





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
		from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor






class  _UpperCAmelCase       (     lowercase_			):


					def      lowerCamelCase	( self      :int		):
												A		=  self.config_class(**self.inputs_dict		)
												self.parent.assertTrue(hasattr(__UpperCamelCase					,	"hidden_sizes"		)		)
												self.parent.assertTrue(hasattr(__UpperCamelCase					,	"num_attention_heads"		)		)
												self.parent.assertTrue(hasattr(__UpperCamelCase					,	"num_encoder_blocks"		)		)







class  _UpperCAmelCase       :


					def __init__( self      :Optional[int]					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :List[Any]=13					,	__UpperCamelCase      :List[str]=64					,	__UpperCamelCase      :str=3					,	__UpperCamelCase      :Tuple=4					,	__UpperCamelCase      :Optional[int]=[2, 2, 2, 2]					,	__UpperCamelCase      :List[str]=[8, 4, 2, 1]					,	__UpperCamelCase      :int=[16, 32, 64, 1_28]					,	__UpperCamelCase      :List[str]=[1, 4, 8, 16]					,	__UpperCamelCase      :Dict=[1, 2, 4, 8]					,	__UpperCamelCase      :List[str]=True					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :Dict="gelu"					,	__UpperCamelCase      :Optional[int]=0.1					,	__UpperCamelCase      :Union[str, Any]=0.1					,	__UpperCamelCase      :List[Any]=0.02					,	__UpperCamelCase      :int=16					,	__UpperCamelCase      :int=3					,	__UpperCamelCase      :Dict=None					,	):
												A		=  parent
												A		=  batch_size
												A		=  image_size
												A		=  num_channels
												A		=  num_encoder_blocks
												A		=  sr_ratios
												A		=  depths
												A		=  hidden_sizes
												A		=  downsampling_rates
												A		=  num_attention_heads
												A		=  is_training
												A		=  use_labels
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  decoder_hidden_size
												A		=  num_labels
												A		=  scope


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

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

												A		=  self.get_config()
												return config, pixel_values, labels


					def      lowerCamelCase	( self      :Union[str, Any]		):
												return GLPNConfig(
												    image_size=self.image_size					,	num_channels=self.num_channels					,	num_encoder_blocks=self.num_encoder_blocks					,	depths=self.depths					,	hidden_sizes=self.hidden_sizes					,	num_attention_heads=self.num_attention_heads					,	hidden_act=self.hidden_act					,	hidden_dropout_prob=self.hidden_dropout_prob					,	attention_probs_dropout_prob=self.attention_probs_dropout_prob					,	initializer_range=self.initializer_range					,	decoder_hidden_size=self.decoder_hidden_size					,	)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :List[Any]		):
												A		=  GLPNModel(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												A		=  A		=  self.image_size // (self.downsampling_rates[-1] * 2)
												self.parent.assertEqual(
												    result.last_hidden_state.shape					,	(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :Any		):
												A		=  self.num_labels
												A		=  GLPNForDepthEstimation(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.predicted_depth.shape					,	(self.batch_size, self.image_size, self.image_size)		)
												A		=  model(__UpperCamelCase					,	labels=__UpperCamelCase		)
												self.parent.assertEqual(result.predicted_depth.shape					,	(self.batch_size, self.image_size, self.image_size)		)


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







@require_torch
class  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
					UpperCamelCase								=							(
					    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
					)

					UpperCamelCase								=							False
					UpperCamelCase								=							False
					UpperCamelCase								=							False


					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  GLPNModelTester(self		)
												A		=  GLPNConfigTester(self					,	config_class=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Dict		):
												self.config_tester.run_common_tests()


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


					def      lowerCamelCase	( self      :Tuple		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase		)


					@unittest.skip("GLPN does not use inputs_embeds"		)
					def      lowerCamelCase	( self      :Tuple		):
												pass


					@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"		)
					def      lowerCamelCase	( self      :List[Any]		):
												pass


					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      :List[str]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												A		=  True

												for model_class in self.all_model_classes:
																			A		=  True
																			A		=  False
																			A		=  True
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()
																			with torch.no_grad():
																										A		=  model(**self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase		)		)
																			A		=  outputs.attentions

																			A		=  sum(self.model_tester.depths		)
																			self.assertEqual(len(__UpperCamelCase		)					,	__UpperCamelCase		)

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			A		=  True
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()
																			with torch.no_grad():
																										A		=  model(**self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase		)		)
																			A		=  outputs.attentions

																			self.assertEqual(len(__UpperCamelCase		)					,	__UpperCamelCase		)

																			# verify the first attentions (first block, first layer)
																			A		=  (self.model_tester.image_size // 4) ** 2
																			A		=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																			self.assertListEqual(
																			    list(attentions[0].shape[-3:]		)					,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,	)

																			# verify the last attentions (last block, last layer)
																			A		=  (self.model_tester.image_size // 32) ** 2
																			A		=  (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
																			self.assertListEqual(
																			    list(attentions[-1].shape[-3:]		)					,	[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,	)
																			A		=  len(__UpperCamelCase		)

																			# Check attention is always last and order is fine
																			A		=  True
																			A		=  True
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()
																			with torch.no_grad():
																										A		=  model(**self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase		)		)

																			self.assertEqual(out_len + 1					,	len(__UpperCamelCase		)		)

																			A		=  outputs.attentions

																			self.assertEqual(len(__UpperCamelCase		)					,	__UpperCamelCase		)
																			# verify the first attentions (first block, first layer)
																			A		=  (self.model_tester.image_size // 4) ** 2
																			A		=  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																			self.assertListEqual(
																			    list(self_attentions[0].shape[-3:]		)					,	[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,	)


					def      lowerCamelCase	( self      :List[str]		):
												def check_hidden_states_output(__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :int		):
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()

																			with torch.no_grad():
																										A		=  model(**self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase		)		)

																			A		=  outputs.hidden_states

																			A		=  self.model_tester.num_encoder_blocks
																			self.assertEqual(len(__UpperCamelCase		)					,	__UpperCamelCase		)

																			# verify the first hidden states (first block)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-3:]		)					,	[
																			        self.model_tester.hidden_sizes[0],
																			        self.model_tester.image_size // 4,
																			        self.model_tester.image_size // 4,
																			    ]					,	)

												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  True
																			check_hidden_states_output(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			A		=  True

																			check_hidden_states_output(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[int]		):
												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
																			# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
																			# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																			if model_class.__name__ == "GLPNForDepthEstimation":
																										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.to(__UpperCamelCase		)
																			model.train()
																			A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase					,	return_labels=__UpperCamelCase		)
																			A		=  model(**__UpperCamelCase		).loss
																			loss.backward()


					@slow
					def      lowerCamelCase	( self      :Any		):
												for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A		=  GLPNModel.from_pretrained(__UpperCamelCase		)
																			self.assertIsNotNone(__UpperCamelCase		)







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






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


					@slow
					def      lowerCamelCase	( self      :Any		):
												A		=  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
												A		=  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__UpperCamelCase		)

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

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

												# verify the predicted depth
												A		=  torch.Size([1, 4_80, 6_40]		)
												self.assertEqual(outputs.predicted_depth.shape					,	__UpperCamelCase		)

												A		=  torch.tensor(
												    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]		).to(__UpperCamelCase		)

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









"""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)


