





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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   if gpta_config_file == "":
      A_     :				List[Any]			      =	GPTaConfig()
   else:
      A_     :				List[str]			      =	GPTaConfig.from_json_file(_lowerCAmelCase     )
   A_     :				Optional[int]			      =	GPTaModel(_lowerCAmelCase     )

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

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


if __name__ == "__main__":
      _lowerCAmelCase 				=     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."""
          ),
      )
      _lowerCAmelCase 				=     parser.parse_args()
      convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)













# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase 				=     """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
      print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
      print("""Torch version:""", None)

try:
      import deepspeed

      print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
      print("""DeepSpeed version:""", None)

try:
      import tensorflow as tf

      print("""TensorFlow version:""", tf.__version__)
      print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
      print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
      print("""TensorFlow version:""", None)






