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 			__snake_case  (      _lowerCamelCase     ):




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				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 			__snake_case  :




					def __init__(       self   ,	__UpperCamelCase   ,	__UpperCamelCase=13   ,	__UpperCamelCase=64   ,	__UpperCamelCase=3   ,	__UpperCamelCase=4   ,	__UpperCamelCase=[2, 2, 2, 2]   ,	__UpperCamelCase=[8, 4, 2, 1]   ,	__UpperCamelCase=[16, 32, 64, 128]   ,	__UpperCamelCase=[1, 4, 8, 16]   ,	__UpperCamelCase=[1, 2, 4, 8]   ,	__UpperCamelCase=True   ,	__UpperCamelCase=True   ,	__UpperCamelCase="gelu"   ,	__UpperCamelCase=0.1   ,	__UpperCamelCase=0.1   ,	__UpperCamelCase=0.0_2   ,	__UpperCamelCase=16   ,	__UpperCamelCase=3   ,	__UpperCamelCase=None   ,	)					->  str:



									'''simple docstring'''







									snake_case__ :     Union[str, Any] 							=				parent
									snake_case__ :     Dict 							=				batch_size
									snake_case__ :     int 							=				image_size
									snake_case__ :     str 							=				num_channels
									snake_case__ :     Dict 							=				num_encoder_blocks
									snake_case__ :     List[str] 							=				sr_ratios
									snake_case__ :     str 							=				depths
									snake_case__ :     str 							=				hidden_sizes
									snake_case__ :     Dict 							=				downsampling_rates
									snake_case__ :     str 							=				num_attention_heads
									snake_case__ :     List[str] 							=				is_training
									snake_case__ :     Optional[int] 							=				use_labels
									snake_case__ :     Any 							=				hidden_act
									snake_case__ :     int 							=				hidden_dropout_prob
									snake_case__ :     Tuple 							=				attention_probs_dropout_prob
									snake_case__ :     Dict 							=				initializer_range
									snake_case__ :     Tuple 							=				decoder_hidden_size
									snake_case__ :     Any 							=				num_labels
									snake_case__ :     Any 							=				scope




					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									snake_case__ :     Optional[Any] 							=				None
									if self.use_labels:
													snake_case__ :     Optional[int] 							=				ids_tensor([self.batch_size, self.image_size, self.image_size]   ,	self.num_labels		)

									snake_case__ :     List[str] 							=				self.get_config()
									return config, pixel_values, labels




					def       __a      (       self		)					->  str:



									'''simple docstring'''







									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       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				GLPNModel(config=__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									model.eval()
									snake_case__ :     Optional[Any] 							=				model(__UpperCamelCase		)
									snake_case__ :     Any 							=				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       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				self.num_labels
									snake_case__ :     Any 							=				GLPNForDepthEstimation(__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									model.eval()
									snake_case__ :     int 							=				model(__UpperCamelCase		)
									self.parent.assertEqual(result.predicted_depth.shape   ,	(self.batch_size, self.image_size, self.image_size)		)
									snake_case__ :     Dict 							=				model(__UpperCamelCase   ,	labels=__UpperCamelCase		)
									self.parent.assertEqual(result.predicted_depth.shape   ,	(self.batch_size, self.image_size, self.image_size)		)




					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     int 							=				self.prepare_config_and_inputs()
									snake_case__							,  snake_case__							,  snake_case__ :     Dict 							=				config_and_inputs
									snake_case__ :     Union[str, Any] 							=				{'pixel_values': pixel_values}
									return config, inputs_dict





@require_torch
class 			__snake_case  (      _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
					__lowerCamelCase	 =       (
					    {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
					)

					__lowerCamelCase	 =       False
					__lowerCamelCase	 =       False
					__lowerCamelCase	 =       False




					def       __a      (       self		)					->  str:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				GLPNModelTester(self		)
									snake_case__ :     Optional[int] 							=				GLPNConfigTester(self   ,	config_class=__UpperCamelCase		)




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									self.config_tester.run_common_tests()




					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     int 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__UpperCamelCase		)




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     str 							=				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       __a      (       self		)					->  Any:



									'''simple docstring'''







									pass




					@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'		)
					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									pass




					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     int 							=				self.model_tester.prepare_config_and_inputs_for_common()

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

													snake_case__ :     Dict 							=				['pixel_values']
													self.assertListEqual(arg_names[:1]   ,	__UpperCamelCase		)




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Any 							=				self.model_tester.prepare_config_and_inputs_for_common()
									snake_case__ :     List[Any] 							=				True

									for model_class in self.all_model_classes:
													snake_case__ :     int 							=				True
													snake_case__ :     Optional[Any] 							=				False
													snake_case__ :     int 							=				True
													snake_case__ :     Tuple 							=				model_class(__UpperCamelCase		)
													model.to(__UpperCamelCase		)
													model.eval()
													with torch.no_grad():
																	snake_case__ :     Union[str, Any] 							=				model(**self._prepare_for_class(__UpperCamelCase   ,	__UpperCamelCase		)		)
													snake_case__ :     List[Any] 							=				outputs.attentions

													snake_case__ :     Optional[Any] 							=				sum(self.model_tester.depths		)
													self.assertEqual(len(__UpperCamelCase		)   ,	__UpperCamelCase		)

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

													self.assertEqual(len(__UpperCamelCase		)   ,	__UpperCamelCase		)

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

													# Check attention is always last and order is fine
													snake_case__ :     Union[str, Any] 							=				True
													snake_case__ :     Optional[int] 							=				True
													snake_case__ :     List[str] 							=				model_class(__UpperCamelCase		)
													model.to(__UpperCamelCase		)
													model.eval()
													with torch.no_grad():
																	snake_case__ :     Tuple 							=				model(**self._prepare_for_class(__UpperCamelCase   ,	__UpperCamelCase		)		)

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

													snake_case__ :     Tuple 							=				outputs.attentions

													self.assertEqual(len(__UpperCamelCase		)   ,	__UpperCamelCase		)
													# verify the first attentions (first block, first layer)
													snake_case__ :     List[str] 							=				(self.model_tester.image_size // 4) ** 2
													snake_case__ :     str 							=				(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       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									def check_hidden_states_output(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		):
													snake_case__ :     List[Any] 							=				model_class(__UpperCamelCase		)
													model.to(__UpperCamelCase		)
													model.eval()

													with torch.no_grad():
																	snake_case__ :     List[str] 							=				model(**self._prepare_for_class(__UpperCamelCase   ,	__UpperCamelCase		)		)

													snake_case__ :     List[Any] 							=				outputs.hidden_states

													snake_case__ :     Any 							=				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,
													    ]   ,	)

									snake_case__							,  snake_case__ :     Tuple 							=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

													check_hidden_states_output(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)




					def       __a      (       self		)					->  str:



									'''simple docstring'''







									if not self.model_tester.is_training:
													return

									snake_case__							,  snake_case__ :     int 							=				self.model_tester.prepare_config_and_inputs_for_common()
									snake_case__ :     Optional[Any] 							=				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":
																	snake_case__							,  snake_case__							,  snake_case__							,  snake_case__ :     Optional[int] 							=				inputs_dict['pixel_values'].shape
																	snake_case__ :     Optional[int] 							=				torch.zeros(
																	    [self.model_tester.batch_size, height, width]   ,	device=__UpperCamelCase		).long()
													snake_case__ :     Dict 							=				model_class(__UpperCamelCase		)
													model.to(__UpperCamelCase		)
													model.train()
													snake_case__ :     List[Any] 							=				self._prepare_for_class(__UpperCamelCase   ,	__UpperCamelCase   ,	return_labels=__UpperCamelCase		)
													snake_case__ :     Union[str, Any] 							=				model(**__UpperCamelCase		).loss
													loss.backward()




					@slow
					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case__ :     List[str] 							=				GLPNModel.from_pretrained(__UpperCamelCase		)
													self.assertIsNotNone(__UpperCamelCase		)




def 			UpperCamelCase__						(    )       -> Optional[Any]:
				snake_case__ :     Any 							=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
				return image





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




					@slow
					def       __a      (       self		)					->  str:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
									snake_case__ :     int 							=				GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__UpperCamelCase		)

									snake_case__ :     Optional[Any] 							=				prepare_img()
									snake_case__ :     Any 							=				image_processor(images=__UpperCamelCase   ,	return_tensors='pt'		).to(__UpperCamelCase		)

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

									# verify the predicted depth
									snake_case__ :     Optional[int] 							=				torch.Size([1, 480, 640]		)
									self.assertEqual(outputs.predicted_depth.shape   ,	__UpperCamelCase		)

									snake_case__ :     List[str] 							=				torch.tensor(
									    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		).to(__UpperCamelCase		)

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



from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
lowerCAmelCase__					:       Dict		=       {
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}
def 			UpperCamelCase__						(    A__   )       -> set[tuple[str, str]]:
				snake_case__ :     Tuple 							=				{node: {node} for node in graph}

				snake_case__ :     Tuple 							=				{node: graph[node][:] for node in graph}

				while len(A__   ) > 2:
								# Choose a random edge.
								snake_case__ :     Union[str, Any] 							=				random.choice(list(graph_copy.keys()   )   )
								snake_case__ :     Any 							=				random.choice(graph_copy[u]   )

								# Contract edge (u, v) to new node uv
								snake_case__ :     Tuple 							=				u + v
								snake_case__ :     Dict 							=				list(set(graph_copy[u] + graph_copy[v]   )   )
								uv_neighbors.remove(A__   )
								uv_neighbors.remove(A__   )
								snake_case__ :     Optional[Any] 							=				uv_neighbors
								for neighbor in uv_neighbors:
												graph_copy[neighbor].append(A__   )

								snake_case__ :     Tuple 							=				set(contracted_nodes[u].union(contracted_nodes[v]   )   )

								# Remove nodes u and v.
								del graph_copy[u]
								del graph_copy[v]
								for neighbor in uv_neighbors:
												if u in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )
												if v in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )

    # Find cutset.
				snake_case__ :     Tuple 							=				[contracted_nodes[node] for node in graph_copy]
				return {
				    (node, neighbor)
				    for node in groups[0]
				    for neighbor in graph[node]
				    if neighbor in groups[1]
				}


if __name__ == "__main__":
				print(partition_graph(TEST_GRAPH))


