





"""simple docstring"""
import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    MODEL_MAPPING,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		)
		from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor






class  _UpperCAmelCase       :


					def __init__( self      :Dict					,	__UpperCamelCase      :int					,	__UpperCamelCase      :List[Any]=1_00					,	__UpperCamelCase      :Any=13					,	__UpperCamelCase      :str=30					,	__UpperCamelCase      :str=2					,	__UpperCamelCase      :Optional[Any]=3					,	__UpperCamelCase      :Optional[Any]=True					,	__UpperCamelCase      :Optional[int]=True					,	__UpperCamelCase      :Optional[Any]=32					,	__UpperCamelCase      :str=4					,	__UpperCamelCase      :Any=4					,	__UpperCamelCase      :Dict=37					,	__UpperCamelCase      :Union[str, Any]="gelu"					,	__UpperCamelCase      :Union[str, Any]=0.1					,	__UpperCamelCase      :List[Any]=0.1					,	__UpperCamelCase      :Optional[Any]=10					,	__UpperCamelCase      :Tuple=0.02					,	__UpperCamelCase      :int=3					,	__UpperCamelCase      :Union[str, Any]=None					,	__UpperCamelCase      :Tuple=[0, 1, 2, 3]					,	):
												A		=  parent
												A		=  1_00
												A		=  batch_size
												A		=  image_size
												A		=  patch_size
												A		=  num_channels
												A		=  is_training
												A		=  use_labels
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  type_sequence_label_size
												A		=  initializer_range
												A		=  scope
												A		=  out_indices
												A		=  num_labels

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


					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

												A		=  None
												A		=  None
												if self.use_labels:
																			A		=  ids_tensor([self.batch_size]					,	self.type_sequence_label_size		)
																			A		=  ids_tensor([self.batch_size, self.image_size, self.image_size]					,	self.num_labels		)

												A		=  self.get_config()

												return config, pixel_values, labels, pixel_labels


					def      lowerCamelCase	( self      :Any		):
												return DataaVecVisionConfig(
												    vocab_size=self.vocab_size					,	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					,	out_indices=self.out_indices					,	)


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :Any		):
												A		=  DataaVecVisionModel(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
												A		=  (self.image_size // self.patch_size) ** 2
												self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, num_patches + 1, self.hidden_size)		)


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :Dict		):
												A		=  self.type_sequence_label_size
												A		=  DataaVecVisionForImageClassification(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase					,	labels=__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.type_sequence_label_size)		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :str		):
												A		=  self.num_labels
												A		=  DataaVecVisionForSemanticSegmentation(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(
												    result.logits.shape					,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)
												A		=  model(__UpperCamelCase					,	labels=__UpperCamelCase		)
												self.parent.assertEqual(
												    result.logits.shape					,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)		)


					def      lowerCamelCase	( self      :Dict		):
												A		=  self.prepare_config_and_inputs()
												A,     A,     A,     A		=  config_and_inputs
												A		=  {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							(
					    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					UpperCamelCase								=							(
					    {
					        '''feature-extraction''': DataaVecVisionModel,
					        '''image-classification''': DataaVecVisionForImageClassification,
					        '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False


					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  DataaVecVisionModelTester(self		)
												A		=  ConfigTester(
												    self					,	config_class=__UpperCamelCase					,	has_text_modality=__UpperCamelCase					,	hidden_size=37		)


					def      lowerCamelCase	( self      :List[Any]		):
												self.config_tester.run_common_tests()


					def      lowerCamelCase	( self      :List[Any]		):
												# Data2VecVision does not use inputs_embeds
												pass


					@require_torch_multi_gpu
					@unittest.skip(
					    reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"		)
					def      lowerCamelCase	( self      :Optional[int]		):
												pass


					def      lowerCamelCase	( self      :int		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			self.assertIsInstance(model.get_input_embeddings()					,	(nn.Module)		)
																			A		=  model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__UpperCamelCase					,	nn.Linear		)		)


					def      lowerCamelCase	( self      :Any		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

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

																			A		=  ["pixel_values"]
																			self.assertListEqual(arg_names[:1]					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :Any		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_segmentation(*__UpperCamelCase		)


					def      lowerCamelCase	( self      :str		):
												if not self.model_tester.is_training:
																			return

												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												A		=  True

												for model_class in self.all_model_classes:
																			if model_class in [*get_values(__UpperCamelCase		)]:
																										continue

																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.train()
																			A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase					,	return_labels=__UpperCamelCase		)
																			A		=  model(**__UpperCamelCase		).loss
																			loss.backward()


					def      lowerCamelCase	( self      :str		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																			return

												A		=  False
												A		=  True

												for model_class in self.all_model_classes:
																			if model_class in [*get_values(__UpperCamelCase		)] or not model_class.supports_gradient_checkpointing:
																										continue
																			# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
																			# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																			elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																										A,     A,     A,     A		=  inputs_dict["pixel_values"].shape
																										A		=  torch.zeros(
																										    [self.model_tester.batch_size, height, width]					,	device=__UpperCamelCase		).long()
																			A		=  model_class(__UpperCamelCase		)
																			model.gradient_checkpointing_enable()
																			model.to(__UpperCamelCase		)
																			model.train()
																			A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase					,	return_labels=__UpperCamelCase		)
																			A		=  model(**__UpperCamelCase		).loss
																			loss.backward()


					def      lowerCamelCase	( self      :Any		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												A		=  _config_zero_init(__UpperCamelCase		)
												for model_class in self.all_model_classes:
																			A		=  model_class(config=__UpperCamelCase		)
																			for name, param in model.named_parameters():
																										# we skip lambda parameters as these require special initial values
																										# determined by config.layer_scale_init_value
																										if "lambda" in name:
																																	continue
																										if param.requires_grad:
																																	self.assertIn(
																																	    ((param.data.mean() * 1e9).round() / 1e9).item()					,	[0.0, 1.0]					,	msg=f"Parameter {name} of model {model_class} seems not properly initialized"					,	)


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :int=2e-4					,	__UpperCamelCase      :Dict="outputs"					,	__UpperCamelCase      :Dict=None		):
												# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
												super().check_pt_tf_outputs(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase		)


					@slow
					def      lowerCamelCase	( self      :str		):
												for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  DataaVecVisionModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







def  A__    (      ):
							A		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
							return image






@require_torch
@require_vision
class  _UpperCAmelCase       (     unittest.TestCase			):


					@cached_property
					def      lowerCamelCase	( self      :Optional[Any]		):
												return (
												    BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"		) if is_vision_available() else None
												)


					@slow
					def      lowerCamelCase	( self      :str		):
												A		=  DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"		).to(
												    __UpperCamelCase		)

												A		=  self.default_image_processor
												A		=  prepare_img()
												A		=  image_processor(images=__UpperCamelCase					,	return_tensors="pt"		).to(__UpperCamelCase		)

												# forward pass
												with torch.no_grad():
																			A		=  model(**__UpperCamelCase		)
												A		=  outputs.logits

												# verify the logits
												A		=  torch.Size((1, 10_00)		)
												self.assertEqual(logits.shape					,	__UpperCamelCase		)

												A		=  torch.tensor([0.3_277, -0.1_395, 0.0_911]		).to(__UpperCamelCase		)

												self.assertTrue(torch.allclose(logits[0, :3]					,	__UpperCamelCase					,	atol=1e-4		)		)

												A		=  [model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
												self.assertEqual(logits[0].topk(2		).indices.cpu().tolist()					,	__UpperCamelCase		)









"""simple docstring"""
from __future__ import annotations






class  _UpperCAmelCase       :


					def __init__( self      :Optional[Any]					,	__UpperCamelCase      :int		):
												A		=  vertices
												A		=  [[0] * vertices for _ in range(__UpperCamelCase		)]


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :list[int]		):
												print("Vertex \t Distance from Source"		)
												for vertex in range(self.vertices		):
																			print(__UpperCamelCase					,	"\t\t"					,	distances_from_source[vertex]		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :list[int]					,	__UpperCamelCase      :list[bool]		):
												A		=  1e7
												A		=  0

												# Search not nearest vertex not in the shortest path tree
												for vertex in range(self.vertices		):
																			if distances_from_source[vertex] < minimum and visited[vertex] is False:
																										A		=  distances_from_source[vertex]
																										A		=  vertex
												return min_index


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :int		):
												A		=  [int(1e7		)] * self.vertices  # distances from the source
												A		=  0
												A		=  [False] * self.vertices

												for _ in range(self.vertices		):
																			A		=  self.minimum_distance(__UpperCamelCase					,	__UpperCamelCase		)
																			A		=  True

																			# Update dist value of the adjacent vertices
																			# of the picked vertex only if the current
																			# distance is greater than new distance and
																			# the vertex in not in the shortest path tree
																			for v in range(self.vertices		):
																										if (
																										    self.graph[u][v] > 0
																										    and visited[v] is False
																										    and distances[v] > distances[u] + self.graph[u][v]
																										):
																																	A		=  distances[u] + self.graph[u][v]

												self.print_solution(__UpperCamelCase		)


if __name__ == "__main__":
		_snake_case					:		List[Any]							=			Graph(9)
		_snake_case					:		List[str]							=			[
		    [0, 4, 0, 0, 0, 0, 0, 8, 0],
		    [4, 0, 8, 0, 0, 0, 0, 11, 0],
		    [0, 8, 0, 7, 0, 4, 0, 0, 2],
		    [0, 0, 7, 0, 9, 14, 0, 0, 0],
		    [0, 0, 0, 9, 0, 10, 0, 0, 0],
		    [0, 0, 4, 14, 10, 0, 2, 0, 0],
		    [0, 0, 0, 0, 0, 2, 0, 1, 6],
		    [8, 11, 0, 0, 0, 0, 1, 0, 7],
		    [0, 0, 2, 0, 0, 0, 6, 7, 0],
		]
		graph.dijkstra(0)


