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


UpperCamelCase__											=   [
    {'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_					(	__A=True				)	->							Tuple:







      '''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=UpperCAmelCase_	)	)
class 	A				(							UpperCAmelCase_	):
   __UpperCAmelCase					:					Dict    =     None
   __UpperCAmelCase					:					Optional[int]    =     None


   def      lowercase_ (self	:		List[str]			,    __UpperCAmelCase	:		Dict			,    __UpperCAmelCase	:		Any						)		->							str:
         """simple docstring"""



         with TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__								=    dataset_module_factory(__SCREAMING_SNAKE_CASE			,    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=__SCREAMING_SNAKE_CASE			,    hash=dataset_module.hash			,    )

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




@pytest.mark.integration
def   lowerCAmelCase_					(	__A				)	->							Any:







      '''simple docstring'''


      UpperCAmelCase__								=    tmp_path_factory.mktemp("test_hf_gcp"				) / "test_wikipedia_simple"
      UpperCAmelCase__								=    dataset_module_factory("wikipedia",    cache_dir=a__				)
      UpperCAmelCase__								=    import_main_class(dataset_module.module_path				)
      UpperCAmelCase__								=    builder_cls(
          cache_dir=a__,    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   lowerCAmelCase_					(	__A				)	->							str:







      '''simple docstring'''


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






'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """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:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if images is not None:
           __SCREAMING_SNAKE_CASE      = self.image_processor(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




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


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




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






