

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 					a__						:
		"""simple docstring"""







		def __init__(    self      ,      lowercase      ,      lowercase=2      ,      lowercase=32      ,      lowercase=16      ,      lowercase=3      ,      lowercase=True      ,      lowercase=True      ,      lowercase=32      ,      lowercase=4      ,      lowercase=[0, 1, 2, 3]      ,      lowercase=4      ,      lowercase=37      ,      lowercase="gelu"      ,      lowercase=0.1      ,      lowercase=0.1      ,      lowercase=0.02      ,      lowercase=3      ,      lowercase=[1, 384, 24, 24]      ,      lowercase=True      ,      lowercase=None      ,      )  ->						List[Any]:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self       )  ->						Tuple:
						'''simple docstring'''






						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      UpperCamelCase		(    self       )  ->						Union[str, Any]:
						'''simple docstring'''






						A__        =			{
						    "global_padding": "same",
						    "layer_type": "bottleneck",
						    "depths": [3, 4, 9],
						    "out_features": ["stage1", "stage2", "stage3"],
						    "embedding_dynamic_padding": True,
						    "hidden_sizes": [96, 192, 384, 768],
						    "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=lowercase      ,      initializer_range=self.initializer_range      ,      is_hybrid=self.is_hybrid      ,      backbone_config=lowercase      ,      backbone_featmap_shape=self.backbone_featmap_shape      ,      )


		def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						str:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Tuple:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						List[str]:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self       )  ->						Optional[Any]:
						'''simple docstring'''






						A__        =			self.prepare_config_and_inputs()
						A__    ,				A__    ,				A__        =			config_and_inputs
						A__        =			{"pixel_values": pixel_values}
						return config, inputs_dict

@require_torch
class 					a__						(					snake_case							,	snake_case							,	unittest.TestCase			):
		"""simple docstring"""







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

		__lowerCamelCase        =       False
		__lowerCamelCase        =       False
		__lowerCamelCase        =       False


		def      UpperCamelCase		(    self       )  ->						List[str]:
						'''simple docstring'''






						A__        =			DPTModelTester(self       )
						A__        =			ConfigTester(self      ,      config_class=lowercase      ,      has_text_modality=lowercase      ,      hidden_size=37       )


		def      UpperCamelCase		(    self       )  ->						Optional[Any]:
						'''simple docstring'''






						self.config_tester.run_common_tests()


		@unittest.skip(reason="DPT does not use inputs_embeds"       )
		def      UpperCamelCase		(    self       )  ->						List[Any]:
						'''simple docstring'''






						pass


		def      UpperCamelCase		(    self       )  ->						Tuple:
						'''simple docstring'''






						A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

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


		def      UpperCamelCase		(    self       )  ->						List[str]:
						'''simple docstring'''






						A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										A__        =			model_class(lowercase       )
										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]      ,      lowercase       )


		def      UpperCamelCase		(    self       )  ->						Optional[int]:
						'''simple docstring'''






						A__        =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowercase       )


		def      UpperCamelCase		(    self       )  ->						Dict:
						'''simple docstring'''






						A__        =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*lowercase       )


		def      UpperCamelCase		(    self       )  ->						Optional[int]:
						'''simple docstring'''






						A__        =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*lowercase       )


		def      UpperCamelCase		(    self       )  ->						int:
						'''simple docstring'''






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

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

										if model_class in get_values(lowercase       ):
														continue

										A__        =			model_class(lowercase       )
										model.to(lowercase       )
										model.train()
										A__        =			self._prepare_for_class(lowercase      ,      lowercase      ,      return_labels=lowercase       )
										A__        =			model(**lowercase       ).loss
										loss.backward()


		def      UpperCamelCase		(    self       )  ->						Optional[int]:
						'''simple docstring'''






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

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

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


		def      UpperCamelCase		(    self       )  ->						List[Any]:
						'''simple docstring'''






						A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

						A__        =			_config_zero_init(lowercase       )
						for model_class in self.all_model_classes:
										A__        =			model_class(config=lowercase       )
										# Skip the check for the backbone
										A__        =			[]
										for name, module in model.named_modules():
														if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																		A__        =			[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       )  ->						str:
						'''simple docstring'''






						pass


		@slow
		def      UpperCamelCase		(    self       )  ->						str:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self       )  ->						Tuple:
						'''simple docstring'''






						A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()
						A__        =			"add"
						with self.assertRaises(lowercase       ):
										A__        =			DPTForDepthEstimation(lowercase       )



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





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





@require_torch
@require_vision
@slow
class 					a__						(					unittest.TestCase			):
		"""simple docstring"""







		def      UpperCamelCase		(    self       )  ->						Tuple:
						'''simple docstring'''






						A__        =			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"       )
						A__        =			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"       ).to(lowercase       )

						A__        =			prepare_img()
						A__        =			image_processor(images=lowercase      ,      return_tensors="pt"       ).to(lowercase       )

						# forward pass
						with torch.no_grad():
										A__        =			model(**lowercase       )
										A__        =			outputs.predicted_depth

						# verify the predicted depth
						A__        =			torch.Size((1, 384, 384)       )
						self.assertEqual(predicted_depth.shape      ,      lowercase       )

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

						self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100      ,      lowercase      ,      atol=1e-4       )       )




def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





				if divisor % 5 == 0 or divisor % 2 == 0:
								return 0
				A__        =			1
				A__        =			1
				while repunit:
								A__        =			(1_0 * repunit + 1) % divisor
								repunit_index += 1
				return repunit_index



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0_0_0_0_0      )			->      int:




				'''simple docstring'''





				A__        =			limit - 1
				if divisor % 2 == 0:
								divisor += 1
				while least_divisible_repunit(SCREAMING_SNAKE_CASE_      ) <= limit:
								divisor += 2
				return divisor


if __name__ == "__main__":
	print(f"""{solution() = }""")

