snake_case						:					str    =   create_tf_var(tensor=__lowercase ,      name=__lowercase ,      session=__lowercase						)
																		tf.keras.backend.set_value(__lowercase ,      __lowercase						)
																		_snake_case						:					Dict    =   session.run(__lowercase						)
																		print(F"""Successfully created {tf_name}: {np.allclose(__lowercase ,      __lowercase						)}"""						)

												_snake_case						:					Tuple    =   tf.train.Saver(tf.trainable_variables()						)
												saver.save(__lowercase ,      os.path.join(__lowercase ,      model_name.replace("-" ,      "_"						) + ".ckpt"						)						)
def  snake_case						(__lowercase=None						)	->	Tuple:

						'''simple docstring'''



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

						_snake_case						:					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=__lowercase ,      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


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    '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

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)