






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 :      Any										=				[
    {"""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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Any=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=UpperCamelCase_						)						)
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		None
					a_     =		None



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Optional[Any]							,		__A					:  Optional[int]	):
						with TemporaryDirectory() as tmp_dir:
							snake_case__			:				Optional[Any]       =  dataset_module_factory(__A							,		cache_dir=__A	)

							snake_case__			:				Tuple       =  import_main_class(dataset_module.module_path							,		dataset=__A	)

							snake_case__			:				DatasetBuilder       =  builder_cls(
							    cache_dir=__A							,		config_name=__A							,		hash=dataset_module.hash							,		)

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







@pytest.mark.integration
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__			:				Dict       =  tmp_path_factory.mktemp("test_hf_gcp"       ) / "test_wikipedia_simple"
	snake_case__			:				Optional[int]       =  dataset_module_factory("wikipedia"					,  cache_dir=snake_case_       )
	snake_case__			:				Union[str, Any]       =  import_main_class(dataset_module.module_path       )
	snake_case__			:				DatasetBuilder       =  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
	snake_case__			:				Any       =  None
	builder_instance.download_and_prepare()
	snake_case__			:				Tuple       =  builder_instance.as_dataset()
	assert ds







@pytest.mark.integration
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[int]       ):
	snake_case__			:				Tuple       =  dataset_module_factory("wikipedia"					,  cache_dir=snake_case_       )
	snake_case__			:				Optional[int]       =  import_main_class(dataset_module.module_path					,  dataset=snake_case_       )
	snake_case__			:				DatasetBuilder       =  builder_cls(
	    cache_dir=snake_case_					,  config_name="20220301.frr"					,  hash=dataset_module.hash					,  )
	snake_case__			:				Dict       =  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"]       )       )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Any										=				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      List[Any]										=				[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

	__lowerCamelCase :      Any										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



