



'''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   _A       (   _lowerCAmelCase=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=A		)		)
class _UpperCamelCase		(     A		):





    '''simple docstring'''





    lowerCAmelCase__					    =    None
    lowerCAmelCase__					    =    None

    def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[str]  ,       _lowerCAmelCase  :  Dict):







           '''simple docstring'''

           with TemporaryDirectory() as tmp_dir:
                  __lowercase       	=dataset_module_factory(_lowerCAmelCase  ,       cache_dir=_lowerCAmelCase)

                  __lowercase       	=import_main_class(dataset_module.module_path  ,       dataset=_lowerCAmelCase)

                  __lowercase       	=builder_cls(
                      cache_dir=_lowerCAmelCase  ,       config_name=_lowerCAmelCase  ,       hash=dataset_module.hash  ,       )

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



@pytest.mark.integration
def   _A       (   _lowerCAmelCase					):



       """simple docstring"""



       __lowercase       	=tmp_path_factory.mktemp('test_hf_gcp'					) / 'test_wikipedia_simple'
       __lowercase       	=dataset_module_factory('wikipedia'  ,   cache_dir=_lowerCAmelCase					)
       __lowercase       	=import_main_class(dataset_module.module_path					)
       __lowercase       	=builder_cls(
           cache_dir=_lowerCAmelCase  ,   config_name='20220301.frr'  ,   hash=dataset_module.hash  ,   )
       # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
       __lowercase       	=None
       builder_instance.download_and_prepare()
       __lowercase       	=builder_instance.as_dataset()
       assert ds



@pytest.mark.integration
def   _A       (   _lowerCAmelCase					):



       """simple docstring"""



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








'''simple docstring'''



import warnings
from typing import List, Optional, Union

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





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    ["""image_processor""", """tokenizer"""]
				lowerCAmelCase__					    =    """ViltImageProcessor"""
				lowerCAmelCase__					    =    ("""BertTokenizer""", """BertTokenizerFast""")

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







											'''simple docstring'''

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

											__lowercase       	=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__(_lowerCAmelCase  ,       _lowerCAmelCase)
											__lowercase       	=self.image_processor

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







											'''simple docstring'''

											__lowercase       	=self.tokenizer(
											    text=_lowerCAmelCase  ,       add_special_tokens=_lowerCAmelCase  ,       padding=_lowerCAmelCase  ,       truncation=_lowerCAmelCase  ,       max_length=_lowerCAmelCase  ,       stride=_lowerCAmelCase  ,       pad_to_multiple_of=_lowerCAmelCase  ,       return_token_type_ids=_lowerCAmelCase  ,       return_attention_mask=_lowerCAmelCase  ,       return_overflowing_tokens=_lowerCAmelCase  ,       return_special_tokens_mask=_lowerCAmelCase  ,       return_offsets_mapping=_lowerCAmelCase  ,       return_length=_lowerCAmelCase  ,       verbose=_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )
											# add pixel_values + pixel_mask
											__lowercase       	=self.image_processor(_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase)
											encoding.update(_lowerCAmelCase)

											return encoding

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

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

				@property
				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

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

				@property
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

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



