
"""simple docstring"""




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 			snake_case  :

							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_=0.02							,							SCREAMING_SNAKE_CASE_=3							,							SCREAMING_SNAKE_CASE_=[1, 3_84, 24, 24]							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

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

									SCREAMING_SNAKE_CASE_    			=			self.get_config()

									return config, pixel_values, labels

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''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=SCREAMING_SNAKE_CASE_							,							initializer_range=self.initializer_range							,							is_hybrid=self.is_hybrid							,							backbone_config=SCREAMING_SNAKE_CASE_							,							backbone_featmap_shape=self.backbone_featmap_shape							,							)

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.num_labels
									SCREAMING_SNAKE_CASE_    			=			DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			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 _lowercase       (self     ):

									"""simple docstring"""







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







@require_torch
class 			snake_case  (				__lowercase							,      __lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							UpperCAmelCase__      =   (
							    {
							        '''depth-estimation''': DPTForDepthEstimation,
							        '''feature-extraction''': DPTModel,
							        '''image-segmentation''': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   False

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			DPTModelTester(self     )
									SCREAMING_SNAKE_CASE_    			=			ConfigTester(self							,							config_class=SCREAMING_SNAKE_CASE_							,							has_text_modality=SCREAMING_SNAKE_CASE_							,							hidden_size=37     )

							def _lowercase       (self     ):

									"""simple docstring"""







									self.config_tester.run_common_tests()

							@unittest.skip(reason='''DPT does not use inputs_embeds'''     )
							def _lowercase       (self     ):

									"""simple docstring"""







									pass

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs_for_common()

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

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs_for_common()

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

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

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

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

											if model_class in get_values(SCREAMING_SNAKE_CASE_     ):
													continue

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

							def _lowercase       (self     ):

									"""simple docstring"""







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

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

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

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs_for_common()

									SCREAMING_SNAKE_CASE_    			=			_config_zero_init(SCREAMING_SNAKE_CASE_     )
									for model_class in self.all_model_classes:
											SCREAMING_SNAKE_CASE_    			=			model_class(config=SCREAMING_SNAKE_CASE_     )
											# Skip the check for the backbone
											SCREAMING_SNAKE_CASE_    			=			[]
											for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
															SCREAMING_SNAKE_CASE_    			=			[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 _lowercase       (self     ):

									"""simple docstring"""







									pass

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







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







							def _lowercase       (self     ):

									"""simple docstring"""







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







def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image





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

							def _lowercase       (self     ):

									"""simple docstring"""







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

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

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

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

									SCREAMING_SNAKE_CASE_    			=			torch.tensor(
									    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]     ).to(SCREAMING_SNAKE_CASE_     )

									self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00							,							SCREAMING_SNAKE_CASE_							,							atol=1e-4     )     )

"""simple docstring"""




import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')



def 							_lowerCamelCase			(			__a     ):
		return input_array.reshape((input_array.size, 1)     )



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				# Centralize the data of class i
				SCREAMING_SNAKE_CASE_    			=			data - column_reshape(__a     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += np.dot(__a,				centered_data.T     )
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     )

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.shape[1]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a     ):

		# Check if the features have been loaded
		if features.any():
				SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
				# Center the dataset
				SCREAMING_SNAKE_CASE_    			=			features - np.reshape(__a,				(data_mean.size, 1)     )
				SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     ) / features.shape[1]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.eigh(__a     )
				# Take all the columns in the reverse order (-1), and then takes only the first
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, 0:dimensions]
				# Project the database on the new space
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_eigenvectors.T,				__a     )
				logging.info('''Principal Component Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a     ):
		assert classes > dimensions

		# Check if features have been already loaded
		if features.any:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			eigh(
				    covariance_between_classes(__a,				__a,				__a     ),				covariance_within_classes(__a,				__a,				__a     ),				)
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, :dimensions]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.svd(__a     )
				SCREAMING_SNAKE_CASE_    			=			svd_matrix[:, 0:dimensions]
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_svd_matrix.T,				__a     )
				logging.info('''Linear Discriminant Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			):
		# Create dummy dataset with 2 classes and 3 features
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
		SCREAMING_SNAKE_CASE_    			=			np.array([0, 0, 0, 1, 1]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			2

		# Assert that the function raises an AssertionError if dimensions > classes
		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			linear_discriminant_analysis(
				    __a,				__a,				__a,				__a     )
				if isinstance(__a,				np.ndarray     ):
						raise AssertionError(
						    '''Did not raise AssertionError for dimensions > classes'''     )
				assert error_info.type is AssertionError



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			principal_component_analysis(__a,				__a     )
				if not np.allclose(__a,				__a     ):
						raise AssertionError
				assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()