


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      ):
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        return datasets.DatasetInfo(
            features=datasets.Features({'content': datasets.Value('string' )} )				, supervised_keys=lowerCAmelCase__				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={'examples': get_test_dummy_examples()} )]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        import apache_beam as beam

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





class   __a			(    datasets.BeamBasedBuilder      ):
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return datasets.DatasetInfo(
            features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string' )} )} )				, supervised_keys=lowerCAmelCase__				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Tuple:




        '''simple docstring'''
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN				, gen_kwargs={'examples': get_test_nested_examples()} )
        ]
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        import apache_beam as beam

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





def    snake_case_     (  )  ->				Tuple:
       return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']     )]




def    snake_case_     (  )  ->				Dict:
       return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']     )]





class   __a			(    __UpperCamelCase      ):
 @require_beam
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   List[str]  			=   len(get_test_dummy_examples() )
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               lowercase__:   str  			=   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' )} ) )
               lowercase__:   Any  			=   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 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        import apache_beam as beam

        lowercase__:   str  			=   beam.io.parquetio.WriteToParquet

        lowercase__:   str  			=   len(get_test_dummy_examples() )
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               lowercase__:   Dict  			=   DummyBeamDataset(cache_dir=lowerCAmelCase__				, beam_runner='DirectRunner' )
               with patch('apache_beam.io.parquetio.WriteToParquet' ) as write_parquet_mock:
                      lowercase__:   Any  			=   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' )} ) )
               lowercase__:   Any  			=   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 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               lowercase__:   Optional[int]  			=   DummyBeamDataset(cache_dir=lowerCAmelCase__ )
               self.assertRaises(datasets.builder.MissingBeamOptions				, builder.download_and_prepare )
 @require_beam
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   str  			=   len(get_test_nested_examples() )
        with tempfile.TemporaryDirectory() as tmp_cache_dir:
               lowercase__:   Union[str, Any]  			=   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' )} )} ) )
               lowercase__:   List[Any]  			=   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






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




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				list[str]:
       lowercase__:   int  			=   set()
       # keep track of all the paths to be checked
       lowercase__:   Optional[int]  			=   [[start]]

       # return path if start is goal
       if start == goal:
              return [start]

       # keeps looping until all possible paths have been checked
       while queue:
              # pop the first path from the queue
              lowercase__:   List[Any]  			=   queue.pop(0     )
              # get the last node from the path
              lowercase__:   Optional[int]  			=   path[-1]
              if node not in explored:
                     lowercase__:   Optional[Any]  			=   graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            lowercase__:   Tuple  			=   list(snake_case     )
                            new_path.append(snake_case     )
                            queue.append(snake_case     )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(snake_case     )

    # in case there's no path between the 2 nodes
       return []




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       lowercase__:   Tuple  			=   [start]
       lowercase__:   List[Any]  			=   set(snake_case     )
       # Keep tab on distances from `start` node.
       lowercase__:   Tuple  			=   {start: 0, target: -1}
       while queue:
              lowercase__:   Dict  			=   queue.pop(0     )
              if node == target:
                     lowercase__:   str  			=   (
                         dist[node] if dist[target] == -1 else min(dist[target]			,							dist[node]     )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(snake_case     )
                            queue.append(snake_case     )
                            lowercase__:   List[str]  			=   dist[node] + 1
       return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4


