


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_      : Optional[int]  					=       [
    {'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_						(			__A :			Tuple=True				)	->		Union[str, 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=lowercase__			)			)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     List[Any]        =						None
      snake_case__				:     List[str]        =						None
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			List[str]:
             with TemporaryDirectory() as tmp_dir:
                    a_					:		Optional[Any]							=  dataset_module_factory(SCREAMING_SNAKE_CASE__     ,							cache_dir=SCREAMING_SNAKE_CASE__     )

                    a_					:		Optional[int]							=  import_main_class(dataset_module.module_path     ,							dataset=SCREAMING_SNAKE_CASE__     )

                    a_					:		DatasetBuilder							=  builder_cls(
                        cache_dir=SCREAMING_SNAKE_CASE__     ,							config_name=SCREAMING_SNAKE_CASE__     ,							hash=dataset_module.hash     ,							)

                    a_					:		Any							=  '/'.join(
                        [
                            HF_GCP_BASE_URL,
                            builder_instance._relative_data_dir(with_hash=SCREAMING_SNAKE_CASE__     ).replace(os.sep     ,							'/'     ),
                            config.DATASET_INFO_FILENAME,
                        ]     )
                    a_					:		Dict							=  cached_path(SCREAMING_SNAKE_CASE__     ,							cache_dir=SCREAMING_SNAKE_CASE__     )
                    self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE__     )     )







@pytest.mark.integration
def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		int:

       """simple docstring"""

       a_					:		List[str]							=  tmp_path_factory.mktemp('test_hf_gcp'				) / 'test_wikipedia_simple'
       a_					:		Union[str, Any]							=  dataset_module_factory('wikipedia'  ,    cache_dir=__A				)
       a_					:		Dict							=  import_main_class(dataset_module.module_path				)
       a_					:		DatasetBuilder							=  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
       a_					:		Optional[Any]							=  None
       builder_instance.download_and_prepare()
       a_					:		List[Any]							=  builder_instance.as_dataset()
       assert ds







@pytest.mark.integration
def        SCREAMING_SNAKE_CASE_						(			__A :			Tuple				)	->		List[Any]:

       """simple docstring"""

       a_					:		Optional[int]							=  dataset_module_factory('wikipedia'  ,    cache_dir=__A				)
       a_					:		Any							=  import_main_class(dataset_module.module_path  ,    dataset=__A				)
       a_					:		DatasetBuilder							=  builder_cls(
           cache_dir=__A  ,    config_name='20220301.frr'  ,    hash=dataset_module.hash  ,    )
       a_					:		Tuple							=  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']				)				)



def    a__       (						__UpperCamelCase ):
      if not head:
            return True
      # split the list to two parts
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     head.next, head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_      =     fast.next.next
            SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     None  # Don't forget here! But forget still works!
      # reverse the second part
      SCREAMING_SNAKE_CASE_      =     None
      while second:
            SCREAMING_SNAKE_CASE_      =     second.next
            SCREAMING_SNAKE_CASE_      =     node
            SCREAMING_SNAKE_CASE_      =     second
            SCREAMING_SNAKE_CASE_      =     nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     node.next
            SCREAMING_SNAKE_CASE_      =     head.next
      return True


def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     fast.next.next, slow.next

      # 2. Push the second half into the stack
      SCREAMING_SNAKE_CASE_      =     [slow.val]
      while slow.next:
            SCREAMING_SNAKE_CASE_      =     slow.next
            stack.append(slow.val )

      # 3. Comparison
      while stack:
            if stack.pop() != cur.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     cur.next

      return True





def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True
      SCREAMING_SNAKE_CASE_      =     {}
      SCREAMING_SNAKE_CASE_      =     0
      while head:
            if head.val in d:
                  d[head.val].append(__UpperCamelCase )
            else:
                  SCREAMING_SNAKE_CASE_      =     [pos]
            SCREAMING_SNAKE_CASE_      =     head.next
            pos += 1
      SCREAMING_SNAKE_CASE_      =     pos - 1
      SCREAMING_SNAKE_CASE_      =     0
      for v in d.values():
            if len(__UpperCamelCase ) % 2 != 0:
                  middle += 1
            else:
                  SCREAMING_SNAKE_CASE_      =     0
                  for i in range(0   ,					len(__UpperCamelCase ) ):
                        if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum:
                              return False
                        step += 1
            if middle > 1:
                  return False
      return True


