






'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor

class _lowercase	:




							def __init__(							self:				Optional[int]			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Any=13			,							UpperCamelCase__:				List[str]=30			,							UpperCamelCase__:				Optional[Any]=2			,							UpperCamelCase__:				List[Any]=3			,							UpperCamelCase__:				int=True			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				Union[str, Any]=32			,							UpperCamelCase__:				Tuple=2			,							UpperCamelCase__:				List[Any]=4			,							UpperCamelCase__:				str=37			,							UpperCamelCase__:				str="gelu"			,							UpperCamelCase__:				Tuple=0.1			,							UpperCamelCase__:				Optional[Any]=0.1			,							UpperCamelCase__:				Optional[int]=10			,							UpperCamelCase__:				List[Any]=0.02			,							UpperCamelCase__:				Dict=3			,							UpperCamelCase__:				Optional[int]=None			,							):
														lowerCamelCase__					:	List[Any]         =       parent
														lowerCamelCase__					:	str         =       batch_size
														lowerCamelCase__					:	Tuple         =       image_size
														lowerCamelCase__					:	Tuple         =       patch_size
														lowerCamelCase__					:	Dict         =       num_channels
														lowerCamelCase__					:	Optional[int]         =       is_training
														lowerCamelCase__					:	Optional[int]         =       use_labels
														lowerCamelCase__					:	Optional[int]         =       hidden_size
														lowerCamelCase__					:	Union[str, Any]         =       num_hidden_layers
														lowerCamelCase__					:	int         =       num_attention_heads
														lowerCamelCase__					:	Union[str, Any]         =       intermediate_size
														lowerCamelCase__					:	Union[str, Any]         =       hidden_act
														lowerCamelCase__					:	Union[str, Any]         =       hidden_dropout_prob
														lowerCamelCase__					:	Optional[int]         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Dict         =       type_sequence_label_size
														lowerCamelCase__					:	int         =       initializer_range
														lowerCamelCase__					:	Optional[Any]         =       scope

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




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	List[str]         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	int         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Dict         =       ids_tensor([self.batch_size]			,							self.type_sequence_label_size     )

														lowerCamelCase__					:	Optional[int]         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														return 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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							)




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Dict     ):
														lowerCamelCase__					:	str         =       TFViTModel(config=UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       model(UpperCamelCase__			,							training=UpperCamelCase__     )
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)     )

														# Test with an image with different size than the one specified in config.
														lowerCamelCase__					:	Tuple         =       self.image_size // 2
														lowerCamelCase__					:	Dict         =       pixel_values[:, :, :image_size, :image_size]
														lowerCamelCase__					:	Dict         =       model(UpperCamelCase__			,							interpolate_pos_encoding=UpperCamelCase__			,							training=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       (image_size // self.patch_size) ** 2 + 1
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, seq_length, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Optional[Any]			,							UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	List[str]         =       self.type_sequence_label_size
														lowerCamelCase__					:	Optional[Any]         =       TFViTForImageClassification(UpperCamelCase__     )
														lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__			,							labels=UpperCamelCase__			,							training=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )

														# Test with an image with different size than the one specified in config.
														lowerCamelCase__					:	str         =       self.image_size // 2
														lowerCamelCase__					:	str         =       pixel_values[:, :, :image_size, :image_size]
														lowerCamelCase__					:	str         =       model(UpperCamelCase__			,							interpolate_pos_encoding=UpperCamelCase__			,							training=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )

														# test greyscale images
														lowerCamelCase__					:	Optional[Any]         =       1
														lowerCamelCase__					:	Tuple         =       TFViTForImageClassification(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	Optional[int]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	str         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       config_and_inputs
														lowerCamelCase__					:	List[str]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
							a				      =       (
							    {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	List[str]         =       TFViTModelTester(self     )
														lowerCamelCase__					:	Any         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViT does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				int     ):
														pass




							@unittest.skip(reason="""ViT does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[Any]         =       model_class(UpperCamelCase__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(tf.keras.layers.Layer)     )
																					lowerCamelCase__					:	Any         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__			,							tf.keras.layers.Layer     )     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	List[str]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Any         =       inspect.signature(model.call     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	str         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	List[str]         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Dict         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														lowerCamelCase__					:	Optional[int]         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	List[str]         =       TFViTModel.from_pretrained("""google/vit-base-patch16-224"""     )
														self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Optional[Any]:
							lowerCamelCase__					:	List[str]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_tf
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				Any     ):
														return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""     ) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	str         =       TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""     )

														lowerCamelCase__					:	str         =       self.default_image_processor
														lowerCamelCase__					:	Optional[Any]         =       prepare_img()
														lowerCamelCase__					:	Optional[int]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""tf"""     )

														# forward pass
														lowerCamelCase__					:	Union[str, Any]         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	int         =       tf.TensorShape((1, 1_000)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       tf.constant([-0.2_744, 0.8_215, -0.0_836]     )

														tf.debugging.assert_near(outputs.logits[0, :3]			,							UpperCamelCase__			,							atol=1e-4     )









'''simple docstring'''







from __future__ import annotations

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

class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				dict[str, list[str]]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	str         =       graph
														# mapping node to its parent in resulting breadth first tree
														lowerCamelCase__					:	dict[str, str | None]         =       {}
														lowerCamelCase__					:	Any         =       source_vertex




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       {self.source_vertex}
														lowerCamelCase__					:	List[str]         =       None
														lowerCamelCase__					:	Tuple         =       [self.source_vertex]  # first in first out queue

														while queue:
																					lowerCamelCase__					:	Tuple         =       queue.pop(0     )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(UpperCamelCase__     )
																																			lowerCamelCase__					:	List[str]         =       vertex
																																			queue.append(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):
														if target_vertex == self.source_vertex:
																					return self.source_vertex

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

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


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

