



"""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   ):
      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=SCREAMING_SNAKE_CASE   )   )
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  None
       UpperCamelCase		=  None



       def   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						Optional[int]	,       _UpperCAmelCase    :						Union[str, Any]					)				->		int:


             '''simple docstring'''




             with TemporaryDirectory() as tmp_dir:
                   UpperCAmelCase_    				=							dataset_module_factory(_UpperCAmelCase	,       cache_dir=_UpperCAmelCase					)

                   UpperCAmelCase_    				=							import_main_class(dataset_module.module_path	,       dataset=_UpperCAmelCase					)

                   UpperCAmelCase_    				=							builder_cls(
                       cache_dir=_UpperCAmelCase	,       config_name=_UpperCAmelCase	,       hash=dataset_module.hash	,       )

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



@pytest.mark.integration
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							tmp_path_factory.mktemp("test_hf_gcp"   ) / "test_wikipedia_simple"
      UpperCAmelCase_    				=							dataset_module_factory("wikipedia"			,    cache_dir=lowerCAmelCase__   )
      UpperCAmelCase_    				=							import_main_class(dataset_module.module_path   )
      UpperCAmelCase_    				=							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
      UpperCAmelCase_    				=							None
      builder_instance.download_and_prepare()
      UpperCAmelCase_    				=							builder_instance.as_dataset()
      assert ds
@pytest.mark.integration
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dataset_module_factory("wikipedia"			,    cache_dir=lowerCAmelCase__   )
      UpperCAmelCase_    				=							import_main_class(dataset_module.module_path			,    dataset=lowerCAmelCase__   )
      UpperCAmelCase_    				=							builder_cls(
          cache_dir=lowerCAmelCase__			,    config_name="20220301.frr"			,    hash=dataset_module.hash			,    )
      UpperCAmelCase_    				=							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 heapq import heappop, heappush

import numpy as np
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							grid.shape
      UpperCAmelCase_    				=							[-1, 1, 0, 0]
      UpperCAmelCase_    				=							[0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      UpperCAmelCase_				,					UpperCAmelCase_    				=							[(0, source)], set()
      UpperCAmelCase_    				=							np.full((rows, cols)			,    np.inf   )
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							np.empty((rows, cols)			,    dtype=lowerCAmelCase__   )
      UpperCAmelCase_    				=							None

      while queue:
            ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heappop(lowerCAmelCase__   )
            if (x, y) in visited:
                  continue
            visited.add((x, y)   )

            if (x, y) == destination:
                  UpperCAmelCase_    				=							[]
                  while (x, y) != source:
                        path.append((x, y)   )
                        UpperCAmelCase_				,					UpperCAmelCase_    				=							predecessors[x, y]
                  path.append(lowerCAmelCase__   )  # add the source manually
                  path.reverse()
                  return matrix[destination], path

            for i in range(len(lowerCAmelCase__   )   ):
                  UpperCAmelCase_				,					UpperCAmelCase_    				=							x + dx[i], y + dy[i]
                  if 0 <= nx < rows and 0 <= ny < cols:
                        UpperCAmelCase_    				=							grid[nx][ny]
                        if next_node == 1 and matrix[nx, ny] > dist + 1:
                              heappush(lowerCAmelCase__			,    (dist + 1, (nx, ny))   )
                              UpperCAmelCase_    				=							dist + 1
                              UpperCAmelCase_    				=							(x, y)

      return np.inf, []


if __name__ == "__main__":
  import doctest

  doctest.testmod()




