






"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_lowerCAmelCase         =		[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]






def 			__UpperCamelCase	(		snake_case__=True					):
	if with_config:
		return [
		    {
		        "testcase_name": d["dataset"] + "/" + d["config_name"],
		        "dataset": d["dataset"],
		        "config_name": d["config_name"],
		    }
		    for d in DATASETS_ON_HF_GCP
		]
	else:
		return [
		    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
		]



@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=a__						)						)
class      SCREAMING_SNAKE_CASE  (				a__						):





	"""simple docstring"""




	_A       :				Dict    =				None
	_A       :				Optional[int]    =				None





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		with TemporaryDirectory() as tmp_dir:
			A_		:						int	    =   dataset_module_factory(lowerCamelCase_     ,     cache_dir=lowerCamelCase_							)

			A_		:						List[Any]	    =   import_main_class(dataset_module.module_path     ,     dataset=lowerCamelCase_							)

			A_		:						Dict	    =   builder_cls(
			    cache_dir=lowerCamelCase_     ,     config_name=lowerCamelCase_     ,     hash=dataset_module.hash     ,     )

			A_		:						str	    =   """/""".join(
			    [
			        HF_GCP_BASE_URL,
			        builder_instance._relative_data_dir(with_hash=lowerCamelCase_							).replace(os.sep     ,     """/"""							),
			        config.DATASET_INFO_FILENAME,
			    ]							)
			A_		:						int	    =   cached_path(lowerCamelCase_     ,     cache_dir=lowerCamelCase_							)
			self.assertTrue(os.path.exists(lowerCamelCase_							)							)






@pytest.mark.integration
def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Tuple	    =   tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
	A_		:						Optional[Any]	    =   dataset_module_factory("""wikipedia"""   ,   cache_dir=snake_case__					)
	A_		:						Tuple	    =   import_main_class(dataset_module.module_path					)
	A_		:						int	    =   builder_cls(
	    cache_dir=snake_case__   ,   config_name="""20220301.frr"""   ,   hash=dataset_module.hash   ,   )
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	A_		:						Any	    =   None
	builder_instance.download_and_prepare()
	A_		:						Any	    =   builder_instance.as_dataset()
	assert ds






@pytest.mark.integration
def 			__UpperCamelCase	(		snake_case__					):
	A_		:						str	    =   dataset_module_factory("""wikipedia"""   ,   cache_dir=snake_case__					)
	A_		:						Optional[int]	    =   import_main_class(dataset_module.module_path   ,   dataset=snake_case__					)
	A_		:						Tuple	    =   builder_cls(
	    cache_dir=snake_case__   ,   config_name="""20220301.frr"""   ,   hash=dataset_module.hash   ,   )
	A_		:						int	    =   builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(snake_case__   ,   snake_case__					)
	assert "train" in ds
	assert isinstance(ds["""train"""]   ,   snake_case__					)
	assert next(iter(ds["""train"""]					)					)









"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





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

		if text is None and images is None:
			raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

		if text is not None:
			A_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if text is not None and images is not None:
			A_		:						int	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

