import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _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, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
		from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DPTImageProcessor




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=[0, 1, 2, 3]      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=37      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=[1, 3_84, 24, 24]      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   )     ->					Any:

							'''simple docstring'''


							__lowercase 			=							parent
							__lowercase 			=							batch_size
							__lowercase 			=							image_size
							__lowercase 			=							patch_size
							__lowercase 			=							num_channels
							__lowercase 			=							is_training
							__lowercase 			=							use_labels
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							backbone_out_indices
							__lowercase 			=							num_attention_heads
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							initializer_range
							__lowercase 			=							num_labels
							__lowercase 			=							backbone_featmap_shape
							__lowercase 			=							scope
							__lowercase 			=							is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							__lowercase 			=							(image_size // patch_size) ** 2
							__lowercase 			=							num_patches + 1





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

							'''simple docstring'''


							__lowercase 			=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

							__lowercase 			=							self.get_config()

							return config, pixel_values, labels





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

							'''simple docstring'''


							__lowercase 			=							{
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
							    '''num_groups''': 2,
							}

							return DPTConfig(
							    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      ,   backbone_out_indices=self.backbone_out_indices      ,   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=lowerCAmelCase__      ,   initializer_range=self.initializer_range      ,   is_hybrid=self.is_hybrid      ,   backbone_config=lowerCAmelCase__      ,   backbone_featmap_shape=self.backbone_featmap_shape      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							DPTModel(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__						)
							self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							self.num_labels
							__lowercase 			=							DPTForDepthEstimation(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__						)
							self.parent.assertEqual(result.predicted_depth.shape      ,   (self.batch_size, self.image_size, self.image_size)						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.num_labels
							__lowercase 			=							DPTForSemanticSegmentation(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase 			=							model(lowerCAmelCase__      ,   labels=lowerCAmelCase__						)
							self.parent.assertEqual(
							    result.logits.shape      ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)						)





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

							'''simple docstring'''


							__lowercase 			=							self.prepare_config_and_inputs()
							__lowercase			,     __lowercase			,     __lowercase 			=							config_and_inputs
							__lowercase 			=							{'''pixel_values''': pixel_values}
							return config, inputs_dict




@require_torch
class    _UpperCamelCase      (						_UpperCAmelCase						,_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : Dict			 =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
			__a  : str			 =						(
			    {
			        '''depth-estimation''': DPTForDepthEstimation,
			        '''feature-extraction''': DPTModel,
			        '''image-segmentation''': DPTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__a  : str			 =						False
			__a  : str			 =						False
			__a  : str			 =						False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							DPTModelTester(self						)
							__lowercase 			=							ConfigTester(self      ,   config_class=lowerCAmelCase__      ,   has_text_modality=lowerCAmelCase__      ,   hidden_size=37						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							self.config_tester.run_common_tests()





			@unittest.skip(reason='''DPT does not use inputs_embeds'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

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





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

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowercase 			=							['''pixel_values''']
											self.assertListEqual(arg_names[:1]      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase__						)





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

							'''simple docstring'''


							__lowercase 			=							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__						)





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

							'''simple docstring'''


							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

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

											if model_class in get_values(lowerCAmelCase__						):
															continue

											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





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

							'''simple docstring'''


							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

											__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()
											__lowercase 			=							False
											__lowercase 			=							True

											if model_class in get_values(lowerCAmelCase__						) or not model_class.supports_gradient_checkpointing:
															continue
											__lowercase 			=							model_class(lowerCAmelCase__						)
											model.to(lowerCAmelCase__						)
											model.gradient_checkpointing_enable()
											model.train()
											__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__      ,   return_labels=lowerCAmelCase__						)
											__lowercase 			=							model(**lowerCAmelCase__						).loss
											loss.backward()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							__lowercase 			=							_config_zero_init(lowerCAmelCase__						)
							for model_class in self.all_model_classes:
											__lowercase 			=							model_class(config=lowerCAmelCase__						)
											# Skip the check for the backbone
											__lowercase 			=							[]
											for name, module in model.named_modules():
															if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																			__lowercase 			=							[F"{name}.{key}" for key in module.state_dict().keys()]
																			break

											for name, param in model.named_parameters():
															if param.requires_grad:
																			if name in backbone_params:
																							continue
																			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"      ,   )





			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							pass





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											__lowercase 			=							DPTModel.from_pretrained(lowerCAmelCase__						)
											self.assertIsNotNone(lowerCAmelCase__						)





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

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()
							__lowercase 			=							'''add'''
							with self.assertRaises(lowerCAmelCase__						):
											__lowercase 			=							DPTForDepthEstimation(lowerCAmelCase__						)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




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


			"""simple docstring"""






			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''						)
							__lowercase 			=							DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''						).to(lowerCAmelCase__						)

							__lowercase 			=							prepare_img()
							__lowercase 			=							image_processor(images=lowerCAmelCase__      ,   return_tensors='''pt'''						).to(lowerCAmelCase__						)

							# forward pass
							with torch.no_grad():
											__lowercase 			=							model(**lowerCAmelCase__						)
											__lowercase 			=							outputs.predicted_depth

							# verify the predicted depth
							__lowercase 			=							torch.Size((1, 3_84, 3_84)						)
							self.assertEqual(predicted_depth.shape      ,   lowerCAmelCase__						)

							__lowercase 			=							torch.tensor(
							    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]						).to(lowerCAmelCase__						)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00      ,   lowerCAmelCase__      ,   atol=1E-4						)						)
from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__a  :     List[str]       =  Lock()





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				global process_lock

				# we perform n swaps since after n swaps we know we are sorted
				# we *could* stop early if we are sorted already, but it takes as long to
				# find out we are sorted as it does to sort the list with this algorithm
				for i in range(0							,			10							):
								if (i + position) % 2 == 0 and r_send is not None:
												# send your value to your right neighbor
												process_lock.acquire()
												r_send[1].send(lowercase							)
												process_lock.release()

												# receive your right neighbor's value
												process_lock.acquire()
												__lowercase 			=							rr_cv[0].recv()
												process_lock.release()

												# take the lower value since you are on the left
												__lowercase 			=							min(lowercase							,			lowercase							)
								elif (i + position) % 2 != 0 and l_send is not None:
												# send your value to your left neighbor
												process_lock.acquire()
												l_send[1].send(lowercase							)
												process_lock.release()

												# receive your left neighbor's value
												process_lock.acquire()
												__lowercase 			=							lr_cv[0].recv()
												process_lock.release()

												# take the higher value since you are on the right
												__lowercase 			=							max(lowercase							,			lowercase							)
    # after all swaps are performed, send the values back to main
				result_pipe[1].send(lowercase							)





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]
				__lowercase 			=							[]
				# initialize the list of pipes where the values will be retrieved
				for _ in arr:
								result_pipe.append(Pipe()							)
				# creates the processes
				# the first and last process only have one neighbor so they are made outside
				# of the loop
				__lowercase 			=							Pipe()
				__lowercase 			=							Pipe()
				process_array_.append(
				    Process(
				        target=lowercase							,			args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])							,			)							)
				__lowercase 			=							temp_rs
				__lowercase 			=							temp_rr

				for i in range(1							,			len(lowercase							) - 1							):
								__lowercase 			=							Pipe()
								__lowercase 			=							Pipe()
								process_array_.append(
								    Process(
								        target=lowercase							,			args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])							,			)							)
								__lowercase 			=							temp_rs
								__lowercase 			=							temp_rr

				process_array_.append(
				    Process(
				        target=lowercase							,			args=(
				            len(lowercase							) - 1,
				            arr[len(lowercase							) - 1],
				            temp_ls,
				            None,
				            temp_lr,
				            None,
				            result_pipe[len(lowercase							) - 1],
				        )							,			)							)

				# start the processes
				for p in process_array_:
								p.start()

				# wait for the processes to end and write their values to the list
				for p in range(0							,			len(lowercase							)							):
								__lowercase 			=							result_pipe[p][0].recv()
								process_array_[p].join()
				return arr





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							list(range(10							,			0							,			-1							)							)
				print('''Initial List'''							)
				print(*lowercase							)
				__lowercase 			=							odd_even_transposition(lowercase							)
				print('''Sorted List\n'''							)
				print(*lowercase							)


if __name__ == "__main__":
		main()