






'''simple docstring'''

import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 				snake_case       (  a_				:			List[Any]     , a_				:			Dict     , a_				:			Optional[Any]						)							->					int:





      """simple docstring"""

      if gpta_config_file == "":
            UpperCamelCase_					: Union[str, Any]            =				GPTaConfig()
      else:
            UpperCamelCase_					: Dict            =				GPTaConfig.from_json_file(a_						)
      UpperCamelCase_					: Optional[Any]            =				GPTaModel(a_						)

      # Load weights from numpy
      load_tf_weights_in_gpta(a_     , a_     , a_						)

      # Save pytorch-model
      UpperCamelCase_					: str            =				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
      UpperCamelCase_					: Dict            =				pytorch_dump_folder_path + """/""" + CONFIG_NAME
      print(f"Save PyTorch model to {pytorch_weights_dump_path}"						)
      torch.save(model.state_dict()     , a_						)
      print(f"Save configuration file to {pytorch_config_dump_path}"						)
      with open(a_     , """w"""     , encoding="""utf-8"""						) as f:
            f.write(config.to_json_string()						)


if __name__ == "__main__":
      UpperCamelCase      					=argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--gpt2_config_file",
          default="",
          type=str,
          help=(
              "An optional config json file corresponding to the pre-trained OpenAI model. \n"
              "This specifies the model architecture."
          ),
      )
      UpperCamelCase      					=parser.parse_args()
      convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)













"""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

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


__magic_name__       :					Tuple						=			{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """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

		__magic_name__       :					Any						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



