

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 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]=True	)						->				Any:




	'''simple docstring'''




	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=_lowercase							)							)
class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  None
		a__					       =  None


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			with TemporaryDirectory() as tmp_dir:
				_UpperCamelCase   :		List[Any]			=						dataset_module_factory(__snake_case	,       cache_dir=__snake_case)

				_UpperCamelCase   :		int			=						import_main_class(dataset_module.module_path	,       dataset=__snake_case)

				_UpperCamelCase   :		DatasetBuilder			=						builder_cls(
				    cache_dir=__snake_case	,       config_name=__snake_case	,       hash=dataset_module.hash	,       )

				_UpperCamelCase   :		Union[str, Any]			=						'/'.join(
				    [
				        HF_GCP_BASE_URL,
				        builder_instance._relative_data_dir(with_hash=__snake_case).replace(os.sep	,       '/'),
				        config.DATASET_INFO_FILENAME,
				    ])
				_UpperCamelCase   :		Tuple			=						cached_path(__snake_case	,       cache_dir=__snake_case)
				self.assertTrue(os.path.exists(__snake_case))






@pytest.mark.integration
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						tmp_path_factory.mktemp('test_hf_gcp'	) / 'test_wikipedia_simple'
	_UpperCamelCase   :		Optional[Any]			=						dataset_module_factory('wikipedia'							,       cache_dir=UpperCAmelCase_	)
	_UpperCamelCase   :		Tuple			=						import_main_class(dataset_module.module_path	)
	_UpperCamelCase   :		DatasetBuilder			=						builder_cls(
	    cache_dir=UpperCAmelCase_							,       config_name='20220301.frr'							,       hash=dataset_module.hash							,       )
	# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
	_UpperCamelCase   :		Dict			=						None
	builder_instance.download_and_prepare()
	_UpperCamelCase   :		Optional[Any]			=						builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						dataset_module_factory('wikipedia'							,       cache_dir=UpperCAmelCase_	)
	_UpperCamelCase   :		Tuple			=						import_main_class(dataset_module.module_path							,       dataset=UpperCAmelCase_	)
	_UpperCamelCase   :		DatasetBuilder			=						builder_cls(
	    cache_dir=UpperCAmelCase_							,       config_name='20220301.frr'							,       hash=dataset_module.hash							,       )
	_UpperCamelCase   :		Union[str, Any]			=						builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(UpperCAmelCase_							,       UpperCAmelCase_	)
	assert "train" in ds
	assert isinstance(ds['train']							,       UpperCAmelCase_	)
	assert next(iter(ds['train']	)	)







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




