


from __future__ import annotations

A       :		List[str]    =   {
    '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    :						str      ,					_UpperCAmelCase    :						dict[str, list[str]]      ,					_UpperCAmelCase    :						str		)						->   Union[str, Any]:




							"""simple docstring"""


							lowercase__     			=		graph
							# mapping node to its parent in resulting breadth first tree
							lowercase__     			=		{}
							lowercase__     			=		source_vertex

					def   lowerCamelCase__     (self    :						Dict		)						->   Optional[Any]:




							"""simple docstring"""


							lowercase__     			=		{self.source_vertex}
							lowercase__     			=		None
							lowercase__     			=		[self.source_vertex]  # first in first out queue

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


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




							"""simple docstring"""


							if target_vertex == self.source_vertex:
									return self.source_vertex

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

							return self.shortest_path(__a		) + f'''->{target_vertex}'''


if __name__ == "__main__":
	A       :		List[Any]    =   Graph(graph, 'G')
	g.breath_first_search()
	print(g.shortest_path('D'))
	print(g.shortest_path('G'))
	print(g.shortest_path('Foo'))


'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class        __A      ( unittest.TestCase	):



    def __init__(self    :    str   ,			__a    :    Optional[Any]   ,			__a    :    Optional[Any]=13   ,			__a    :    int=30   ,			__a    :    Union[str, Any]=2   ,			__a    :    Dict=3   ,			__a    :    List[Any]=True   ,			__a    :    Optional[Any]=True   ,			__a    :    List[Any]=32   ,			__a    :    Any=5   ,			__a    :    str=4   ,			__a    :    Optional[int]=37   ,			__a    :    Optional[int]="gelu"   ,			__a    :    List[str]=0.1   ,			__a    :    Tuple=0.1   ,			__a    :    List[str]=10   ,			__a    :    Optional[int]=0.02   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   patch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (image_size // patch_size) ** 2
         UpperCAmelCase_           =   num_patches + 1



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase_           =   ViTConfig(
             image_size=self.image_size   ,			patch_size=self.patch_size   ,			num_channels=self.num_channels   ,			hidden_size=self.hidden_size   ,			num_hidden_layers=self.num_hidden_layers   ,			num_attention_heads=self.num_attention_heads   ,			intermediate_size=self.intermediate_size   ,			hidden_act=self.hidden_act   ,			hidden_dropout_prob=self.hidden_dropout_prob   ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,			is_decoder=__a   ,			initializer_range=self.initializer_range   ,			)

         return config, pixel_values



    def 					_lowercase						(self    :    Dict   ,			__a    :    Any   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   FlaxViTModel(config=__a )
         UpperCAmelCase_           =   model(__a )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (self.image_size, self.image_size)
         UpperCAmelCase_           =   (self.patch_size, self.patch_size)
         UpperCAmelCase_           =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, num_patches + 1, self.hidden_size) )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    str   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.type_sequence_label_size
         UpperCAmelCase_           =   FlaxViTForImageClassification(config=__a )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   FlaxViTForImageClassification(__a )

         UpperCAmelCase_           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         UpperCAmelCase_           =   model(__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Tuple          =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   FlaxViTModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			has_text_modality=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    Tuple ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(__a )
              UpperCAmelCase_           =   inspect.signature(model.__call__ )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_           =   [*signature.parameters.keys()]

              UpperCAmelCase_           =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]   ,			__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                   UpperCAmelCase_           =   self._prepare_for_class(__a   ,			__a )
                   UpperCAmelCase_           =   model_class(__a )

                   @jax.jit
                   def model_jitted(__a    :    Tuple   ,			**__a    :    List[Any] ):
                        return model(pixel_values=__a   ,			**__a )

                   with self.subTest("JIT Enabled" ):
                        UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   with self.subTest("JIT Disabled" ):
                        with jax.disable_jit():
                             UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   self.assertEqual(len(__a )   ,			len(__a ) )
                   for jitted_output, output in zip(__a   ,			__a ):
                        self.assertEqual(jitted_output.shape   ,			output.shape )






    @slow
    def 					_lowercase						(self    :    Tuple ):
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_           =   model_class_name.from_pretrained("google/vit-base-patch16-224" )
              UpperCAmelCase_           =   model(np.ones((1, 3, 224, 224) ) )
              self.assertIsNotNone(__a )




