

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 						__lowercase       (				lowerCAmelCase_			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				Tuple							= self.config_class(**self.inputs_dict)
									self.parent.assertTrue(hasattr(__a     ,		"hidden_sizes"))
									self.parent.assertTrue(hasattr(__a     ,		"num_attention_heads"))
									self.parent.assertTrue(hasattr(__a     ,		"num_encoder_blocks"))







class 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  Optional[Any]     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Dict=13     ,		lowerCAmelCase__						:  Dict=64     ,		lowerCAmelCase__						:  Any=3     ,		lowerCAmelCase__						:  List[str]=4     ,		lowerCAmelCase__						:  Any=[2, 2, 2, 2]     ,		lowerCAmelCase__						:  List[str]=[8, 4, 2, 1]     ,		lowerCAmelCase__						:  List[Any]=[16, 32, 64, 128]     ,		lowerCAmelCase__						:  Optional[Any]=[1, 4, 8, 16]     ,		lowerCAmelCase__						:  Optional[Any]=[1, 2, 4, 8]     ,		lowerCAmelCase__						:  Union[str, Any]=True     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  Optional[int]="gelu"     ,		lowerCAmelCase__						:  Optional[int]=0.1     ,		lowerCAmelCase__						:  Union[str, Any]=0.1     ,		lowerCAmelCase__						:  Dict=0.02     ,		lowerCAmelCase__						:  Optional[int]=16     ,		lowerCAmelCase__						:  List[Any]=3     ,		lowerCAmelCase__						:  int=None     ,		):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= parent
									SCREAMING_SNAKE_CASE_:				List[Any]							= batch_size
									SCREAMING_SNAKE_CASE_:				List[str]							= image_size
									SCREAMING_SNAKE_CASE_:				Any							= num_channels
									SCREAMING_SNAKE_CASE_:				str							= num_encoder_blocks
									SCREAMING_SNAKE_CASE_:				Optional[int]							= sr_ratios
									SCREAMING_SNAKE_CASE_:				Any							= depths
									SCREAMING_SNAKE_CASE_:				Dict							= hidden_sizes
									SCREAMING_SNAKE_CASE_:				List[Any]							= downsampling_rates
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= num_attention_heads
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= is_training
									SCREAMING_SNAKE_CASE_:				List[str]							= use_labels
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= hidden_act
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_:				List[Any]							= initializer_range
									SCREAMING_SNAKE_CASE_:				Any							= decoder_hidden_size
									SCREAMING_SNAKE_CASE_:				Dict							= num_labels
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= scope



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

									SCREAMING_SNAKE_CASE_:				int							= self.get_config()
									return config, pixel_values, labels



			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									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  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= GLPNModel(config=__a)
									model.to(__a)
									model.eval()
									SCREAMING_SNAKE_CASE_:				Tuple							= model(__a)
									SCREAMING_SNAKE_CASE_:				Optional[int]							= 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  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  Union[str, Any]     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.num_labels
									SCREAMING_SNAKE_CASE_:				List[Any]							= GLPNForDepthEstimation(__a)
									model.to(__a)
									model.eval()
									SCREAMING_SNAKE_CASE_:				List[str]							= model(__a)
									self.parent.assertEqual(result.predicted_depth.shape     ,		(self.batch_size, self.image_size, self.image_size))
									SCREAMING_SNAKE_CASE_:				List[Any]							= model(__a     ,		labels=__a)
									self.parent.assertEqual(result.predicted_depth.shape     ,		(self.batch_size, self.image_size, self.image_size))




			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_:				Tuple							= config_and_inputs
									SCREAMING_SNAKE_CASE_:				List[str]							= {'pixel_values': pixel_values}
									return config, inputs_dict







@require_torch
class 						__lowercase       (				lowerCAmelCase_	, lowerCAmelCase_	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							Optional[int]    							=  (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
			_UpperCAmelCase     :							Tuple    							=  (
			    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
			)

			_UpperCAmelCase     :							Any    							=  False
			_UpperCAmelCase     :							List[str]    							=  False
			_UpperCAmelCase     :							Tuple    							=  False



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= GLPNModelTester(self)
									SCREAMING_SNAKE_CASE_:				Tuple							= GLPNConfigTester(self     ,		config_class=__a)



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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__a)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									SCREAMING_SNAKE_CASE_:				Tuple							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*__a)



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



			@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods")
			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									pass



			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				str							= self.model_tester.prepare_config_and_inputs_for_common()

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

															SCREAMING_SNAKE_CASE_:				Tuple							= ['pixel_values']
															self.assertListEqual(arg_names[:1]     ,		__a)



			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									SCREAMING_SNAKE_CASE_:				List[Any]							= self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_:				Dict							= True

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_:				Dict							= True
															SCREAMING_SNAKE_CASE_:				Tuple							= False
															SCREAMING_SNAKE_CASE_:				Tuple							= True
															SCREAMING_SNAKE_CASE_:				Dict							= model_class(__a)
															model.to(__a)
															model.eval()
															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_:				str							= model(**self._prepare_for_class(__a     ,		__a))
															SCREAMING_SNAKE_CASE_:				Dict							= outputs.attentions

															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= sum(self.model_tester.depths)
															self.assertEqual(len(__a)     ,		__a)

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

															self.assertEqual(len(__a)     ,		__a)

															# verify the first attentions (first block, first layer)
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= (self.model_tester.image_size // 4) ** 2
															SCREAMING_SNAKE_CASE_:				int							= (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)
															SCREAMING_SNAKE_CASE_:				int							= (self.model_tester.image_size // 32) ** 2
															SCREAMING_SNAKE_CASE_:				List[str]							= (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]     ,		)
															SCREAMING_SNAKE_CASE_:				Dict							= len(__a)

															# Check attention is always last and order is fine
															SCREAMING_SNAKE_CASE_:				Tuple							= True
															SCREAMING_SNAKE_CASE_:				Dict							= True
															SCREAMING_SNAKE_CASE_:				int							= model_class(__a)
															model.to(__a)
															model.eval()
															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_:				int							= model(**self._prepare_for_class(__a     ,		__a))

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

															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= outputs.attentions

															self.assertEqual(len(__a)     ,		__a)
															# verify the first attentions (first block, first layer)
															SCREAMING_SNAKE_CASE_:				int							= (self.model_tester.image_size // 4) ** 2
															SCREAMING_SNAKE_CASE_:				Tuple							= (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  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):





									def check_hidden_states_output(lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Tuple):
															SCREAMING_SNAKE_CASE_:				Tuple							= model_class(__a)
															model.to(__a)
															model.eval()

															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_:				int							= model(**self._prepare_for_class(__a     ,		__a))

															SCREAMING_SNAKE_CASE_:				Dict							= outputs.hidden_states

															SCREAMING_SNAKE_CASE_:				Any							= self.model_tester.num_encoder_blocks
															self.assertEqual(len(__a)     ,		__a)

															# 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,
															    ]     ,		)

									SCREAMING_SNAKE_CASE_:				Dict							= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= True
															check_hidden_states_output(__a     ,		__a     ,		__a)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= True

															check_hidden_states_output(__a     ,		__a     ,		__a)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):





									if not self.model_tester.is_training:
															return

									SCREAMING_SNAKE_CASE_:				Tuple							= self.model_tester.prepare_config_and_inputs_for_common()
									SCREAMING_SNAKE_CASE_:				str							= True

									for model_class in self.all_model_classes:
															if model_class in get_values(__a):
																					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":
																					SCREAMING_SNAKE_CASE_:				Any							= inputs_dict['pixel_values'].shape
																					SCREAMING_SNAKE_CASE_:				Dict							= torch.zeros(
																					    [self.model_tester.batch_size, height, width]     ,		device=__a).long()
															SCREAMING_SNAKE_CASE_:				int							= model_class(__a)
															model.to(__a)
															model.train()
															SCREAMING_SNAKE_CASE_:				Any							= self._prepare_for_class(__a     ,		__a     ,		return_labels=__a)
															SCREAMING_SNAKE_CASE_:				str							= model(**__a).loss
															loss.backward()




			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  int):





									for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_:				str							= GLPNModel.from_pretrained(__a)
															self.assertIsNotNone(__a)







def     A_   (					):

						'''simple docstring'''





						SCREAMING_SNAKE_CASE_:				str							= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
						return image



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




			"""simple docstring"""







			@slow
			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]):
									SCREAMING_SNAKE_CASE_:				List[str]							= GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
									SCREAMING_SNAKE_CASE_:				Optional[int]							= GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__a)

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= prepare_img()
									SCREAMING_SNAKE_CASE_:				List[str]							= image_processor(images=__a     ,		return_tensors="pt").to(__a)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= model(**__a)

									# verify the predicted depth
									SCREAMING_SNAKE_CASE_:				List[str]							= torch.Size([1, 480, 640])
									self.assertEqual(outputs.predicted_depth.shape     ,		__a)

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= torch.tensor(
									    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]).to(__a)

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









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






