






'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def  _a(  UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Dict									=[False] * len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Any									=[-1] * len(UpperCamelCase__							)

      def dfs(UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						str							):
            SCREAMING_SNAKE_CASE__     :				Tuple									=True
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=c
            for u in graph[v]:
                  if not visited[u]:
                        dfs(UpperCamelCase__,	1 - c							)

      for i in range(len(UpperCamelCase__							)							):
            if not visited[i]:
                  dfs(UpperCamelCase__,	0							)

      for i in range(len(UpperCamelCase__							)							):
            for j in graph[i]:
                  if color[i] == color[j]:
                        return False

      return True


# Adjacency list of graph
a_					= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}







print(check_bipartite_dfs(graph))







'''simple docstring'''
import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:






  class      __SCREAMING_SNAKE_CASE      :




   @staticmethod
   def      __magic_name__	(      *__lowercase	:							int					,      **__lowercase	:							Optional[Any]      )	->						Optional[Any]:
         pass






@is_pipeline_test
@require_vision
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @require_torch
 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
       # python and torch versions.
       self.assertIn(
           nested_simplify(__lowercase      )					,      [
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				int									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''					,      framework='''tf'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=image_classifier(__lowercase					,      candidate_labels=['''a''', '''b''', '''c''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''A''', '''B''', '''C''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
                   {'''score''': 0.333, '''label''': ANY(__lowercase      )},
               ],
           ]					,      )




 @slow
 @require_torch
 def      __magic_name__	(      self	:							List[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Any									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )

       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )




 @slow
 @require_tf
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						int:
       SCREAMING_SNAKE_CASE__     :				str									=pipeline(
           task='''zero-shot-image-classification'''					,      model='''openai/clip-vit-base-patch32'''					,      framework='''tf'''      )
       # This is an image of 2 cats with remotes and no planes
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=image_classifier(__lowercase					,      candidate_labels=['''cat''', '''plane''', '''remote''']      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]					,      )

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image_classifier([image] * 5					,      candidate_labels=['''cat''', '''plane''', '''remote''']					,      batch_size=2      )
       self.assertEqual(
           nested_simplify(__lowercase      )					,      [
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5					,      )