import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def       lowerCAmelCase_     (		lowercase:							int  ,					lowercase:							Optional[int]  ,					lowercase:							str		)    ->				List[str]:







 '''simple docstring'''
 # Construct model
 if openai_config_file == "":
  _UpperCamelCase:     Dict							= OpenAIGPTConfig()
 else:
  _UpperCamelCase:     Union[str, Any]							= OpenAIGPTConfig.from_json_file(lowercase		)
 _UpperCamelCase:     Union[str, Any]							= OpenAIGPTModel(lowercase		)

 # Load weights from numpy
 load_tf_weights_in_openai_gpt(lowercase  ,					lowercase  ,					lowercase		)

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


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--openai_checkpoint_folder_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(
      '''--openai_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_openai_checkpoint_to_pytorch(
      args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
  )
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

UpperCAmelCase_			    =							'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
  raise ImportWarning(
      '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
  )

if version.parse(pyarrow.__version__).major < 8:
  raise ImportWarning(
      '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
      '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
  )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

UpperCAmelCase_			    =							concatenate_datasets
UpperCAmelCase_			    =							DownloadConfig
UpperCAmelCase_			    =							DownloadManager
UpperCAmelCase_			    =							DownloadMode
UpperCAmelCase_			    =							DownloadConfig
UpperCAmelCase_			    =							DownloadMode
UpperCAmelCase_			    =							DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager