


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


__snake_case            =   [
    {"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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict=True	):





			"""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=__snake_case    )    )
class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= None
			lowercase						= None




			def   lowerCamelCase_					(						self							:		str       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						with TemporaryDirectory() as tmp_dir:
									UpperCamelCase         =					dataset_module_factory(__magic_name__       ,       cache_dir=__magic_name__						)

									UpperCamelCase         =					import_main_class(dataset_module.module_path       ,       dataset=__magic_name__						)

									UpperCamelCase         =					builder_cls(
									    cache_dir=__magic_name__       ,       config_name=__magic_name__       ,       hash=dataset_module.hash       ,       )

									UpperCamelCase         =					"""/""".join(
									    [
									        HF_GCP_BASE_URL,
									        builder_instance._relative_data_dir(with_hash=__magic_name__						).replace(os.sep       ,       """/"""						),
									        config.DATASET_INFO_FILENAME,
									    ]						)
									UpperCamelCase         =					cached_path(__magic_name__       ,       cache_dir=__magic_name__						)
									self.assertTrue(os.path.exists(__magic_name__						)						)






@pytest.mark.integration
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




			UpperCamelCase         =					tmp_path_factory.mktemp("""test_hf_gcp"""	) / """test_wikipedia_simple"""
			UpperCamelCase         =					dataset_module_factory("""wikipedia"""       ,  cache_dir=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					import_main_class(dataset_module.module_path	)
			UpperCamelCase         =					builder_cls(
			    cache_dir=SCREAMING_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
			UpperCamelCase         =					None
			builder_instance.download_and_prepare()
			UpperCamelCase         =					builder_instance.as_dataset()
			assert ds






@pytest.mark.integration
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					dataset_module_factory("""wikipedia"""       ,  cache_dir=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					import_main_class(dataset_module.module_path       ,  dataset=SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					builder_cls(
			    cache_dir=SCREAMING_SNAKE_CASE_       ,  config_name="""20220301.frr"""       ,  hash=dataset_module.hash       ,  )
			UpperCamelCase         =					builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			assert "train" in ds
			assert isinstance(ds["""train"""]       ,  SCREAMING_SNAKE_CASE_	)
			assert next(iter(ds["""train"""]	)	)








import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= ["""image_processor""", """tokenizer"""]
			lowercase						= """FlavaImageProcessor"""
			lowercase						= ("""BertTokenizer""", """BertTokenizerFast""")




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		str=None       ,       **__magic_name__							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""       ,       __magic_name__       ,       )
									UpperCamelCase         =					kwargs.pop("""feature_extractor"""						)

						UpperCamelCase         =					image_processor if image_processor is not None else feature_extractor
						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`."""						)

						super().__init__(__magic_name__       ,       __magic_name__						)
						UpperCamelCase         =					self.image_processor




			def __call__(						self							:		int       ,       __magic_name__							:		Optional[ImageInput] = None       ,       __magic_name__							:		Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Union[bool, str, PaddingStrategy] = False       ,       __magic_name__							:		Union[bool, str, TruncationStrategy] = False       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		int = 0       ,       __magic_name__							:		Optional[int] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		Optional[bool] = None       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		bool = True       ,       __magic_name__							:		Optional[Union[str, TensorType]] = None       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







						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:
									UpperCamelCase         =					self.tokenizer(
									    text=__magic_name__       ,       add_special_tokens=__magic_name__       ,       padding=__magic_name__       ,       truncation=__magic_name__       ,       max_length=__magic_name__       ,       stride=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_token_type_ids=__magic_name__       ,       return_attention_mask=__magic_name__       ,       return_overflowing_tokens=__magic_name__       ,       return_special_tokens_mask=__magic_name__       ,       return_offsets_mapping=__magic_name__       ,       return_length=__magic_name__       ,       verbose=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )
						if images is not None:
									UpperCamelCase         =					self.image_processor(
									    __magic_name__       ,       return_image_mask=__magic_name__       ,       return_codebook_pixels=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__       ,       )

						if text is not None and images is not None:
									encoding.update(__magic_name__						)
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__magic_name__						)       ,       tensor_type=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple       ,       *__magic_name__							:		Dict       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.tokenizer.batch_decode(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		int       ,       *__magic_name__							:		Tuple       ,       **__magic_name__							:		Optional[Any]						):







						"""simple docstring"""







						return self.tokenizer.decode(*__magic_name__       ,       **__magic_name__						)




			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.tokenizer.model_input_names
						UpperCamelCase         =					self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,       __magic_name__       ,       )
						return self.image_processor_class





			@property
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,       __magic_name__       ,       )
						return self.image_processor




