






'''simple docstring'''





import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowerCamelCase_		  =						logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowerCamelCase_		  =						direct_transformers_import(Path(__file__).parent)


lowerCamelCase_		  =						{
    """AutoTokenizer""": """PreTrainedTokenizerBase""",
    """AutoFeatureExtractor""": """FeatureExtractionMixin""",
    """AutoImageProcessor""": """ImageProcessingMixin""",
}




class 							a_				(		a_	):


							'''simple docstring'''






							__a:			List[str]									=					['''feature_extractor''', '''tokenizer''']
							# Names need to be attr_class for attr in attributes
							__a:			List[str]									=					None
							__a:			Union[str, Any]									=					None
							__a:			Dict									=					None

							def __init__(	self    ,    *lowercase_    ,    **lowercase_     )      ->      int:
														'''simple docstring'''

														for key in kwargs:
																					if key not in self.attributes:
																												raise TypeError(f'''Unexpected keyword argument {key}.'''     )
														for arg, attribute_name in zip(lowercase_    ,    self.attributes     ):
																					if attribute_name in kwargs:
																												raise TypeError(f'''Got multiple values for argument {attribute_name}.'''     )
																					else:
																												lowerCAmelCase_       		=  arg

														if len(lowercase_     ) != len(self.attributes     ):
																					raise ValueError(
																					    f'''This processor requires {len(self.attributes     )} arguments: {", ".join(self.attributes     )}. Got '''
																					    f'''{len(lowercase_     )} arguments instead.'''     )

														# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
														for attribute_name, arg in kwargs.items():
																					lowerCAmelCase_       		=  getattr(self    ,    f'''{attribute_name}_class'''     )
																					# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
																					lowerCAmelCase_       		=  AUTO_TO_BASE_CLASS_MAPPING.get(lowercase_    ,    lowercase_     )
																					if isinstance(lowercase_    ,    lowercase_     ):
																												lowerCAmelCase_       		=  tuple(getattr(lowercase_    ,    lowercase_     ) for n in class_name if n is not None     )
																					else:
																												lowerCAmelCase_       		=  getattr(lowercase_    ,    lowercase_     )

																					if not isinstance(lowercase_    ,    lowercase_     ):
																												raise ValueError(
																												    f'''Received a {type(lowercase_     ).__name__} for argument {attribute_name}, but a {class_name} was expected.'''     )

																					setattr(self    ,    lowercase_    ,    lowercase_     )

							def __repr__(	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  [f'''- {name}: {repr(getattr(self    ,    lowercase_     )     )}''' for name in self.attributes]
														lowerCAmelCase_       		=  '\n'.join(lowercase_     )
														return f'''{self.__class__.__name__}:\n{attributes_repr}'''

							def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = False    ,    **lowercase_     )      ->      Optional[Any]:
														'''simple docstring'''

														os.makedirs(lowercase_    ,    exist_ok=lowercase_     )

														if push_to_hub:
																					lowerCAmelCase_       		=  kwargs.pop('commit_message'    ,    lowercase_     )
																					lowerCAmelCase_       		=  kwargs.pop('repo_id'    ,    save_directory.split(os.path.sep     )[-1]     )
																					lowerCAmelCase_       		=  self._create_repo(lowercase_    ,    **lowercase_     )
																					lowerCAmelCase_       		=  self._get_files_timestamps(lowercase_     )
														# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
														# loaded from the Hub.
														if self._auto_class is not None:
																					lowerCAmelCase_       		=  [getattr(self    ,    lowercase_     ) for attribute_name in self.attributes]
																					lowerCAmelCase_       		=  [(a.init_kwargs if isinstance(lowercase_    ,    lowercase_     ) else a) for a in attrs]
																					custom_object_save(self    ,    lowercase_    ,    config=lowercase_     )

														for attribute_name in self.attributes:
																					lowerCAmelCase_       		=  getattr(self    ,    lowercase_     )
																					# Include the processor class in the attribute config so this processor can then be reloaded with the
																					# `AutoProcessor` API.
																					if hasattr(lowercase_    ,    '_set_processor_class'     ):
																												attribute._set_processor_class(self.__class__.__name__     )
																					attribute.save_pretrained(lowercase_     )

														if self._auto_class is not None:
																					# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
																					for attribute_name in self.attributes:
																												lowerCAmelCase_       		=  getattr(self    ,    lowercase_     )
																												if isinstance(lowercase_    ,    lowercase_     ):
																																			del attribute.init_kwargs["auto_map"]

														if push_to_hub:
																					self._upload_modified_files(
																					    lowercase_    ,    lowercase_    ,    lowercase_    ,    commit_message=lowercase_    ,    token=kwargs.get('use_auth_token'     )    ,    )

							@classmethod
							def   _lowercase (	cls    ,    lowercase_    ,    lowercase_ = None    ,    lowercase_ = False    ,    lowercase_ = False    ,    lowercase_ = None    ,    lowercase_ = "main"    ,    **lowercase_    ,    )      ->      str:
														'''simple docstring'''

														lowerCAmelCase_       		=  cache_dir
														lowerCAmelCase_       		=  force_download
														lowerCAmelCase_       		=  local_files_only
														lowerCAmelCase_       		=  revision

														lowerCAmelCase_       		=  kwargs.pop('use_auth_token'    ,    lowercase_     )
														if use_auth_token is not None:
																					warnings.warn(
																					    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'    ,    lowercase_     )
																					if token is not None:
																												raise ValueError(
																												    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'     )
																					lowerCAmelCase_       		=  use_auth_token

														if token is not None:
																					# change to `token` in a follow-up PR
																					lowerCAmelCase_       		=  token

														lowerCAmelCase_       		=  cls._get_arguments_from_pretrained(lowercase_    ,    **lowercase_     )
														return cls(*lowercase_     )

							@classmethod
							def   _lowercase (	cls    ,    lowercase_="AutoProcessor"     )      ->      Optional[int]:
														'''simple docstring'''

														if not isinstance(lowercase_    ,    lowercase_     ):
																					lowerCAmelCase_       		=  auto_class.__name__

														import transformers.models.auto as auto_module

														if not hasattr(lowercase_    ,    lowercase_     ):
																					raise ValueError(f'''{auto_class} is not a valid auto class.'''     )

														lowerCAmelCase_       		=  auto_class

							@classmethod
							def   _lowercase (	cls    ,    lowercase_    ,    **lowercase_     )      ->      Dict:
														'''simple docstring'''

														lowerCAmelCase_       		=  []
														for attribute_name in cls.attributes:
																					lowerCAmelCase_       		=  getattr(cls    ,    f'''{attribute_name}_class'''     )
																					if isinstance(lowercase_    ,    lowercase_     ):
																												lowerCAmelCase_       		=  tuple(getattr(lowercase_    ,    lowercase_     ) if n is not None else None for n in class_name     )
																												lowerCAmelCase_       		=  kwargs.get('use_fast'    ,    lowercase_     )
																												if use_fast and classes[1] is not None:
																																			lowerCAmelCase_       		=  classes[1]
																												else:
																																			lowerCAmelCase_       		=  classes[0]
																					else:
																												lowerCAmelCase_       		=  getattr(lowercase_    ,    lowercase_     )

																					args.append(attribute_class.from_pretrained(lowercase_    ,    **lowercase_     )     )
														return args






							@property
							def   _lowercase (	self     )      ->      Tuple:
														'''simple docstring'''

														lowerCAmelCase_       		=  getattr(self    ,    self.attributes[0]     )
														return getattr(lowercase_    ,    'model_input_names'    ,    lowercase_     )


lowerCamelCase_		  =						copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
		lowerCamelCase_		  =						ProcessorMixin.push_to_hub.__doc__.format(
		    object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
		)











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


lowerCamelCase_		  =						get_logger(__name__)
def   lowerCamelCase      (		a_			)			->    Tuple:
							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 							a_				(		a_	):


							'''simple docstring'''






							__a:			Any									=					False  # to tell pytest it's not a test class

							@staticmethod
							def   _lowercase (	lowercase_     )      ->      Dict:
														'''simple docstring'''

														lowerCAmelCase_       		=  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    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  dataset
														lowerCAmelCase_       		=  name
														lowerCAmelCase_       		=  cache_dir
														lowerCAmelCase_       		=  data_dir
														lowerCAmelCase_       		=  all_configs
														lowerCAmelCase_       		=  save_infos
														lowerCAmelCase_       		=  ignore_verifications
														lowerCAmelCase_       		=  force_redownload
														lowerCAmelCase_       		=  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:
																					lowerCAmelCase_       		=  True






							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														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     )
														lowerCAmelCase_		,     lowerCAmelCase_       		=  self._dataset, self._name
														lowerCAmelCase_       		=  dataset_module_factory(lowercase_     )
														lowerCAmelCase_       		=  import_main_class(module.module_path     )
														lowerCAmelCase_       		=  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})'''     )
																					lowerCAmelCase_       		=  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:
																												lowerCAmelCase_       		=  os.path.join(builder_cls.get_imported_module_dir()    ,    'README.md'     )
																												lowerCAmelCase_       		=  Path(lowercase_     ).name + '.py'
																												lowerCAmelCase_       		=  os.path.join(lowercase_    ,    lowercase_     )
																												if os.path.isfile(lowercase_     ):
																																			lowerCAmelCase_       		=  os.path.dirname(lowercase_     )
																												elif os.path.isfile(lowercase_     ):
																																			lowerCAmelCase_       		=  path
																												elif os.path.isdir(lowercase_     ):  # for local directories containing only data files
																																			lowerCAmelCase_       		=  path
																												else:  # in case of a remote dataset
																																			lowerCAmelCase_       		=  None
																																			print(f'''Dataset card saved at {dataset_readme_path}'''     )

																												# Move dataset_info back to the user
																												if dataset_dir is not None:
																																			lowerCAmelCase_       		=  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     )
																												lowerCAmelCase_       		=  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.'     )



