

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					StableUnCLIPImgaImgPipeline
   UpperCamelCase       					=					TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   UpperCamelCase       					=					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCamelCase       					=					frozenset(
       []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   UpperCamelCase       					=					frozenset([]						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Any						=			3_2
     UpperCAmelCase	:							List[str]						=			embedder_hidden_size

     # image encoding components

     UpperCAmelCase	:							Union[str, Any]						=			CLIPImageProcessor(crop_size=3_2,		size=3_2						)

     torch.manual_seed(0						)
     UpperCAmelCase	:							List[Any]						=			CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=__A,		projection_dim=__A,		num_hidden_layers=5,		num_attention_heads=4,		image_size=3_2,		intermediate_size=3_7,		patch_size=1,		)						)

     # regular denoising components

     torch.manual_seed(0						)
     UpperCAmelCase	:							Any						=			StableUnCLIPImageNormalizer(embedding_dim=__A						)
     UpperCAmelCase	:							Tuple						=			DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''						)

     torch.manual_seed(0						)
     UpperCAmelCase	:							Tuple						=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

     torch.manual_seed(0						)
     UpperCAmelCase	:							str						=			CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0,		eos_token_id=2,		hidden_size=__A,		projection_dim=3_2,		intermediate_size=3_7,		layer_norm_eps=1E-05,		num_attention_heads=4,		num_hidden_layers=5,		pad_token_id=1,		vocab_size=1_0_0_0,		)						)

     torch.manual_seed(0						)
     UpperCAmelCase	:							str						=			UNetaDConditionModel(
         sample_size=3_2,		in_channels=4,		out_channels=4,		down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D'''),		up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D'''),		block_out_channels=(3_2, 6_4),		attention_head_dim=(2, 4),		class_embed_type='''projection''',		projection_class_embeddings_input_dim=embedder_projection_dim * 2,		cross_attention_dim=__A,		layers_per_block=1,		upcast_attention=__A,		use_linear_projection=__A,		)

     torch.manual_seed(0						)
     UpperCAmelCase	:							Tuple						=			DDIMScheduler(
         beta_schedule='''scaled_linear''',		beta_start=0.0_0_0_8_5,		beta_end=0.0_1_2,		prediction_type='''v_prediction''',		set_alpha_to_one=__A,		steps_offset=1,		)

     torch.manual_seed(0						)
     UpperCAmelCase	:							int						=			AutoencoderKL()

     UpperCAmelCase	:							List[Any]						=			{
         # image encoding components
         '''feature_extractor''': feature_extractor,
         '''image_encoder''': image_encoder.eval(),
         # image noising components
         '''image_normalizer''': image_normalizer.eval(),
         '''image_noising_scheduler''': image_noising_scheduler,
         # regular denoising components
         '''tokenizer''': tokenizer,
         '''text_encoder''': text_encoder.eval(),
         '''unet''': unet.eval(),
         '''scheduler''': scheduler,
         '''vae''': vae.eval(),
     }

     return components



   def 	__magic_name__   (							self							:					List[str],		__A							:					Optional[int],		__A							:					Optional[Any]=0,		__A							:					int=True						):
     if str(__A						).startswith('''mps'''						):
       UpperCAmelCase	:							Tuple						=			torch.manual_seed(__A						)
     else:
       UpperCAmelCase	:							List[str]						=			torch.Generator(device=__A						).manual_seed(__A						)

     UpperCAmelCase	:							Any						=			floats_tensor((1, 3, 3_2, 3_2),		rng=random.Random(__A						)						).to(__A						)

     if pil_image:
       UpperCAmelCase	:							Optional[int]						=			input_image * 0.5 + 0.5
       UpperCAmelCase	:							Tuple						=			input_image.clamp(0,		1						)
       UpperCAmelCase	:							List[Any]						=			input_image.cpu().permute(0,		2,		3,		1						).float().numpy()
       UpperCAmelCase	:							Any						=			DiffusionPipeline.numpy_to_pil(__A						)[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }



   @skip_mps
   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							str						=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
     UpperCAmelCase	:							Dict						=			self.get_dummy_components()
     UpperCAmelCase	:							int						=			StableUnCLIPImgaImgPipeline(**__A						)
     UpperCAmelCase	:							List[Any]						=			sd_pipe.to(__A						)
     sd_pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							List[str]						=			self.get_dummy_inputs(__A						)
     inputs.update({'''image_embeds''': None}						)
     UpperCAmelCase	:							Union[str, Any]						=			sd_pipe(**__A						).images
     UpperCAmelCase	:							Any						=			image[0, -3:, -3:, -1]

     assert image.shape == (1, 3_2, 3_2, 3)
     UpperCAmelCase	:							int						=			np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]						)

     assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							List[str]						=			torch_device in ['''cpu''', '''mps''']

     self._test_attention_slicing_forward_pass(test_max_difference=__A						)



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							Dict						=			torch_device in ['''cpu''', '''mps''']

     self._test_inference_batch_single_identical(test_max_difference=__A						)





   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available(),		reason='''XFormers attention is only available with CUDA and `xformers` installed''',		)
   def 	__magic_name__   (							self							:					List[str]						):
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__A						)






@slow
@require_torch_gpu
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					Any						):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Tuple						=			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''						)

     UpperCAmelCase	:							Dict						=			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''						)

     UpperCAmelCase	:							List[str]						=			StableUnCLIPImgaImgPipeline.from_pretrained(
         '''fusing/stable-unclip-2-1-l-img2img''',		torch_dtype=torch.floataa						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     UpperCAmelCase	:							Tuple						=			torch.Generator(device='''cpu'''						).manual_seed(0						)
     UpperCAmelCase	:							Union[str, Any]						=			pipe(__A,		'''anime turle''',		generator=__A,		output_type='''np'''						)

     UpperCAmelCase	:							Optional[Any]						=			output.images[0]

     assert image.shape == (7_6_8, 7_6_8, 3)

     assert_mean_pixel_difference(__A,		__A						)



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							int						=			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''						)

     UpperCAmelCase	:							Optional[int]						=			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''						)

     UpperCAmelCase	:							Tuple						=			StableUnCLIPImgaImgPipeline.from_pretrained(
         '''fusing/stable-unclip-2-1-h-img2img''',		torch_dtype=torch.floataa						)
     pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     UpperCAmelCase	:							List[Any]						=			torch.Generator(device='''cpu'''						).manual_seed(0						)
     UpperCAmelCase	:							Any						=			pipe(__A,		'''anime turle''',		generator=__A,		output_type='''np'''						)

     UpperCAmelCase	:							List[Any]						=			output.images[0]

     assert image.shape == (7_6_8, 7_6_8, 3)

     assert_mean_pixel_difference(__A,		__A						)





   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Optional[int]						=			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''						)

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     UpperCAmelCase	:							Any						=			StableUnCLIPImgaImgPipeline.from_pretrained(
         '''fusing/stable-unclip-2-1-h-img2img''',		torch_dtype=torch.floataa						)
     UpperCAmelCase	:							Tuple						=			pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     UpperCAmelCase	:							List[Any]						=			pipe(
         __A,		'''anime turtle''',		num_inference_steps=2,		output_type='''np''',		)

     UpperCAmelCase	:							Any						=			torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 1_0**9








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  __UpperCAmelCase					:



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



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Optional[Any]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     UpperCAmelCase	:							Union[str, Any]						=			None
     if self.use_labels:
       UpperCAmelCase	:							List[Any]						=			ids_tensor([self.batch_size, self.image_size, self.image_size],		self.num_labels						)

     UpperCAmelCase	:							Tuple						=			self.get_config()

     return config, pixel_values, labels



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							List[Any]						=			{
         '''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=__A,		initializer_range=self.initializer_range,		is_hybrid=self.is_hybrid,		backbone_config=__A,		backbone_featmap_shape=self.backbone_featmap_shape,		)



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any],		__A							:					Union[str, Any],		__A							:					Tuple						):
     UpperCAmelCase	:							Optional[Any]						=			DPTModel(config=__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							int						=			model(__A						)
     self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)						)



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					Any,		__A							:					Dict,		__A							:					Optional[int]						):
     UpperCAmelCase	:							Optional[Any]						=			self.num_labels
     UpperCAmelCase	:							List[Any]						=			DPTForDepthEstimation(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Tuple						=			model(__A						)
     self.parent.assertEqual(result.predicted_depth.shape,		(self.batch_size, self.image_size, self.image_size)						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Dict,		__A							:					List[Any],		__A							:					Optional[int]						):
     UpperCAmelCase	:							Dict						=			self.num_labels
     UpperCAmelCase	:							Tuple						=			DPTForSemanticSegmentation(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Dict						=			model(__A,		labels=__A						)
     self.parent.assertEqual(
         result.logits.shape,		(self.batch_size, self.num_labels, self.image_size, self.image_size)						)





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






@require_torch
class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__      ,  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 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							int						=			DPTModelTester(self						)
     UpperCAmelCase	:							List[Any]						=			ConfigTester(self,		config_class=__A,		has_text_modality=__A,		hidden_size=3_7						)



   def 	__magic_name__   (							self							:					Any						):
     self.config_tester.run_common_tests()



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



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()

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



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase	:							Dict						=			['''pixel_values''']
       self.assertListEqual(arg_names[:1],		__A						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							int						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*__A						)



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							List[str]						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*__A						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
         continue

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

       if model_class in get_values(__A						):
         continue

       UpperCAmelCase	:							Union[str, Any]						=			model_class(__A						)
       model.to(__A						)
       model.train()
       UpperCAmelCase	:							str						=			self._prepare_for_class(__A,		__A,		return_labels=__A						)
       UpperCAmelCase	:							Union[str, Any]						=			model(**__A						).loss
       loss.backward()



   def 	__magic_name__   (							self							:					Optional[int]						):
     for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
         continue

       UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase	:							int						=			False
       UpperCAmelCase	:							int						=			True

       if model_class in get_values(__A						) or not model_class.supports_gradient_checkpointing:
         continue
       UpperCAmelCase	:							Dict						=			model_class(__A						)
       model.to(__A						)
       model.gradient_checkpointing_enable()
       model.train()
       UpperCAmelCase	:							List[str]						=			self._prepare_for_class(__A,		__A,		return_labels=__A						)
       UpperCAmelCase	:							Any						=			model(**__A						).loss
       loss.backward()



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

     UpperCAmelCase	:							Optional[Any]						=			_config_zero_init(__A						)
     for model_class in self.all_model_classes:
       UpperCAmelCase	:							Dict						=			model_class(config=__A						)
       # Skip the check for the backbone
       UpperCAmelCase	:							Dict						=			[]
       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 	__magic_name__   (							self							:					Optional[int]						):
     pass



   @slow
   def 	__magic_name__   (							self							:					Optional[Any]						):
     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       UpperCAmelCase	:							Optional[int]						=			DPTModel.from_pretrained(__A						)
       self.assertIsNotNone(__A						)





   def 	__magic_name__   (							self							:					int						):
     # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase	:							int						=			'''add'''
     with self.assertRaises(__A						):
       UpperCAmelCase	:							Dict						=			DPTForDepthEstimation(__A						)






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






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



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Dict						=			DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''						)
     UpperCAmelCase	:							Tuple						=			DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''						).to(__A						)

     UpperCAmelCase	:							List[Any]						=			prepare_img()
     UpperCAmelCase	:							Union[str, Any]						=			image_processor(images=__A,		return_tensors='''pt'''						).to(__A						)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase	:							int						=			model(**__A						)
       UpperCAmelCase	:							int						=			outputs.predicted_depth

     # verify the predicted depth
     UpperCAmelCase	:							Tuple						=			torch.Size((1, 3_8_4, 3_8_4)						)
     self.assertEqual(predicted_depth.shape,		__A						)

     UpperCAmelCase	:							Dict						=			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(__A						)

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





