



"""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


UpperCAmelCase						:		str			= [
    {"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						(__lowerCAmelCase=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=__UpperCAmelCase   )   )
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   None
    lowercase__      =   None

    def 					_UpperCAmelCase	(				self			:				Any ,  lowerCAmelCase_			:				Tuple ,  lowerCAmelCase_			:				int):







      """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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						str:







  '''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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						List[str]:







  '''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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Any			= {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= ["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Dict			= ["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		int			= [
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[str]			= [
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure)

