





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase__      :   Optional[int]      	=			logging.get_logger(__name__)


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	['''pixel_values''']
					def __init__(   self       :      Any		,	UpperCAmelCase_       :      bool = True		,	UpperCAmelCase_       :      Optional[Dict[str, int]] = None		,	UpperCAmelCase_       :      PILImageResampling = PILImageResampling.BILINEAR		,	UpperCAmelCase_       :      bool = True		,	UpperCAmelCase_       :      Union[int, float] = 1 / 255		,	UpperCAmelCase_       :      bool = True		,	UpperCAmelCase_       :      Optional[Union[float, List[float]]] = None		,	UpperCAmelCase_       :      Optional[Union[float, List[float]]] = None		,	**UpperCAmelCase_       :      Optional[Any]		,	):







							"""simple docstring"""
							super().__init__(**UpperCAmelCase_		)
							__UpperCAmelCase	:							Optional[int]     =     size if size is not None else {"height": 224, "width": 224}
							__UpperCAmelCase	:							List[str]     =     get_size_dict(UpperCAmelCase_		)
							__UpperCAmelCase	:							str     =     do_resize
							__UpperCAmelCase	:							int     =     do_rescale
							__UpperCAmelCase	:							int     =     do_normalize
							__UpperCAmelCase	:							List[Any]     =     size
							__UpperCAmelCase	:							List[str]     =     resample
							__UpperCAmelCase	:							Union[str, Any]     =     rescale_factor
							__UpperCAmelCase	:							List[str]     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__UpperCAmelCase	:							Dict     =     image_std if image_std is not None else IMAGENET_STANDARD_STD
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      np.ndarray		,	UpperCAmelCase_       :      Dict[str, int]		,	UpperCAmelCase_       :      PILImageResampling = PILImageResampling.BILINEAR		,	UpperCAmelCase_       :      Optional[Union[str, ChannelDimension]] = None		,	**UpperCAmelCase_       :      List[str]		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     get_size_dict(UpperCAmelCase_		)
							if "height" not in size or "width" not in size:
									raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
							return resize(
							    UpperCAmelCase_		,	size=(size["height"], size["width"])		,	resample=UpperCAmelCase_		,	data_format=UpperCAmelCase_		,	**UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      np.ndarray		,	UpperCAmelCase_       :      float		,	UpperCAmelCase_       :      Optional[Union[str, ChannelDimension]] = None		,	**UpperCAmelCase_       :      List[str]		):







							"""simple docstring"""
							return rescale(UpperCAmelCase_		,	scale=UpperCAmelCase_		,	data_format=UpperCAmelCase_		,	**UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      np.ndarray		,	UpperCAmelCase_       :      Union[float, List[float]]		,	UpperCAmelCase_       :      Union[float, List[float]]		,	UpperCAmelCase_       :      Optional[Union[str, ChannelDimension]] = None		,	**UpperCAmelCase_       :      List[Any]		,	):







							"""simple docstring"""
							return normalize(UpperCAmelCase_		,	mean=UpperCAmelCase_		,	std=UpperCAmelCase_		,	data_format=UpperCAmelCase_		,	**UpperCAmelCase_		)






					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      ImageInput		,	UpperCAmelCase_       :      Optional[bool] = None		,	UpperCAmelCase_       :      Dict[str, int] = None		,	UpperCAmelCase_       :      PILImageResampling = None		,	UpperCAmelCase_       :      Optional[bool] = None		,	UpperCAmelCase_       :      Optional[float] = None		,	UpperCAmelCase_       :      Optional[bool] = None		,	UpperCAmelCase_       :      Optional[Union[float, List[float]]] = None		,	UpperCAmelCase_       :      Optional[Union[float, List[float]]] = None		,	UpperCAmelCase_       :      Optional[Union[str, TensorType]] = None		,	UpperCAmelCase_       :      Union[str, ChannelDimension] = ChannelDimension.FIRST		,	**UpperCAmelCase_       :      Union[str, Any]		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							str     =     do_resize if do_resize is not None else self.do_resize
							__UpperCAmelCase	:							str     =     do_rescale if do_rescale is not None else self.do_rescale
							__UpperCAmelCase	:							List[str]     =     do_normalize if do_normalize is not None else self.do_normalize
							__UpperCAmelCase	:							List[str]     =     resample if resample is not None else self.resample
							__UpperCAmelCase	:							List[str]     =     rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCAmelCase	:							Tuple     =     image_mean if image_mean is not None else self.image_mean
							__UpperCAmelCase	:							str     =     image_std if image_std is not None else self.image_std

							__UpperCAmelCase	:							Dict     =     size if size is not None else self.size
							__UpperCAmelCase	:							Optional[int]     =     get_size_dict(UpperCAmelCase_		)

							__UpperCAmelCase	:							Union[str, Any]     =     make_list_of_images(UpperCAmelCase_		)

							if not valid_images(UpperCAmelCase_		):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray."		)

							if do_resize and size is None:
									raise ValueError("Size must be specified if do_resize is True."		)

							if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True."		)

							# All transformations expect numpy arrays.
							__UpperCAmelCase	:							Union[str, Any]     =     [to_numpy_array(UpperCAmelCase_		) for image in images]

							if do_resize:
									__UpperCAmelCase	:							Optional[Any]     =     [self.resize(image=UpperCAmelCase_		,	size=UpperCAmelCase_		,	resample=UpperCAmelCase_		) for image in images]

							if do_rescale:
									__UpperCAmelCase	:							int     =     [self.rescale(image=UpperCAmelCase_		,	scale=UpperCAmelCase_		) for image in images]

							if do_normalize:
									__UpperCAmelCase	:							Optional[Any]     =     [self.normalize(image=UpperCAmelCase_		,	mean=UpperCAmelCase_		,	std=UpperCAmelCase_		) for image in images]

							__UpperCAmelCase	:							str     =     [to_channel_dimension_format(UpperCAmelCase_		,	UpperCAmelCase_		) for image in images]

							__UpperCAmelCase	:							int     =     {"pixel_values": images}
							return BatchFeature(data=UpperCAmelCase_		,	tensor_type=UpperCAmelCase_		)










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


__A    :				Union[str, Any]    						=	get_logger(__name__)
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			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_   ( A							):




							'''simple docstring'''



							a__    	=  False  # to tell pytest it's not a test class





							@staticmethod
							def  _UpperCAmelCase  (       a							:						ArgumentParser      )      ->       str:
										SCREAMING_SNAKE_CASE     =       parser.add_parser("""test"""							,  help="""Test dataset implementation."""      )
										test_parser.add_argument("""--name"""							,  type=a							,  default=a							,  help="""Dataset processing name"""      )
										test_parser.add_argument(
										    """--cache_dir"""							,  type=a							,  default=a							,  help="""Cache directory where the datasets are stored."""							,  )
										test_parser.add_argument(
										    """--data_dir"""							,  type=a							,  default=a							,  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=a							,  help="""Name of the dataset to download"""      )
										test_parser.set_defaults(func=_test_command_factory      )





							def __init__(       self							:						List[Any]							,  a							:						str							,  a							:						str							,  a							:						str							,  a							:						str							,  a							:						bool							,  a							:						bool							,  a							:						bool							,  a							:						bool							,  a							:						bool							,  )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       dataset
										SCREAMING_SNAKE_CASE     =       name
										SCREAMING_SNAKE_CASE     =       cache_dir
										SCREAMING_SNAKE_CASE     =       data_dir
										SCREAMING_SNAKE_CASE     =       all_configs
										SCREAMING_SNAKE_CASE     =       save_infos
										SCREAMING_SNAKE_CASE     =       ignore_verifications
										SCREAMING_SNAKE_CASE     =       force_redownload
										SCREAMING_SNAKE_CASE     =       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:
													SCREAMING_SNAKE_CASE     =       True







							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Optional[int]:
										fl_logger().setLevel(a      )
										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      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self._dataset, self._name
										SCREAMING_SNAKE_CASE     =       dataset_module_factory(a      )
										SCREAMING_SNAKE_CASE     =       import_main_class(module.module_path      )
										SCREAMING_SNAKE_CASE     =       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=a							,  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})"""      )
													SCREAMING_SNAKE_CASE     =       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=a							,  )
													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:
																SCREAMING_SNAKE_CASE     =       os.path.join(builder_cls.get_imported_module_dir()							,  """README.md"""      )
																SCREAMING_SNAKE_CASE     =       Path(a      ).name + """.py"""
																SCREAMING_SNAKE_CASE     =       os.path.join(a							,  a      )
																if os.path.isfile(a      ):
																			SCREAMING_SNAKE_CASE     =       os.path.dirname(a      )
																elif os.path.isfile(a      ):
																			SCREAMING_SNAKE_CASE     =       path
																elif os.path.isdir(a      ):  # for local directories containing only data files
																			SCREAMING_SNAKE_CASE     =       path
																else:  # in case of a remote dataset
																			SCREAMING_SNAKE_CASE     =       None
																			print(f"""Dataset card saved at {dataset_readme_path}"""      )

																# Move dataset_info back to the user
																if dataset_dir is not None:
																			SCREAMING_SNAKE_CASE     =       os.path.join(a							,  """README.md"""      )
																			copyfile(a							,  a      )
																			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      )
																SCREAMING_SNAKE_CASE     =       os.path.join(self._cache_dir							,  datasets.config.DOWNLOADED_DATASETS_DIR      )
																if os.path.isdir(a      ):
																			logger.warning(f"""Clearing cache at {download_dir}"""      )
																			rmtree(a      )

										print("""Test successful."""      )






