






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			:


						def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase=2					,						__UpperCamelCase=3_2					,						__UpperCamelCase=1_6					,						__UpperCamelCase=3					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=3_2					,						__UpperCamelCase=4					,						__UpperCamelCase=[0, 1, 2, 3]					,						__UpperCamelCase=4					,						__UpperCamelCase=3_7					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=3					,						__UpperCamelCase=[1, 3_8_4, 2_4, 2_4]					,						__UpperCamelCase=True					,						__UpperCamelCase=None					,						)->	Union[str, Any]:
											__lowerCAmelCase								=					parent
											__lowerCAmelCase								=					batch_size
											__lowerCAmelCase								=					image_size
											__lowerCAmelCase								=					patch_size
											__lowerCAmelCase								=					num_channels
											__lowerCAmelCase								=					is_training
											__lowerCAmelCase								=					use_labels
											__lowerCAmelCase								=					hidden_size
											__lowerCAmelCase								=					num_hidden_layers
											__lowerCAmelCase								=					backbone_out_indices
											__lowerCAmelCase								=					num_attention_heads
											__lowerCAmelCase								=					intermediate_size
											__lowerCAmelCase								=					hidden_act
											__lowerCAmelCase								=					hidden_dropout_prob
											__lowerCAmelCase								=					attention_probs_dropout_prob
											__lowerCAmelCase								=					initializer_range
											__lowerCAmelCase								=					num_labels
											__lowerCAmelCase								=					backbone_featmap_shape
											__lowerCAmelCase								=					scope
											__lowerCAmelCase								=					is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											__lowerCAmelCase								=					(image_size // patch_size) ** 2
											__lowerCAmelCase								=					num_patches + 1


						def  __UpperCAmelCase		(	self	)->	Optional[int]:
											__lowerCAmelCase								=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

											__lowerCAmelCase								=					self.get_config()

											return config, pixel_values, labels


						def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
											__lowerCAmelCase								=					{
											    "global_padding": "same",
											    "layer_type": "bottleneck",
											    "depths": [3, 4, 9],
											    "out_features": ["stage1", "stage2", "stage3"],
											    "embedding_dynamic_padding": True,
											    "hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
											    "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=__UpperCamelCase					,						initializer_range=self.initializer_range					,						is_hybrid=self.is_hybrid					,						backbone_config=__UpperCamelCase					,						backbone_featmap_shape=self.backbone_featmap_shape					,						)


						def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Any:
											__lowerCAmelCase								=					DPTModel(config=__UpperCamelCase	)
											model.to(__UpperCamelCase	)
											model.eval()
											__lowerCAmelCase								=					model(__UpperCamelCase	)
											self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)	)


						def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
											__lowerCAmelCase								=					self.num_labels
											__lowerCAmelCase								=					DPTForDepthEstimation(__UpperCamelCase	)
											model.to(__UpperCamelCase	)
											model.eval()
											__lowerCAmelCase								=					model(__UpperCamelCase	)
											self.parent.assertEqual(result.predicted_depth.shape					,						(self.batch_size, self.image_size, self.image_size)	)


						def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Tuple:
											__lowerCAmelCase								=					self.num_labels
											__lowerCAmelCase								=					DPTForSemanticSegmentation(__UpperCamelCase	)
											model.to(__UpperCamelCase	)
											model.eval()
											__lowerCAmelCase								=					model(__UpperCamelCase					,						labels=__UpperCamelCase	)
											self.parent.assertEqual(
											    result.logits.shape					,						(self.batch_size, self.num_labels, self.image_size, self.image_size)	)


						def  __UpperCAmelCase		(	self	)->	Dict:
											__lowerCAmelCase								=					self.prepare_config_and_inputs()
											__lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					config_and_inputs
											__lowerCAmelCase								=					{"pixel_values": pixel_values}
											return config, inputs_dict



@require_torch
class 							_UpperCamelCase			(a_							,		a_							,		unittest.TestCase			):
						snake_case_  =							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						snake_case_  =							(
						    {
						        """depth-estimation""": DPTForDepthEstimation,
						        """feature-extraction""": DPTModel,
						        """image-segmentation""": DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						snake_case_  =							False
						snake_case_  =							False
						snake_case_  =							False


						def  __UpperCAmelCase		(	self	)->	List[str]:
											__lowerCAmelCase								=					DPTModelTester(self	)
											__lowerCAmelCase								=					ConfigTester(self					,						config_class=__UpperCamelCase					,						has_text_modality=__UpperCamelCase					,						hidden_size=3_7	)


						def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
											self.config_tester.run_common_tests()


						@unittest.skip(reason="DPT does not use inputs_embeds"	)
						def  __UpperCAmelCase		(	self	)->	str:
											pass


						def  __UpperCAmelCase		(	self	)->	str:
											__lowerCAmelCase			, __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()

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


						def  __UpperCAmelCase		(	self	)->	Optional[Any]:
											__lowerCAmelCase			, __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()

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

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


						def  __UpperCAmelCase		(	self	)->	Optional[Any]:
											__lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCamelCase	)


						def  __UpperCAmelCase		(	self	)->	List[str]:
											__lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase	)


						def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
											__lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase	)


						def  __UpperCAmelCase		(	self	)->	List[str]:
											for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																					continue

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

																if model_class in get_values(__UpperCamelCase	):
																					continue

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


						def  __UpperCAmelCase		(	self	)->	Optional[int]:
											for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																					continue

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

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


						def  __UpperCAmelCase		(	self	)->	List[str]:
											__lowerCAmelCase			, __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()

											__lowerCAmelCase								=					_config_zero_init(__UpperCamelCase	)
											for model_class in self.all_model_classes:
																__lowerCAmelCase								=					model_class(config=__UpperCamelCase	)
																# Skip the check for the backbone
																__lowerCAmelCase								=					[]
																for name, module in model.named_modules():
																					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																										__lowerCAmelCase								=					[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  __UpperCAmelCase		(	self	)->	int:
											pass


						@slow
						def  __UpperCAmelCase		(	self	)->	Tuple:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																__lowerCAmelCase								=					DPTModel.from_pretrained(__UpperCamelCase	)
																self.assertIsNotNone(__UpperCamelCase	)


						def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
											# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
											__lowerCAmelCase			, __lowerCAmelCase								=					self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase								=					"add"
											with self.assertRaises(__UpperCamelCase	):
																__lowerCAmelCase								=					DPTForDepthEstimation(__UpperCamelCase	)





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



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


						def  __UpperCAmelCase		(	self	)->	int:
											__lowerCAmelCase								=					DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
											__lowerCAmelCase								=					DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(__UpperCamelCase	)

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

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase								=					model(**__UpperCamelCase	)
																__lowerCAmelCase								=					outputs.predicted_depth

											# verify the predicted depth
											__lowerCAmelCase								=					torch.Size((1, 3_8_4, 3_8_4)	)
											self.assertEqual(predicted_depth.shape					,						__UpperCamelCase	)

											__lowerCAmelCase								=					torch.tensor(
											    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]	).to(__UpperCamelCase	)

											self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0					,						__UpperCamelCase					,						atol=1e-4	)	)














def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					len(__snake_case       )
     while cur > 1:
          # Find the maximum number in arr
          __lowerCAmelCase								=					arr.index(max(arr[0:cur]       )       )
          # Reverse from 0 to mi
          __lowerCAmelCase								=					arr[mi::-1] + arr[mi + 1 : len(__snake_case       )]
          # Reverse whole list
          __lowerCAmelCase								=					arr[cur - 1 :: -1] + arr[cur : len(__snake_case       )]
          cur -= 1
     return arr


if __name__ == "__main__":
 lowerCamelCase			:					List[str]         =	input('''Enter numbers separated by a comma:\n''').strip()
 lowerCamelCase			:					Tuple         =	[int(item) for item in user_input.split(''',''')]
 print(pancake_sort(unsorted))






