




"""simple docstring"""






import io
import math
from typing import Dict, Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends


if is_vision_available():
				import textwrap

				from PIL import Image, ImageDraw, ImageFont

if is_torch_available():
				import torch

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				UpperCamelCase__						=			False


UpperCamelCase__						=			logging.get_logger(__name__)
UpperCamelCase__						=			'''ybelkada/fonts'''





def 							UpperCAmelCase		(		):
					if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
										raise ImportError(
										    F'You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '
										    '''Pix2StructImageProcessor. Please upgrade torch.'''    )





def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					str    ):
					requires_backends(snake_case      ,       ['''torch''']    )
					_check_torch_version()

					_lowerCAmelCase:List[Any]									=  image_tensor.unsqueeze(0    )
					_lowerCAmelCase:str									=  torch.nn.functional.unfold(snake_case      ,       (patch_height, patch_width)      ,       stride=(patch_height, patch_width)    )
					_lowerCAmelCase:int									=  patches.reshape(image_tensor.size(0    )      ,       image_tensor.size(1    )      ,       snake_case      ,       snake_case      ,       -1    )
					_lowerCAmelCase:List[Any]									=  patches.permute(0      ,       4      ,       2      ,       3      ,       1    ).reshape(
					    image_tensor.size(2    ) // patch_height      ,       image_tensor.size(3    ) // patch_width      ,       image_tensor.size(1    ) * patch_height * patch_width      ,       )
					return patches.unsqueeze(0    )





def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					int = 36      ,       snake_case			:					str = "black"      ,       snake_case			:					str = "white"      ,       snake_case			:					int = 5      ,       snake_case			:					int = 5      ,       snake_case			:					int = 5      ,       snake_case			:					int = 5      ,       snake_case			:					Optional[bytes] = None      ,       snake_case			:					Optional[str] = None      ,       ):
					requires_backends(snake_case      ,       '''vision'''    )
					# Add new lines so that each line is no more than 80 characters.

					_lowerCAmelCase:int									=  textwrap.TextWrapper(width=80    )
					_lowerCAmelCase:Optional[int]									=  wrapper.wrap(text=snake_case    )
					_lowerCAmelCase:Optional[int]									=  '''\n'''.join(snake_case    )

					if font_bytes is not None and font_path is None:
										_lowerCAmelCase:Optional[int]									=  io.BytesIO(snake_case    )
					elif font_path is not None:
										_lowerCAmelCase:str									=  font_path
					else:
										_lowerCAmelCase:Union[str, Any]									=  hf_hub_download(snake_case      ,       '''Arial.TTF'''    )
					_lowerCAmelCase:Any									=  ImageFont.truetype(snake_case      ,       encoding='''UTF-8'''      ,       size=snake_case    )

					# Use a temporary canvas to determine the width and height in pixels when
					# rendering the text.
					_lowerCAmelCase:Optional[int]									=  ImageDraw.Draw(Image.new('''RGB'''      ,       (1, 1)      ,       snake_case    )    )
					_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  temp_draw.textbbox((0, 0)      ,       snake_case      ,       snake_case    )

					# Create the actual image with a bit of padding around the text.
					_lowerCAmelCase:Dict									=  text_width + left_padding + right_padding
					_lowerCAmelCase:Dict									=  text_height + top_padding + bottom_padding
					_lowerCAmelCase:Optional[Any]									=  Image.new('''RGB'''      ,       (image_width, image_height)      ,       snake_case    )
					_lowerCAmelCase:str									=  ImageDraw.Draw(snake_case    )
					draw.text(xy=(left_padding, top_padding)      ,       text=snake_case      ,       fill=snake_case      ,       font=snake_case    )
					return image





def 							UpperCAmelCase		(		snake_case			:					np.ndarray      ,       snake_case			:					str      ,       **snake_case			:					Union[str, Any]    ):
					requires_backends(snake_case      ,       '''vision'''    )

					# Convert to PIL image if necessary
					_lowerCAmelCase:Any									=  to_pil_image(snake_case    )

					_lowerCAmelCase:Any									=  render_text(snake_case      ,       **snake_case    )
					_lowerCAmelCase:Dict									=  max(header_image.width      ,       image.width    )

					_lowerCAmelCase:List[Any]									=  int(image.height * (new_width / image.width)    )
					_lowerCAmelCase:Any									=  int(header_image.height * (new_width / header_image.width)    )

					_lowerCAmelCase:Union[str, Any]									=  Image.new('''RGB'''      ,       (new_width, new_height + new_header_height)      ,       '''white'''    )
					new_image.paste(header_image.resize((new_width, new_header_height)    )      ,       (0, 0)    )
					new_image.paste(image.resize((new_width, new_height)    )      ,       (0, new_header_height)    )

					# Convert back to the original framework if necessary
					_lowerCAmelCase:Optional[int]									=  to_numpy_array(snake_case    )

					if infer_channel_dimension_format(snake_case    ) == ChannelDimension.LAST:
										_lowerCAmelCase:Tuple									=  to_channel_dimension_format(snake_case      ,       ChannelDimension.LAST    )

					return new_image





class   a__			(       UpperCamelCase_  ):
						snake_case__             = ['''flattened_patches''']





						def __init__(							self      :	Any     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	Dict[str, int] = None     ,a__      :	int = 2048     ,a__      :	bool = False     ,**a__      :	List[Any]     ,)						->       None:




											"""simple docstring"""







											super().__init__(**a__)
											_lowerCAmelCase:Optional[Any]									=  patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
											_lowerCAmelCase:Union[str, Any]									=  do_normalize
											_lowerCAmelCase:str									=  do_convert_rgb
											_lowerCAmelCase:int									=  max_patches
											_lowerCAmelCase:Optional[Any]									=  is_vqa





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	np.ndarray     ,a__      :	int     ,a__      :	dict     ,**a__      :	List[str])						->       np.ndarray:




											"""simple docstring"""







											requires_backends(self.extract_flattened_patches     ,'''torch''')
											_check_torch_version()

											# convert to torch
											_lowerCAmelCase:List[Any]									=  to_channel_dimension_format(a__     ,ChannelDimension.FIRST)
											_lowerCAmelCase:Optional[int]									=  torch.from_numpy(a__)

											_lowerCAmelCase							,   _lowerCAmelCase:str									=  patch_size['''height'''], patch_size['''width''']
											_lowerCAmelCase							,   _lowerCAmelCase:Tuple									=  get_image_size(a__)

											# maximize scale s.t.
											_lowerCAmelCase:Dict									=  math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width))
											_lowerCAmelCase:Tuple									=  max(min(math.floor(scale * image_height / patch_height)     ,a__)     ,1)
											_lowerCAmelCase:Any									=  max(min(math.floor(scale * image_width / patch_width)     ,a__)     ,1)
											_lowerCAmelCase:int									=  max(num_feasible_rows * patch_height     ,1)
											_lowerCAmelCase:int									=  max(num_feasible_cols * patch_width     ,1)

											_lowerCAmelCase:str									=  torch.nn.functional.interpolate(
											    image.unsqueeze(0)     ,size=(resized_height, resized_width)     ,mode='''bilinear'''     ,align_corners=a__     ,antialias=a__     ,).squeeze(0)

											# [1, rows, columns, patch_height * patch_width * image_channels]
											_lowerCAmelCase:int									=  torch_extract_patches(a__     ,a__     ,a__)

											_lowerCAmelCase:Optional[int]									=  patches.shape
											_lowerCAmelCase:Dict									=  patches_shape[1]
											_lowerCAmelCase:str									=  patches_shape[2]
											_lowerCAmelCase:Optional[Any]									=  patches_shape[3]

											# [rows * columns, patch_height * patch_width * image_channels]
											_lowerCAmelCase:int									=  patches.reshape([rows * columns, depth])

											# [rows * columns, 1]
											_lowerCAmelCase:List[Any]									=  torch.arange(a__).reshape([rows, 1]).repeat(1     ,a__).reshape([rows * columns, 1])
											_lowerCAmelCase:Optional[int]									=  torch.arange(a__).reshape([1, columns]).repeat(a__     ,1).reshape([rows * columns, 1])

											# Offset by 1 so the ids do not contain zeros, which represent padding.
											row_ids += 1
											col_ids += 1

											# Prepare additional patch features.
											# [rows * columns, 1]
											_lowerCAmelCase:Optional[int]									=  row_ids.to(torch.floataa)
											_lowerCAmelCase:Optional[int]									=  col_ids.to(torch.floataa)

											# [rows * columns, 2 + patch_height * patch_width * image_channels]
											_lowerCAmelCase:str									=  torch.cat([row_ids, col_ids, patches]     ,-1)

											# [max_patches, 2 + patch_height * patch_width * image_channels]
											_lowerCAmelCase:Optional[Any]									=  torch.nn.functional.pad(a__     ,[0, 0, 0, max_patches - (rows * columns)]).float()

											_lowerCAmelCase:Dict									=  to_numpy_array(a__)

											return result





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	np.ndarray     ,a__      :	Optional[Union[str, ChannelDimension]] = None     ,**a__      :	Optional[Any])						->       np.ndarray:




											"""simple docstring"""







											if image.dtype == np.uinta:
																_lowerCAmelCase:int									=  image.astype(np.floataa)

											# take mean across the whole `image`
											_lowerCAmelCase:List[str]									=  np.mean(a__)
											_lowerCAmelCase:str									=  np.std(a__)
											_lowerCAmelCase:str									=  max(a__     ,1.0 / math.sqrt(np.prod(image.shape)))

											return normalize(a__     ,mean=a__     ,std=a__     ,**a__)







						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	ImageInput     ,a__      :	Optional[str] = None     ,a__      :	bool = None     ,a__      :	Optional[bool] = None     ,a__      :	Optional[int] = None     ,a__      :	Optional[Dict[str, int]] = None     ,a__      :	Optional[Union[str, TensorType]] = None     ,a__      :	ChannelDimension = ChannelDimension.FIRST     ,**a__      :	List[str]     ,)						->       ImageInput:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  do_normalize if do_normalize is not None else self.do_normalize
											_lowerCAmelCase:Union[str, Any]									=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
											_lowerCAmelCase:Tuple									=  patch_size if patch_size is not None else self.patch_size
											_lowerCAmelCase:List[str]									=  max_patches if max_patches is not None else self.max_patches
											_lowerCAmelCase:Union[str, Any]									=  self.is_vqa

											if kwargs.get('''data_format'''     ,a__) is not None:
																raise ValueError('''data_format is not an accepted input as the outputs are ''')

											_lowerCAmelCase:Any									=  make_list_of_images(a__)

											if not valid_images(a__):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.''')

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																_lowerCAmelCase:Union[str, Any]									=  [convert_to_rgb(a__) for image in images]

											# All transformations expect numpy arrays.
											_lowerCAmelCase:Tuple									=  [to_numpy_array(a__) for image in images]

											if is_vqa:
																if header_text is None:
																					raise ValueError('''A header text must be provided for VQA models.''')
																_lowerCAmelCase:Optional[Any]									=  kwargs.pop('''font_bytes'''     ,a__)
																_lowerCAmelCase:Optional[int]									=  kwargs.pop('''font_path'''     ,a__)

																if isinstance(a__     ,a__):
																					_lowerCAmelCase:List[Any]									=  [header_text] * len(a__)

																_lowerCAmelCase:Union[str, Any]									=  [
																    render_header(a__     ,header_text[i]     ,font_bytes=a__     ,font_path=a__)
																    for i, image in enumerate(a__)
																]

											if do_normalize:
																_lowerCAmelCase:Tuple									=  [self.normalize(image=a__) for image in images]

											# convert to torch tensor and permute
											_lowerCAmelCase:List[str]									=  [
											    self.extract_flattened_patches(image=a__     ,max_patches=a__     ,patch_size=a__)
											    for image in images
											]

											# create attention mask in numpy
											_lowerCAmelCase:Dict									=  [(image.sum(axis=-1) != 0).astype(np.floataa) for image in images]

											_lowerCAmelCase:Dict									=  BatchFeature(
											    data={'''flattened_patches''': images, '''attention_mask''': attention_masks}     ,tensor_type=a__)

											return encoded_outputs












"""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   a__			:





						def __init__(							self      :	List[str]     ,a__      :	List[Any]     ,a__      :	List[str]=2     ,a__      :	Optional[Any]=32     ,a__      :	int=16     ,a__      :	Dict=3     ,a__      :	Optional[int]=True     ,a__      :	int=True     ,a__      :	Optional[Any]=32     ,a__      :	str=4     ,a__      :	Tuple=[0, 1, 2, 3]     ,a__      :	Any=4     ,a__      :	int=37     ,a__      :	int="gelu"     ,a__      :	Optional[int]=0.1     ,a__      :	List[Any]=0.1     ,a__      :	Optional[Any]=0.02     ,a__      :	str=3     ,a__      :	str=[1, 384, 24, 24]     ,a__      :	Optional[Any]=True     ,a__      :	Tuple=None     ,)						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  parent
											_lowerCAmelCase:Union[str, Any]									=  batch_size
											_lowerCAmelCase:List[Any]									=  image_size
											_lowerCAmelCase:int									=  patch_size
											_lowerCAmelCase:Optional[int]									=  num_channels
											_lowerCAmelCase:List[str]									=  is_training
											_lowerCAmelCase:int									=  use_labels
											_lowerCAmelCase:Any									=  hidden_size
											_lowerCAmelCase:Any									=  num_hidden_layers
											_lowerCAmelCase:List[Any]									=  backbone_out_indices
											_lowerCAmelCase:int									=  num_attention_heads
											_lowerCAmelCase:Tuple									=  intermediate_size
											_lowerCAmelCase:int									=  hidden_act
											_lowerCAmelCase:Optional[int]									=  hidden_dropout_prob
											_lowerCAmelCase:Union[str, Any]									=  attention_probs_dropout_prob
											_lowerCAmelCase:Any									=  initializer_range
											_lowerCAmelCase:int									=  num_labels
											_lowerCAmelCase:Optional[int]									=  backbone_featmap_shape
											_lowerCAmelCase:List[str]									=  scope
											_lowerCAmelCase:Union[str, Any]									=  is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											_lowerCAmelCase:Any									=  (image_size // patch_size) ** 2
											_lowerCAmelCase:Tuple									=  num_patches + 1





						def       __UpperCamelCase     (							self      :	Tuple)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

											_lowerCAmelCase:Optional[Any]									=  self.get_config()

											return config, pixel_values, labels





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  {
											    '''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=a__     ,initializer_range=self.initializer_range     ,is_hybrid=self.is_hybrid     ,backbone_config=a__     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Tuple     ,a__      :	Tuple     ,a__      :	Tuple)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  DPTModel(config=a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:Optional[Any]									=  model(a__)
											self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))





						def       __UpperCamelCase     (							self      :	str     ,a__      :	Optional[int]     ,a__      :	Union[str, Any]     ,a__      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  self.num_labels
											_lowerCAmelCase:List[Any]									=  DPTForDepthEstimation(a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:int									=  model(a__)
											self.parent.assertEqual(result.predicted_depth.shape     ,(self.batch_size, self.image_size, self.image_size))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Dict     ,a__      :	List[Any]     ,a__      :	str)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.num_labels
											_lowerCAmelCase:str									=  DPTForSemanticSegmentation(a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:int									=  model(a__     ,labels=a__)
											self.parent.assertEqual(
											    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size, self.image_size))







						def       __UpperCamelCase     (							self      :	Optional[int])						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Any									=  config_and_inputs
											_lowerCAmelCase:Union[str, Any]									=  {'''pixel_values''': pixel_values}
											return config, inputs_dict




@require_torch
class   a__			(       UpperCamelCase_					,	UpperCamelCase_					,	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      :	Optional[Any])						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  DPTModelTester(self)
											_lowerCAmelCase:List[str]									=  ConfigTester(self     ,config_class=a__     ,has_text_modality=a__     ,hidden_size=37)





						def       __UpperCamelCase     (							self      :	str)						->       Optional[int]:




											"""simple docstring"""







											self.config_tester.run_common_tests()





						@unittest.skip(reason='''DPT does not use inputs_embeds''')
						def       __UpperCamelCase     (							self      :	Optional[int])						->       Dict:




											"""simple docstring"""







											pass





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:Union[str, Any]									=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																_lowerCAmelCase:Union[str, Any]									=  model_class(a__)
																self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module))
																_lowerCAmelCase:Optional[int]									=  model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(a__     ,nn.Linear))





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:Any									=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																_lowerCAmelCase:int									=  model_class(a__)
																_lowerCAmelCase:Union[str, Any]									=  inspect.signature(model.forward)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_lowerCAmelCase:Any									=  [*signature.parameters.keys()]

																_lowerCAmelCase:Any									=  ['''pixel_values''']
																self.assertListEqual(arg_names[:1]     ,a__)





						def       __UpperCamelCase     (							self      :	Tuple)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*a__)





						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







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

																_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  self.model_tester.prepare_config_and_inputs_for_common()
																_lowerCAmelCase:Union[str, Any]									=  True

																if model_class in get_values(a__):
																					continue

																_lowerCAmelCase:Union[str, Any]									=  model_class(a__)
																model.to(a__)
																model.train()
																_lowerCAmelCase:Optional[int]									=  self._prepare_for_class(a__     ,a__     ,return_labels=a__)
																_lowerCAmelCase:Union[str, Any]									=  model(**a__).loss
																loss.backward()





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[str]:




											"""simple docstring"""







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

																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
																_lowerCAmelCase:str									=  False
																_lowerCAmelCase:str									=  True

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





						def       __UpperCamelCase     (							self      :	Any)						->       int:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()

											_lowerCAmelCase:Dict									=  _config_zero_init(a__)
											for model_class in self.all_model_classes:
																_lowerCAmelCase:Tuple									=  model_class(config=a__)
																# Skip the check for the backbone
																_lowerCAmelCase:Tuple									=  []
																for name, module in model.named_modules():
																					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																										_lowerCAmelCase:Tuple									=  [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      :	Dict)						->       Any:




											"""simple docstring"""







											pass





						@slow
						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







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







						def       __UpperCamelCase     (							self      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCAmelCase:Any									=  '''add'''
											with self.assertRaises(a__):
																_lowerCAmelCase:Any									=  DPTForDepthEstimation(a__)





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Union[str, Any]									=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
					return image





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





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
											_lowerCAmelCase:Optional[int]									=  DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(a__)

											_lowerCAmelCase:Tuple									=  prepare_img()
											_lowerCAmelCase:List[str]									=  image_processor(images=a__     ,return_tensors='''pt''').to(a__)

											# forward pass
											with torch.no_grad():
																_lowerCAmelCase:Dict									=  model(**a__)
																_lowerCAmelCase:List[str]									=  outputs.predicted_depth

											# verify the predicted depth
											_lowerCAmelCase:str									=  torch.Size((1, 384, 384))
											self.assertEqual(predicted_depth.shape     ,a__)

											_lowerCAmelCase:Any									=  torch.tensor(
											    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]).to(a__)

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






