





import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =SwinaSRConfig()

   if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      UpperCAmelCase_           =4
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      UpperCAmelCase_           =4
      UpperCAmelCase_           =4_8
      UpperCAmelCase_           ="pixelshuffle_aux"
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      UpperCAmelCase_           =[6, 6, 6, 6]
      UpperCAmelCase_           =6_0
      UpperCAmelCase_           =[6, 6, 6, 6]
      UpperCAmelCase_           ="pixelshuffledirect"
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      UpperCAmelCase_           =4
      UpperCAmelCase_           ="nearest+conv"
   elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
      UpperCAmelCase_           =1
      UpperCAmelCase_           =1
      UpperCAmelCase_           =1_2_6
      UpperCAmelCase_           =7
      UpperCAmelCase_           =255.0
      UpperCAmelCase_           =""

   return config




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if "patch_embed.proj" in name and "layers" not in name:
      UpperCAmelCase_           =name.replace("patch_embed.proj"	,     "embeddings.patch_embeddings.projection"	)
   if "patch_embed.norm" in name:
      UpperCAmelCase_           =name.replace("patch_embed.norm"	,     "embeddings.patch_embeddings.layernorm"	)
   if "layers" in name:
      UpperCAmelCase_           =name.replace("layers"	,     "encoder.stages"	)
   if "residual_group.blocks" in name:
      UpperCAmelCase_           =name.replace("residual_group.blocks"	,     "layers"	)
   if "attn.proj" in name:
      UpperCAmelCase_           =name.replace("attn.proj"	,     "attention.output.dense"	)
   if "attn" in name:
      UpperCAmelCase_           =name.replace("attn"	,     "attention.self"	)
   if "norm1" in name:
      UpperCAmelCase_           =name.replace("norm1"	,     "layernorm_before"	)
   if "norm2" in name:
      UpperCAmelCase_           =name.replace("norm2"	,     "layernorm_after"	)
   if "mlp.fc1" in name:
      UpperCAmelCase_           =name.replace("mlp.fc1"	,     "intermediate.dense"	)
   if "mlp.fc2" in name:
      UpperCAmelCase_           =name.replace("mlp.fc2"	,     "output.dense"	)
   if "q_bias" in name:
      UpperCAmelCase_           =name.replace("q_bias"	,     "query.bias"	)
   if "k_bias" in name:
      UpperCAmelCase_           =name.replace("k_bias"	,     "key.bias"	)
   if "v_bias" in name:
      UpperCAmelCase_           =name.replace("v_bias"	,     "value.bias"	)
   if "cpb_mlp" in name:
      UpperCAmelCase_           =name.replace("cpb_mlp"	,     "continuous_position_bias_mlp"	)
   if "patch_embed.proj" in name:
      UpperCAmelCase_           =name.replace("patch_embed.proj"	,     "patch_embed.projection"	)

   if name == "norm.weight":
      UpperCAmelCase_           ="layernorm.weight"
   if name == "norm.bias":
      UpperCAmelCase_           ="layernorm.bias"

   if "conv_first" in name:
      UpperCAmelCase_           =name.replace("conv_first"	,     "first_convolution"	)

   if (
       "upsample" in name
       or "conv_before_upsample" in name
       or "conv_bicubic" in name
       or "conv_up" in name
       or "conv_hr" in name
       or "conv_last" in name
       or "aux" in name
   ):
      # heads
      if "conv_last" in name:
         UpperCAmelCase_           =name.replace("conv_last"	,     "final_convolution"	)
      if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
         if "conv_before_upsample.0" in name:
            UpperCAmelCase_           =name.replace("conv_before_upsample.0"	,     "conv_before_upsample"	)
         if "upsample.0" in name:
            UpperCAmelCase_           =name.replace("upsample.0"	,     "upsample.convolution_0"	)
         if "upsample.2" in name:
            UpperCAmelCase_           =name.replace("upsample.2"	,     "upsample.convolution_1"	)
         UpperCAmelCase_           ="upsample." + name
      elif config.upsampler == "pixelshuffledirect":
         UpperCAmelCase_           =name.replace("upsample.0.weight"	,     "upsample.conv.weight"	)
         UpperCAmelCase_           =name.replace("upsample.0.bias"	,     "upsample.conv.bias"	)
      else:
         pass
   else:
      UpperCAmelCase_           ="swin2sr." + name

   return name




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   for key in orig_state_dict.copy().keys():
      UpperCAmelCase_           =orig_state_dict.pop(lowercase__	)

      if "qkv" in key:
         UpperCAmelCase_           =key.split("."	)
         UpperCAmelCase_           =int(key_split[1]	)
         UpperCAmelCase_           =int(key_split[4]	)
         UpperCAmelCase_           =config.embed_dim

         if "weight" in key:
            UpperCAmelCase_           =val[:dim, :]
            UpperCAmelCase_           =val[dim : dim * 2, :]
            UpperCAmelCase_           =val[-dim:, :]
         else:
            UpperCAmelCase_           =val[:dim]
            UpperCAmelCase_           =val[dim : dim * 2]
            UpperCAmelCase_           =val[-dim:]
         pass
      else:
         UpperCAmelCase_           =val

   return orig_state_dict




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =get_config(lowercase__	)
   UpperCAmelCase_           =SwinaSRForImageSuperResolution(lowercase__	)
   model.eval()

   UpperCAmelCase_           =torch.hub.load_state_dict_from_url(lowercase__	,     map_location="cpu"	)
   UpperCAmelCase_           =convert_state_dict(lowercase__	,     lowercase__	)
   UpperCAmelCase_    ,							UpperCAmelCase_           =model.load_state_dict(lowercase__	,     strict=lowercase__	)

   if len(lowercase__	) > 0:
      raise ValueError("Missing keys when converting: {}".format(lowercase__	)	)
   for key in unexpected_keys:
      if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
         raise ValueError(F'Unexpected key {key} in state_dict'	)

    # verify values
   UpperCAmelCase_           ="https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
   UpperCAmelCase_           =Image.open(requests.get(lowercase__	,     stream=lowercase__	).raw	).convert("RGB"	)
   UpperCAmelCase_           =SwinaSRImageProcessor()
   # pixel_values = processor(image, return_tensors="pt").pixel_values

   UpperCAmelCase_           =1_2_6 if "Jpeg" in checkpoint_url else 2_5_6
   UpperCAmelCase_           =Compose(
       [
           Resize((image_size, image_size)	),
           ToTensor(),
           Normalize(mean=[0.485, 0.456, 0.406]	,     std=[0.229, 0.224, 0.225]	),
       ]	)
   UpperCAmelCase_           =transforms(lowercase__	).unsqueeze(0	)

   if config.num_channels == 1:
      UpperCAmelCase_           =pixel_values[:, 0, :, :].unsqueeze(1	)

   UpperCAmelCase_           =model(lowercase__	)

   # assert values
   if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
      UpperCAmelCase_           =torch.Size([1, 3, 5_1_2, 5_1_2]	)
      UpperCAmelCase_           =torch.tensor(
          [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]	)
   elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
      UpperCAmelCase_           =torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
      UpperCAmelCase_           =torch.tensor(
          [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]	)
   elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
      # TODO values didn't match exactly here
      UpperCAmelCase_           =torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
      UpperCAmelCase_           =torch.tensor(
          [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]	)
   elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
      UpperCAmelCase_           =torch.Size([1, 3, 5_1_2, 5_1_2]	)
      UpperCAmelCase_           =torch.tensor(
          [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]	)
   elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
      UpperCAmelCase_           =torch.Size([1, 3, 1_0_2_4, 1_0_2_4]	)
      UpperCAmelCase_           =torch.tensor(
          [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]	)

   assert (
       outputs.reconstruction.shape == expected_shape
   ), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
   assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]	,     lowercase__	,     atol=1E-3	)
   print("Looks ok!"	)

   UpperCAmelCase_           ={
       "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
           "swin2SR-classical-sr-x2-64"
       ),
       "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
           "swin2SR-classical-sr-x4-64"
       ),
       "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
           "swin2SR-compressed-sr-x4-48"
       ),
       "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
           "swin2SR-lightweight-x2-64"
       ),
       "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
           "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
       ),
   }
   UpperCAmelCase_           =url_to_name[checkpoint_url]

   if pytorch_dump_folder_path is not None:
      print(F'Saving model {model_name} to {pytorch_dump_folder_path}'	)
      model.save_pretrained(lowercase__	)
      print(F'Saving image processor to {pytorch_dump_folder_path}'	)
      processor.save_pretrained(lowercase__	)

   if push_to_hub:
      model.push_to_hub(F'caidas/{model_name}'	)
      processor.push_to_hub(F'caidas/{model_name}'	)


if __name__ == "__main__":
     __lowercase	: Dict       =argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--checkpoint_url""",
         default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
         type=str,
         help="""URL of the original Swin2SR checkpoint you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
     )
     parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the converted model to the hub.""")

     __lowercase	: str       =parser.parse_args()
     convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








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 							lowerCamelCase   :


	def __init__(    self				,     lowercase__				,     lowercase__=2				,     lowercase__=3_2				,     lowercase__=1_6				,     lowercase__=3				,     lowercase__=True				,     lowercase__=True				,     lowercase__=3_2				,     lowercase__=4				,     lowercase__=[0, 1, 2, 3]				,     lowercase__=4				,     lowercase__=3_7				,     lowercase__="gelu"				,     lowercase__=0.1				,     lowercase__=0.1				,     lowercase__=0.0_2				,     lowercase__=3				,     lowercase__=[1, 3_8_4, 2_4, 2_4]				,     lowercase__=True				,     lowercase__=None				,     ):
							__UpperCAmelCase			:							Any	    =	parent
							__UpperCAmelCase			:							Tuple	    =	batch_size
							__UpperCAmelCase			:							Optional[int]	    =	image_size
							__UpperCAmelCase			:							Tuple	    =	patch_size
							__UpperCAmelCase			:							int	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	is_training
							__UpperCAmelCase			:							Union[str, Any]	    =	use_labels
							__UpperCAmelCase			:							List[Any]	    =	hidden_size
							__UpperCAmelCase			:							Optional[Any]	    =	num_hidden_layers
							__UpperCAmelCase			:							Any	    =	backbone_out_indices
							__UpperCAmelCase			:							Union[str, Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Dict	    =	intermediate_size
							__UpperCAmelCase			:							Dict	    =	hidden_act
							__UpperCAmelCase			:							Dict	    =	hidden_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	attention_probs_dropout_prob
							__UpperCAmelCase			:							List[Any]	    =	initializer_range
							__UpperCAmelCase			:							Union[str, Any]	    =	num_labels
							__UpperCAmelCase			:							List[Any]	    =	backbone_featmap_shape
							__UpperCAmelCase			:							Any	    =	scope
							__UpperCAmelCase			:							Optional[int]	    =	is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							__UpperCAmelCase			:							Optional[Any]	    =	(image_size // patch_size) ** 2
							__UpperCAmelCase			:							Any	    =	num_patches + 1


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							__UpperCAmelCase			:							List[str]	    =	None
							if self.use_labels:
													__UpperCAmelCase			:							Dict	    =	ids_tensor([self.batch_size, self.image_size, self.image_size]				,     self.num_labels)

							__UpperCAmelCase			:							Dict	    =	self.get_config()

							return config, pixel_values, labels


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	{
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [9_6, 1_9_2, 3_8_4, 7_6_8],
							    '''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  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	DPTModel(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							Dict	    =	model(lowercase__)
							self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	self.num_labels
							__UpperCAmelCase			:							Optional[Any]	    =	DPTForDepthEstimation(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							str	    =	model(lowercase__)
							self.parent.assertEqual(result.predicted_depth.shape				,     (self.batch_size, self.image_size, self.image_size))


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	self.num_labels
							__UpperCAmelCase			:							Optional[int]	    =	DPTForSemanticSegmentation(lowercase__)
							model.to(lowercase__)
							model.eval()
							__UpperCAmelCase			:							str	    =	model(lowercase__				,     labels=lowercase__)
							self.parent.assertEqual(
							    result.logits.shape				,     (self.batch_size, self.num_labels, self.image_size, self.image_size))


	def  A(    self):
							__UpperCAmelCase			:							int	    =	self.prepare_config_and_inputs()
							__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	config_and_inputs
							__UpperCAmelCase			:							Union[str, Any]	    =	{'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Union[str, Any]										=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	_lowerCAmelCase     :		Optional[int]										=						(
	    {
	        '''depth-estimation''': DPTForDepthEstimation,
	        '''feature-extraction''': DPTModel,
	        '''image-segmentation''': DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	_lowerCAmelCase     :		Any										=						False
	_lowerCAmelCase     :		str										=						False
	_lowerCAmelCase     :		List[Any]										=						False


	def  A(    self):
							__UpperCAmelCase			:							Any	    =	DPTModelTester(self)
							__UpperCAmelCase			:							str	    =	ConfigTester(self				,     config_class=lowercase__				,     has_text_modality=lowercase__				,     hidden_size=3_7)


	def  A(    self):
							self.config_tester.run_common_tests()


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


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	self.model_tester.prepare_config_and_inputs_for_common()

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


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Any	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

													__UpperCAmelCase			:							str	    =	['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							List[str]	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__)


	def  A(    self):
							for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																			continue

													__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCAmelCase			:							Any	    =	True

													if model_class in get_values(lowercase__):
																			continue

													__UpperCAmelCase			:							List[Any]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.train()
													__UpperCAmelCase			:							Any	    =	self._prepare_for_class(lowercase__				,     lowercase__				,     return_labels=lowercase__)
													__UpperCAmelCase			:							Any	    =	model(**lowercase__).loss
													loss.backward()


	def  A(    self):
							for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																			continue

													__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCAmelCase			:							Optional[int]	    =	False
													__UpperCAmelCase			:							str	    =	True

													if model_class in get_values(lowercase__) or not model_class.supports_gradient_checkpointing:
																			continue
													__UpperCAmelCase			:							Tuple	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.gradient_checkpointing_enable()
													model.train()
													__UpperCAmelCase			:							Tuple	    =	self._prepare_for_class(lowercase__				,     lowercase__				,     return_labels=lowercase__)
													__UpperCAmelCase			:							Union[str, Any]	    =	model(**lowercase__).loss
													loss.backward()


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs_for_common()

							__UpperCAmelCase			:							List[Any]	    =	_config_zero_init(lowercase__)
							for model_class in self.all_model_classes:
													__UpperCAmelCase			:							Tuple	    =	model_class(config=lowercase__)
													# Skip the check for the backbone
													__UpperCAmelCase			:							List[Any]	    =	[]
													for name, module in model.named_modules():
																			if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																									__UpperCAmelCase			:							Optional[Any]	    =	[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  A(    self):
							pass


	@slow
	def  A(    self):
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
													__UpperCAmelCase			:							Optional[int]	    =	DPTModel.from_pretrained(lowercase__)
													self.assertIsNotNone(lowercase__)


	def  A(    self):
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Tuple	    =	self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase			:							List[str]	    =	'''add'''
							with self.assertRaises(lowercase__):
													__UpperCAmelCase			:							Optional[Any]	    =	DPTForDepthEstimation(lowercase__)






def     __SCREAMING_SNAKE_CASE							(       )		->     Dict:




						'''simple docstring'''







						__UpperCAmelCase			:							str	    =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
						return image





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


	def  A(    self):
							__UpperCAmelCase			:							str	    =	DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
							__UpperCAmelCase			:							str	    =	DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(lowercase__)

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

							# forward pass
							with torch.no_grad():
													__UpperCAmelCase			:							List[Any]	    =	model(**lowercase__)
													__UpperCAmelCase			:							str	    =	outputs.predicted_depth

							# verify the predicted depth
							__UpperCAmelCase			:							Union[str, Any]	    =	torch.Size((1, 3_8_4, 3_8_4))
							self.assertEqual(predicted_depth.shape				,     lowercase__)

							__UpperCAmelCase			:							List[str]	    =	torch.tensor(
							    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]).to(lowercase__)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0				,     lowercase__				,     atol=1e-4))

