


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     :Optional[Any]    ,__snake_case     :List[str]    ,__snake_case     :Tuple=2    ,__snake_case     :Dict=32    ,__snake_case     :int=16    ,__snake_case     :Dict=3    ,__snake_case     :int=True    ,__snake_case     :Any=True    ,__snake_case     :Tuple=32    ,__snake_case     :List[Any]=4    ,__snake_case     :int=[0, 1, 2, 3]    ,__snake_case     :List[str]=4    ,__snake_case     :List[Any]=37    ,__snake_case     :Union[str, Any]="gelu"    ,__snake_case     :Optional[Any]=0.1    ,__snake_case     :int=0.1    ,__snake_case     :Dict=0.02    ,__snake_case     :Dict=3    ,__snake_case     :Union[str, Any]=[1, 3_84, 24, 24]    ,__snake_case     :List[Any]=True    ,__snake_case     :Union[str, Any]=None    ,)      ->					Any:
												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 				lowerCamelCase__(   self     :Any   )      ->					Union[str, Any]:
												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 				lowerCamelCase__(   self     :List[Any]   )      ->					Union[str, Any]:
												a__    =  {
												    '''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=__snake_case    ,initializer_range=self.initializer_range    ,is_hybrid=self.is_hybrid    ,backbone_config=__snake_case    ,backbone_featmap_shape=self.backbone_featmap_shape    ,)
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :List[Any]    ,__snake_case     :Dict    ,__snake_case     :Dict   )      ->					Optional[int]:
												a__    =  DPTModel(config=__snake_case   )
												model.to(__snake_case   )
												model.eval()
												a__    =  model(__snake_case   )
												self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)   )
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :str    ,__snake_case     :List[str]    ,__snake_case     :str   )      ->					List[str]:
												a__    =  self.num_labels
												a__    =  DPTForDepthEstimation(__snake_case   )
												model.to(__snake_case   )
												model.eval()
												a__    =  model(__snake_case   )
												self.parent.assertEqual(result.predicted_depth.shape    ,(self.batch_size, self.image_size, self.image_size)   )
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :Optional[int]    ,__snake_case     :Optional[Any]    ,__snake_case     :Union[str, Any]   )      ->					Any:
												a__    =  self.num_labels
												a__    =  DPTForSemanticSegmentation(__snake_case   )
												model.to(__snake_case   )
												model.eval()
												a__    =  model(__snake_case    ,labels=__snake_case   )
												self.parent.assertEqual(
												    result.logits.shape    ,(self.batch_size, self.num_labels, self.image_size, self.image_size)   )



					def 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					str:
												a__    =  self.prepare_config_and_inputs()
												a__    =  config_and_inputs
												a__    =  {'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class  snake_case_     (UpperCamelCase_			,		UpperCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Optional[Any]					   =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					UpperCAmelCase__      :					Optional[Any]					   =    (
					    {
					        '''depth-estimation''': DPTForDepthEstimation,
					        '''feature-extraction''': DPTModel,
					        '''image-segmentation''': DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__      :					str					   =    False
					UpperCAmelCase__      :					Optional[int]					   =    False
					UpperCAmelCase__      :					List[Any]					   =    False
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												a__    =  DPTModelTester(self   )
												a__    =  ConfigTester(self    ,config_class=__snake_case    ,has_text_modality=__snake_case    ,hidden_size=37   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Tuple:
												self.config_tester.run_common_tests()
					@unittest.skip(reason='DPT does not use inputs_embeds'   )
					def 				lowerCamelCase__(   self     :Dict   )      ->					int:
												pass
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Optional[int]:
												a__    =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__    =  model_class(__snake_case   )
																			self.assertIsInstance(model.get_input_embeddings()    ,(nn.Module)   )
																			a__    =  model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__snake_case    ,nn.Linear   )   )
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Any:
												a__    =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__    =  model_class(__snake_case   )
																			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]    ,__snake_case   )
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Dict:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case   )
					def 				lowerCamelCase__(   self     :List[Any]   )      ->					Any:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*__snake_case   )
					def 				lowerCamelCase__(   self     :List[str]   )      ->					Optional[int]:
												a__    =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case   )
					def 				lowerCamelCase__(   self     :Dict   )      ->					Dict:
												for model_class in self.all_model_classes:
																			if model_class.__name__ == "DPTForDepthEstimation":
																										continue

																			a__    =  self.model_tester.prepare_config_and_inputs_for_common()
																			a__    =  True

																			if model_class in get_values(__snake_case   ):
																										continue

																			a__    =  model_class(__snake_case   )
																			model.to(__snake_case   )
																			model.train()
																			a__    =  self._prepare_for_class(__snake_case    ,__snake_case    ,return_labels=__snake_case   )
																			a__    =  model(**__snake_case   ).loss
																			loss.backward()
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Union[str, Any]:
												for model_class in self.all_model_classes:
																			if model_class.__name__ == "DPTForDepthEstimation":
																										continue

																			a__    =  self.model_tester.prepare_config_and_inputs_for_common()
																			a__    =  False
																			a__    =  True

																			if model_class in get_values(__snake_case   ) or not model_class.supports_gradient_checkpointing:
																										continue
																			a__    =  model_class(__snake_case   )
																			model.to(__snake_case   )
																			model.gradient_checkpointing_enable()
																			model.train()
																			a__    =  self._prepare_for_class(__snake_case    ,__snake_case    ,return_labels=__snake_case   )
																			a__    =  model(**__snake_case   ).loss
																			loss.backward()
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Optional[int]:
												a__    =  self.model_tester.prepare_config_and_inputs_for_common()

												a__    =  _config_zero_init(__snake_case   )
												for model_class in self.all_model_classes:
																			a__    =  model_class(config=__snake_case   )
																			# 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 				lowerCamelCase__(   self     :Union[str, Any]   )      ->					int:
												pass
					@slow
					def 				lowerCamelCase__(   self     :str   )      ->					Any:
												for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																			a__    =  DPTModel.from_pretrained(__snake_case   )
																			self.assertIsNotNone(__snake_case   )



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






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







@require_torch
@require_vision
@slow
class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Tuple   )      ->					str:
												a__    =  DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
												a__    =  DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(__snake_case   )

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

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

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

												a__    =  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(__snake_case   )

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








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							: Dict      =   logging.get_logger(__name__)







class  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Dict					   =    ['''pixel_values''']
					def __init__(   self     :Optional[Any]    ,__snake_case     :bool = True    ,__snake_case     :int = 32    ,__snake_case     :Union[str, Any]=PILImageResampling.BILINEAR    ,__snake_case     :bool = True    ,**__snake_case     :Tuple    ,)      ->					None:
												a__    =  do_resize
												a__    =  do_rescale
												a__    =  size_divisor
												a__    =  resample
												super().__init__(**__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :np.ndarray    ,__snake_case     :int    ,__snake_case     :Tuple    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :List[Any]   )      ->					np.ndarray:
												a__   ,      a__    =  get_image_size(__snake_case   )
												# Rounds the height and width down to the closest multiple of size_divisor
												a__    =  height // size_divisor * size_divisor
												a__    =  width // size_divisor * size_divisor
												a__    =  resize(__snake_case    ,(new_h, new_w)    ,resample=__snake_case    ,data_format=__snake_case    ,**__snake_case   )
												return image
					def 				lowerCamelCase__(   self     :List[str]    ,__snake_case     :np.ndarray    ,__snake_case     :float    ,__snake_case     :Optional[ChannelDimension] = None    ,**__snake_case     :str   )      ->					np.ndarray:
												return rescale(image=__snake_case    ,scale=__snake_case    ,data_format=__snake_case    ,**__snake_case   )



					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :Union[str, Any]=None    ,__snake_case     :Optional[bool] = None    ,__snake_case     :Optional[Union[TensorType, str]] = None    ,__snake_case     :ChannelDimension = ChannelDimension.FIRST    ,**__snake_case     :List[Any]    ,)      ->					BatchFeature:
												a__    =  do_resize if do_resize is not None else self.do_resize
												a__    =  do_rescale if do_rescale is not None else self.do_rescale
												a__    =  size_divisor if size_divisor is not None else self.size_divisor
												a__    =  resample if resample is not None else self.resample

												if do_resize and size_divisor is None:
																			raise ValueError('size_divisor is required for resizing'   )

												a__    =  make_list_of_images(__snake_case   )

												if not valid_images(__snake_case   ):
																			raise ValueError('Invalid image(s)'   )

												# All transformations expect numpy arrays.
												a__    =  [to_numpy_array(__snake_case   ) for img in images]

												if do_resize:
																			a__    =  [self.resize(__snake_case    ,size_divisor=__snake_case    ,resample=__snake_case   ) for image in images]

												if do_rescale:
																			a__    =  [self.rescale(__snake_case    ,scale=1 / 2_55   ) for image in images]

												a__    =  [to_channel_dimension_format(__snake_case    ,__snake_case   ) for image in images]

												a__    =  {'pixel_values': images}
												return BatchFeature(data=__snake_case    ,tensor_type=__snake_case   )




