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        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=2		,      __SCREAMING_SNAKE_CASE=32		,      __SCREAMING_SNAKE_CASE=16		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=True		,      __SCREAMING_SNAKE_CASE=32		,      __SCREAMING_SNAKE_CASE=4		,      __SCREAMING_SNAKE_CASE=[0, 1, 2, 3]		,      __SCREAMING_SNAKE_CASE=4		,      __SCREAMING_SNAKE_CASE=37		,      __SCREAMING_SNAKE_CASE="gelu"		,      __SCREAMING_SNAKE_CASE=0.1		,      __SCREAMING_SNAKE_CASE=0.1		,      __SCREAMING_SNAKE_CASE=0.0_2		,      __SCREAMING_SNAKE_CASE=3		,      __SCREAMING_SNAKE_CASE=False		,      __SCREAMING_SNAKE_CASE=None		,      )						->Tuple:
								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     		=		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       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								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       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								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=__SCREAMING_SNAKE_CASE		,      initializer_range=self.initializer_range		,      is_hybrid=self.is_hybrid		,      )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase     		=		DPTModel(config=__SCREAMING_SNAKE_CASE    )
								model.to(__SCREAMING_SNAKE_CASE    )
								model.eval()
								lowerCAmelCase     		=		model(__SCREAMING_SNAKE_CASE    )
								self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->str:
								lowerCAmelCase     		=		self.num_labels
								lowerCAmelCase     		=		DPTForDepthEstimation(__SCREAMING_SNAKE_CASE    )
								model.to(__SCREAMING_SNAKE_CASE    )
								model.eval()
								lowerCAmelCase     		=		model(__SCREAMING_SNAKE_CASE    )
								self.parent.assertEqual(result.predicted_depth.shape		,      (self.batch_size, self.image_size, self.image_size)    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->str:
								lowerCAmelCase     		=		self.num_labels
								lowerCAmelCase     		=		DPTForSemanticSegmentation(__SCREAMING_SNAKE_CASE    )
								model.to(__SCREAMING_SNAKE_CASE    )
								model.eval()
								lowerCAmelCase     		=		model(__SCREAMING_SNAKE_CASE		,      labels=__SCREAMING_SNAKE_CASE    )
								self.parent.assertEqual(
								    result.logits.shape		,      (self.batch_size, self.num_labels, self.image_size, self.image_size)    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		self.prepare_config_and_inputs()
								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		config_and_inputs
								lowerCAmelCase     		=		{'''pixel_values''': pixel_values}
								return config, inputs_dict




@require_torch
class        lowercase_  (  UpperCamelCase_				,					UpperCamelCase_				,					unittest.TestCase						):



	"""simple docstring"""





	UpperCAmelCase_  : Optional[Any]       =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	UpperCAmelCase_  : Dict       =    (
	    {
	        """depth-estimation""": DPTForDepthEstimation,
	        """feature-extraction""": DPTModel,
	        """image-segmentation""": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	UpperCAmelCase_  : str       =    False
	UpperCAmelCase_  : List[Any]       =    False
	UpperCAmelCase_  : Optional[int]       =    False


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		DPTModelTester(self    )
								lowerCAmelCase     		=		ConfigTester(self		,      config_class=__SCREAMING_SNAKE_CASE		,      has_text_modality=__SCREAMING_SNAKE_CASE		,      hidden_size=37    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[Any]:
								self.config_tester.run_common_tests()


	@unittest.skip(reason='''DPT does not use inputs_embeds'''    )
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								pass


	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[Any]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowerCAmelCase     		=		model_class(__SCREAMING_SNAKE_CASE    )
															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]		,      __SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								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(__SCREAMING_SNAKE_CASE    ):
																						continue

															lowerCAmelCase     		=		model_class(__SCREAMING_SNAKE_CASE    )
															model.to(__SCREAMING_SNAKE_CASE    )
															model.train()
															lowerCAmelCase     		=		self._prepare_for_class(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      return_labels=__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    ).loss
															loss.backward()


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								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(__SCREAMING_SNAKE_CASE    ) or not model_class.supports_gradient_checkpointing:
																						continue
															lowerCAmelCase     		=		model_class(__SCREAMING_SNAKE_CASE    )
															model.to(__SCREAMING_SNAKE_CASE    )
															model.gradient_checkpointing_enable()
															model.train()
															lowerCAmelCase     		=		self._prepare_for_class(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      return_labels=__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    ).loss
															loss.backward()


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase     		=		_config_zero_init(__SCREAMING_SNAKE_CASE    )
								for model_class in self.all_model_classes:
															lowerCAmelCase     		=		model_class(config=__SCREAMING_SNAKE_CASE    )
															# 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       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								pass




	@slow
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase     		=		DPTModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
															self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )

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






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



	"""simple docstring"""





	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								lowerCAmelCase     		=		DPTImageProcessor.from_pretrained('''Intel/dpt-large'''    )
								lowerCAmelCase     		=		DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''    ).to(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		prepare_img()
								lowerCAmelCase     		=		image_processor(images=__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''    ).to(__SCREAMING_SNAKE_CASE    )

								# forward pass
								with torch.no_grad():
															lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		outputs.predicted_depth

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

								lowerCAmelCase     		=		torch.tensor(
								    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]    ).to(__SCREAMING_SNAKE_CASE    )

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								lowerCAmelCase     		=		DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''    )
								lowerCAmelCase     		=		DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''    ).to(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		prepare_img()
								lowerCAmelCase     		=		image_processor(images=__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''    ).to(__SCREAMING_SNAKE_CASE    )

								# forward pass
								with torch.no_grad():
															lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    )

								# verify the logits
								lowerCAmelCase     		=		torch.Size((1, 150, 480, 480)    )
								self.assertEqual(outputs.logits.shape		,      __SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		torch.tensor(
								    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]    ).to(__SCREAMING_SNAKE_CASE    )

								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1e-4    )    )




	def       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''    )
								lowerCAmelCase     		=		DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''    ).to(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		prepare_img()
								lowerCAmelCase     		=		image_processor(images=__SCREAMING_SNAKE_CASE		,      return_tensors='''pt'''    ).to(__SCREAMING_SNAKE_CASE    )

								# forward pass
								with torch.no_grad():
															lowerCAmelCase     		=		model(**__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		outputs.logits.detach().cpu()

								lowerCAmelCase     		=		image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE		,      target_sizes=[(500, 300)]    )
								lowerCAmelCase     		=		torch.Size((500, 300)    )
								self.assertEqual(segmentation[0].shape		,      __SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		torch.Size((480, 480)    )
								self.assertEqual(segmentation[0].shape		,      __SCREAMING_SNAKE_CASE    )




import random




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		x
								lowerCAmelCase     		=		y


	def       SCREAMING_SNAKE_CASE_    (							self    )						->bool:
								return (self.x**2 + self.y**2) <= 1




	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Tuple:
								return cls(x=random.random()		,      y=random.random()    )

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     float:
							if number_of_simulations < 1:
														raise ValueError('''At least one simulation is necessary to estimate PI.'''							)

							lowerCAmelCase     		=		0
							for _ in range(snake_case__							):
														lowerCAmelCase     		=		Point.random_unit_square()

														if random_point.is_in_unit_circle():
																					number_in_unit_circle += 1

							return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
	# import doctest

	# doctest.testmod()
	from math import pi

	lowercase__      :    int          =     '''Please enter the desired number of Monte Carlo simulations: '''
	lowercase__      :    Dict          =     estimate_pi(int(input(prompt).strip()))
	print(f'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')



