

'''simple docstring'''


def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase	)     ->     List[Any]:
	print('\nVertex Distance'	)
	for i in range(UpperCamelCase	):
		if dist[i] != float('inf'	):
			print(UpperCamelCase ,'\t' ,int(dist[i]	) ,end='\t'	)
		else:
			print(UpperCamelCase ,'\t' ,'INF' ,end='\t'	)
		print()







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Any:
	UpperCAmelCase_		:   Optional[Any]													=       float('inf'	)
	UpperCAmelCase_		:   str													=       -1
	for i in range(UpperCamelCase	):
		if (not vset[i]) and mdist[i] < min_val:
			UpperCAmelCase_		:   Tuple													=       i
			UpperCAmelCase_		:   Optional[int]													=       mdist[i]
	return min_ind







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     int:
	UpperCAmelCase_		:   str													=       [float('inf'	) for _ in range(UpperCamelCase	)]
	UpperCAmelCase_		:   str													=       [False for _ in range(UpperCamelCase	)]
	UpperCAmelCase_		:   int													=       0.0

	for _ in range(v - 1	):
		UpperCAmelCase_		:   Tuple													=       min_dist(UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)
		UpperCAmelCase_		:   Union[str, Any]													=       True

		for i in range(UpperCamelCase	):
			if (
			    (not vset[i])
			    and graph[u][i] != float('inf'	)
			    and mdist[u] + graph[u][i] < mdist[i]
			):
				UpperCAmelCase_		:   Optional[int]													=       mdist[u] + graph[u][i]

	print_dist(UpperCamelCase ,UpperCamelCase	)


if __name__ == "__main__":
						lowerCAmelCase__      				=			int(input("Enter number of vertices: ").strip())
						lowerCAmelCase__      				=			int(input("Enter number of edges: ").strip())

						lowerCAmelCase__      				=			[[float("inf") for i in range(V)] for j in range(V)]

						for i in range(V):
												lowerCAmelCase__      				=			0.0

						for i in range(E):
												print("\nEdge ", i + 1)
												lowerCAmelCase__      				=			int(input("Enter source:").strip())
												lowerCAmelCase__      				=			int(input("Enter destination:").strip())
												lowerCAmelCase__      				=			float(input("Enter weight:").strip())
												lowerCAmelCase__      				=			weight

						lowerCAmelCase__      				=			int(input("\nEnter shortest path source:").strip())
						dijkstra(graph, V, gsrc)









'''simple docstring'''


import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class        lowercase    (	unittest.TestCase			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=3     ,      _snake_case=32     ,      _snake_case=3     ,      _snake_case=10     ,      _snake_case=[10, 20, 30, 40]     ,      _snake_case=[1, 1, 2, 1]     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case="relu"     ,      _snake_case=3     ,      _snake_case=None     ,      )				->					Optional[Any]:
								UpperCAmelCase_		:   List[Any]													=       parent
								UpperCAmelCase_		:   Union[str, Any]													=       batch_size
								UpperCAmelCase_		:   List[Any]													=       image_size
								UpperCAmelCase_		:   List[Any]													=       num_channels
								UpperCAmelCase_		:   Dict													=       embeddings_size
								UpperCAmelCase_		:   str													=       hidden_sizes
								UpperCAmelCase_		:   List[Any]													=       depths
								UpperCAmelCase_		:   Optional[Any]													=       is_training
								UpperCAmelCase_		:   str													=       use_labels
								UpperCAmelCase_		:   List[str]													=       hidden_act
								UpperCAmelCase_		:   Dict													=       num_labels
								UpperCAmelCase_		:   List[Any]													=       scope
								UpperCAmelCase_		:   Tuple													=       len(_snake_case)


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   int													=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

								return config, pixel_values


							def     _snake_case				(      self)				->					Union[str, Any]:
								return ResNetConfig(
								    num_channels=self.num_channels     ,      embeddings_size=self.embeddings_size     ,      hidden_sizes=self.hidden_sizes     ,      depths=self.depths     ,      hidden_act=self.hidden_act     ,      num_labels=self.num_labels     ,      image_size=self.image_size     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					str:
								UpperCAmelCase_		:   Any													=       FlaxResNetModel(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Union[str, Any]													=       self.num_labels
								UpperCAmelCase_		:   Optional[Any]													=       FlaxResNetForImageClassification(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels))





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.prepare_config_and_inputs()
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       config_and_inputs
								UpperCAmelCase_		:   str													=       {'pixel_values': pixel_values}
								return config, inputs_dict



@require_flax
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   Dict= (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							_lowerCamelCase		:   Any= False
							_lowerCamelCase		:   List[Any]= False
							_lowerCamelCase		:   str= False


							def     _snake_case				(      self)				->					None:
								UpperCAmelCase_		:   Any													=       FlaxResNetModelTester(self)
								UpperCAmelCase_		:   Optional[int]													=       ConfigTester(self     ,      config_class=_snake_case     ,      has_text_modality=_snake_case)


							def     _snake_case				(      self)				->					Tuple:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()


							def     _snake_case				(      self)				->					int:
								return


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Optional[Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   Union[str, Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case)


							@unittest.skip(reason='ResNet does not use inputs_embeds')
							def     _snake_case				(      self)				->					Optional[Any]:
								pass


							@unittest.skip(reason='ResNet does not support input and output embeddings')
							def     _snake_case				(      self)				->					Union[str, Any]:
								pass


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCAmelCase_		:   List[str]													=       ['pixel_values']
									self.assertListEqual(arg_names[:1]     ,      _snake_case)


							def     _snake_case				(      self)				->					List[Any]:
								def check_hidden_states_output(_snake_case     ,      _snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Union[str, Any]													=       model_class(_snake_case)

									UpperCAmelCase_		:   Any													=       model(**self._prepare_for_class(_snake_case     ,      _snake_case))

									UpperCAmelCase_		:   List[Any]													=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_		:   int													=       self.model_tester.num_stages
									self.assertEqual(len(_snake_case)     ,      expected_num_stages + 1)


							@unittest.skip(reason='ResNet does not use feedforward chunking')
							def     _snake_case				(      self)				->					List[Any]:
								pass





							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Any													=       self.model_tester.prepare_config_and_inputs_for_common()

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

										@jax.jit
										def model_jitted(_snake_case     ,      **_snake_case):
											return model(pixel_values=_snake_case     ,      **_snake_case)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:   Optional[Any]													=       model_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:   Union[str, Any]													=       model_jitted(**_snake_case).to_tuple()

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







def     SCREAMING_SNAKE_CASE(	)     ->     Dict:
	UpperCAmelCase_		:   Tuple													=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







@require_flax
class        lowercase    (	unittest.TestCase			):


							@cached_property
							def     _snake_case				(      self)				->					List[str]:
								return AutoImageProcessor.from_pretrained('microsoft/resnet-50') if is_vision_available() else None





							@slow
							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   Union[str, Any]													=       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50')

								UpperCAmelCase_		:   List[str]													=       self.default_image_processor
								UpperCAmelCase_		:   Union[str, Any]													=       prepare_img()
								UpperCAmelCase_		:   int													=       image_processor(images=_snake_case     ,      return_tensors='np')

								UpperCAmelCase_		:   Any													=       model(**_snake_case)

								# verify the logits
								UpperCAmelCase_		:   Optional[int]													=       (1, 1000)
								self.assertEqual(outputs.logits.shape     ,      _snake_case)

								UpperCAmelCase_		:   Tuple													=       jnp.array([-11.1_069, -9.7_877, -8.3_777])

								self.assertTrue(jnp.allclose(outputs.logits[0, :3]     ,      _snake_case     ,      atol=1e-4))






