import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam



class 				a							(			datasets.BeamBasedBuilder       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        return datasets.DatasetInfo(
            features=datasets.Features({"""content""": datasets.Value("""string"""							)}							)		,				supervised_keys=lowerCAmelCase_		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,				gen_kwargs={"""examples""": get_test_dummy_examples()}							)]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        import apache_beam as beam

        return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase_							)



class 				a							(			datasets.BeamBasedBuilder       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Dict:
        return datasets.DatasetInfo(
            features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""							)}							)}							)		,				supervised_keys=lowerCAmelCase_		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Union[str, Any]:
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN		,				gen_kwargs={"""examples""": get_test_nested_examples()}							)
        ]



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        import apache_beam as beam

        return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase_							)






def  snake_case  (       )						->   List[Any]:
       return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""])]






def  snake_case  (       )						->   Optional[int]:
       return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""])]







class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 @require_beam
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  len(get_test_dummy_examples()							)
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               _A        =  DummyBeamDataset(cache_dir=lowerCAmelCase_		,				beam_runner="""DirectRunner"""							)
               builder.download_and_prepare()
               self.assertTrue(
                   os.path.exists(
                       os.path.join(lowerCAmelCase_		,				builder.name		,				"""default"""		,				"""0.0.0"""		,				F'''{builder.name}-train.arrow'''							)							)							)
               self.assertDictEqual(builder.info.features		,				datasets.Features({"""content""": datasets.Value("""string"""							)}							)							)
               _A        =  builder.as_dataset()
               self.assertEqual(dset["""train"""].num_rows		,				lowerCAmelCase_							)
               self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples		,				lowerCAmelCase_							)
               self.assertDictEqual(dset["""train"""][0]		,				get_test_dummy_examples()[0][1]							)
               self.assertDictEqual(
                   dset["""train"""][expected_num_examples - 1]		,				get_test_dummy_examples()[expected_num_examples - 1][1]							)
               self.assertTrue(
                   os.path.exists(os.path.join(lowerCAmelCase_		,				builder.name		,				"""default"""		,				"""0.0.0"""		,				"""dataset_info.json"""							)							)							)
               del dset



 @require_beam
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        import apache_beam as beam

        _A        =  beam.io.parquetio.WriteToParquet

        _A        =  len(get_test_dummy_examples()							)
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               _A        =  DummyBeamDataset(cache_dir=lowerCAmelCase_		,				beam_runner="""DirectRunner"""							)
               with patch("""apache_beam.io.parquetio.WriteToParquet"""							) as write_parquet_mock:
                      _A        =  partial(lowerCAmelCase_		,				num_shards=2							)
                      builder.download_and_prepare()
               self.assertTrue(
                   os.path.exists(
                       os.path.join(
                           lowerCAmelCase_		,				builder.name		,				"""default"""		,				"""0.0.0"""		,				F'''{builder.name}-train-00000-of-00002.arrow'''							)							)							)
               self.assertTrue(
                   os.path.exists(
                       os.path.join(
                           lowerCAmelCase_		,				builder.name		,				"""default"""		,				"""0.0.0"""		,				F'''{builder.name}-train-00000-of-00002.arrow'''							)							)							)
               self.assertDictEqual(builder.info.features		,				datasets.Features({"""content""": datasets.Value("""string"""							)}							)							)
               _A        =  builder.as_dataset()
               self.assertEqual(dset["""train"""].num_rows		,				lowerCAmelCase_							)
               self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples		,				lowerCAmelCase_							)
               # Order is not preserved when sharding, so we just check that all the elements are there
               self.assertListEqual(sorted(dset["""train"""]["""content"""]							)		,				sorted(["""foo""", """bar""", """foobar"""]							)							)
               self.assertTrue(
                   os.path.exists(os.path.join(lowerCAmelCase_		,				builder.name		,				"""default"""		,				"""0.0.0"""		,				"""dataset_info.json"""							)							)							)
               del dset



 @require_beam
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               _A        =  DummyBeamDataset(cache_dir=lowerCAmelCase_							)
               self.assertRaises(datasets.builder.MissingBeamOptions		,				builder.download_and_prepare							)



 @require_beam
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  len(get_test_nested_examples()							)
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               _A        =  NestedBeamDataset(cache_dir=lowerCAmelCase_		,				beam_runner="""DirectRunner"""							)
               builder.download_and_prepare()
               self.assertTrue(
                   os.path.exists(
                       os.path.join(lowerCAmelCase_		,				builder.name		,				"""default"""		,				"""0.0.0"""		,				F'''{builder.name}-train.arrow'''							)							)							)
               self.assertDictEqual(
                   builder.info.features		,				datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""							)}							)}							)							)
               _A        =  builder.as_dataset()
               self.assertEqual(dset["""train"""].num_rows		,				lowerCAmelCase_							)
               self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples		,				lowerCAmelCase_							)
               self.assertDictEqual(dset["""train"""][0]		,				get_test_nested_examples()[0][1]							)
               self.assertDictEqual(
                   dset["""train"""][expected_num_examples - 1]		,				get_test_nested_examples()[expected_num_examples - 1][1]							)
               self.assertTrue(
                   os.path.exists(os.path.join(lowerCAmelCase_		,				builder.name		,				"""default"""		,				"""0.0.0"""		,				"""dataset_info.json"""							)							)							)
               del dset





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




