"""simple docstring"""


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__UpperCamelCase       :    Union[str, Any]         =		get_logger(__name__)


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   return TestCommand(
       args.dataset,				args.name,				args.cache_dir,				args.data_dir,				args.all_configs,				args.save_info or args.save_infos,				args.ignore_verifications,				args.force_redownload,				args.clear_cache,				)
class   UpperCAmelCase_	(				lowercase__   ):
    snake_case_											=      False  # to tell pytest it's not a test class
    @staticmethod
    def 		_lowerCamelCase      (	_lowercase			:       ArgumentParser		)						->    Optional[int]:
       _lowercase							     =       parser.add_parser("test"   ,						help="Test dataset implementation."		)
       test_parser.add_argument("--name"   ,						type=_lowercase   ,						default=_lowercase   ,						help="Dataset processing name"		)
       test_parser.add_argument(
           "--cache_dir"   ,						type=_lowercase   ,						default=_lowercase   ,						help="Cache directory where the datasets are stored."   ,						)
       test_parser.add_argument(
           "--data_dir"   ,						type=_lowercase   ,						default=_lowercase   ,						help="Can be used to specify a manual directory to get the files from."   ,						)
       test_parser.add_argument("--all_configs"   ,						action="store_true"   ,						help="Test all dataset configurations"		)
       test_parser.add_argument(
           "--save_info"   ,						action="store_true"   ,						help="Save the dataset infos in the dataset card (README.md)"		)
       test_parser.add_argument(
           "--ignore_verifications"   ,						action="store_true"   ,						help="Run the test without checksums and splits checks."   ,						)
       test_parser.add_argument("--force_redownload"   ,						action="store_true"   ,						help="Force dataset redownload"		)
       test_parser.add_argument(
           "--clear_cache"   ,						action="store_true"   ,						help="Remove downloaded files and cached datasets after each config test"   ,						)
       # aliases
       test_parser.add_argument("--save_infos"   ,						action="store_true"   ,						help="alias to save_info"		)
       test_parser.add_argument("dataset"   ,						type=_lowercase   ,						help="Name of the dataset to download"		)
       test_parser.set_defaults(func=_test_command_factory		)
    def __init__(	self			:       Union[str, Any]   ,						_lowercase			:       str   ,						_lowercase			:       str   ,						_lowercase			:       str   ,						_lowercase			:       str   ,						_lowercase			:       bool   ,						_lowercase			:       bool   ,						_lowercase			:       bool   ,						_lowercase			:       bool   ,						_lowercase			:       bool   ,						)						->    int:
       _lowercase							     =       dataset
       _lowercase							     =       name
       _lowercase							     =       cache_dir
       _lowercase							     =       data_dir
       _lowercase							     =       all_configs
       _lowercase							     =       save_infos
       _lowercase							     =       ignore_verifications
       _lowercase							     =       force_redownload
       _lowercase							     =       clear_cache
       if clear_cache and not cache_dir:
          print(
              "When --clear_cache is used, specifying a cache directory is mandatory.\n"
              "The 'download' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n"
              "Please provide a --cache_dir that will be used to test the dataset script."		)
          exit(1		)
       if save_infos:
          _lowercase							     =       True




    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Any:
       fl_logger().setLevel(_lowercase		)
       if self._name is not None and self._all_configs:
          print("Both parameters `config` and `all_configs` can't be used at once."		)
          exit(1		)
       _lowercase     ,  _lowercase							     =       self._dataset, self._name
       _lowercase							     =       dataset_module_factory(_lowercase		)
       _lowercase							     =       import_main_class(module.module_path		)
       _lowercase							     =       len(builder_cls.BUILDER_CONFIGS		) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

       def get_builders() -> Generator[DatasetBuilder, None, None]:
          if self._all_configs and builder_cls.BUILDER_CONFIGS:
             for i, config in enumerate(builder_cls.BUILDER_CONFIGS		):
                if "config_name" in module.builder_kwargs:
                   yield builder_cls(
                       cache_dir=self._cache_dir   ,						data_dir=self._data_dir   ,						**module.builder_kwargs   ,						)
                else:
                   yield builder_cls(
                       config_name=config.name   ,						cache_dir=self._cache_dir   ,						data_dir=self._data_dir   ,						**module.builder_kwargs   ,						)
          else:
             if "config_name" in module.builder_kwargs:
                yield builder_cls(cache_dir=self._cache_dir   ,						data_dir=self._data_dir   ,						**module.builder_kwargs		)
             else:
                yield builder_cls(
                    config_name=_lowercase   ,						cache_dir=self._cache_dir   ,						data_dir=self._data_dir   ,						**module.builder_kwargs   ,						)

       for j, builder in enumerate(get_builders()		):
          print(f"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""		)
          _lowercase							     =       os.path.exists(
              os.path.join(builder.get_imported_module_dir()   ,						datasets.config.DATASETDICT_INFOS_FILENAME		)		)  # record checksums only if we need to update a (deprecated) dataset_infos.json
          builder.download_and_prepare(
              download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
              if not self._force_redownload
              else DownloadMode.FORCE_REDOWNLOAD   ,						verification_mode=VerificationMode.NO_CHECKS
              if self._ignore_verifications
              else VerificationMode.ALL_CHECKS   ,						try_from_hf_gcs=_lowercase   ,						)
          builder.as_dataset()
          if self._save_infos:
             builder._save_infos()

          # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
          # The dataset_infos are saved in the YAML part of the README.md

          # Let's move it to the original directory of the dataset script, to allow the user to
          # upload them on S3 at the same time afterwards.
          if self._save_infos:
             _lowercase							     =       os.path.join(builder_cls.get_imported_module_dir()   ,						"README.md"		)
             _lowercase							     =       Path(_lowercase		).name + ".py"
             _lowercase							     =       os.path.join(_lowercase   ,						_lowercase		)
             if os.path.isfile(_lowercase		):
                _lowercase							     =       os.path.dirname(_lowercase		)
             elif os.path.isfile(_lowercase		):
                _lowercase							     =       path
             elif os.path.isdir(_lowercase		):  # for local directories containing only data files
                _lowercase							     =       path
             else:  # in case of a remote dataset
                _lowercase							     =       None
                print(f"""Dataset card saved at {dataset_readme_path}"""		)

             # Move dataset_info back to the user
             if dataset_dir is not None:
                _lowercase							     =       os.path.join(_lowercase   ,						"README.md"		)
                copyfile(_lowercase   ,						_lowercase		)
                print(f"""Dataset card saved at {user_dataset_readme_path}"""		)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
          if self._clear_cache:
             if os.path.isdir(builder._cache_dir		):
                logger.warning(f"""Clearing cache at {builder._cache_dir}"""		)
                rmtree(builder._cache_dir		)
             _lowercase							     =       os.path.join(self._cache_dir   ,						datasets.config.DOWNLOADED_DATASETS_DIR		)
             if os.path.isdir(_lowercase		):
                logger.warning(f"""Clearing cache at {download_dir}"""		)
                rmtree(_lowercase		)

       print("Test successful."		)
"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase       :    Any         =		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Optional[int]         =		["OpenAIGPTTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    List[Any]         =		[
      "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OpenAIGPTDoubleHeadsModel",
      "OpenAIGPTForSequenceClassification",
      "OpenAIGPTLMHeadModel",
      "OpenAIGPTModel",
      "OpenAIGPTPreTrainedModel",
      "load_tf_weights_in_openai_gpt",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Tuple         =		[
      "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFOpenAIGPTDoubleHeadsModel",
      "TFOpenAIGPTForSequenceClassification",
      "TFOpenAIGPTLMHeadModel",
      "TFOpenAIGPTMainLayer",
      "TFOpenAIGPTModel",
      "TFOpenAIGPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

  __UpperCamelCase       :    str         =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)