



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_    :					List[Any]             =get_logger(__name__)




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""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        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







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


							@staticmethod
							def 						lowercase__     (   _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		parser.add_parser("""test"""			,    help="""Test dataset implementation."""		)
													test_parser.add_argument("""--name"""			,    type=_UpperCAmelCase			,    default=_UpperCAmelCase			,    help="""Dataset processing name"""		)
													test_parser.add_argument(
													    """--cache_dir"""			,    type=_UpperCAmelCase			,    default=_UpperCAmelCase			,    help="""Cache directory where the datasets are stored."""			,    )
													test_parser.add_argument(
													    """--data_dir"""			,    type=_UpperCAmelCase			,    default=_UpperCAmelCase			,    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=_UpperCAmelCase			,    help="""Name of the dataset to download"""		)
													test_parser.set_defaults(func=_test_command_factory		)


							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):







													"""simple docstring"""


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






							def 						lowercase__     (   self		):







													"""simple docstring"""


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

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

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



from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch





class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		['''image_processor''', '''tokenizer''']
							snake_case_        =		'''OneFormerImageProcessor'''
							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if image_processor is None:
																			raise ValueError("""You need to specify an `image_processor`."""		)
													if tokenizer is None:
																			raise ValueError("""You need to specify a `tokenizer`."""		)

													a_					=		max_seq_length
													a_					=		task_seq_length

													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):







													"""simple docstring"""


													if text_list is None:
																			raise ValueError("""tokens cannot be None."""		)

													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)

													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]

													a_					=		[]
													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
																			token_inputs.append(token.unsqueeze(0		)		)

													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
													return token_inputs


							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)






							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)