
"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_A            = logging.get_logger(__name__)  # pylint: disable=invalid-name

_A            = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'
def 		UpperCAmelCase					(		a_,						a_,						a_=8			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCamelCase     :			List[str]		=    width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor
def 		UpperCAmelCase					(		a_,						a_=512,						a_=512			):





		'''simple docstring'''


		lowerCamelCase     :			Union[str, Any]		=    pil_image.resize((w, h),						resample=Image.BICUBIC,						reducing_gap=1			)
		lowerCamelCase     :			Optional[Any]		=    np.array(pil_image.convert('RGB'			)			)
		lowerCamelCase     :			str		=    arr.astype(np.floataa			) / 1_2_7.5 - 1
		lowerCamelCase     :			Tuple		=    np.transpose(a_,						[2, 0, 1]			)
		lowerCamelCase     :			str		=    torch.from_numpy(a_			).unsqueeze(0			)
		return image






class        _lowercase   (				__UpperCAmelCase		):




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, )		->							int:
			super().__init__()

			self.register_modules(
			    unet=UpperCAmelCase_		, scheduler=UpperCAmelCase_		, movq=UpperCAmelCase_		, )
			lowerCamelCase     :			Optional[Any]		=    2 ** (len(self.movq.config.block_out_channels					) - 1)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			# get the original timestep using init_timestep
			lowerCamelCase     :			str		=    min(int(num_inference_steps * strength					)		, UpperCAmelCase_					)

			lowerCamelCase     :			List[str]		=    max(num_inference_steps - init_timestep		, 0					)
			lowerCamelCase     :			Tuple		=    self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_=None					)		->							Optional[Any]:
			if not isinstance(UpperCAmelCase_		, (torch.Tensor, PIL.Image.Image, list)					):
					raise ValueError(
					    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_					)}"""					)

			lowerCamelCase     :			Tuple		=    image.to(device=UpperCAmelCase_		, dtype=UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					lowerCamelCase     :			List[str]		=    image

			else:
					if isinstance(UpperCAmelCase_		, UpperCAmelCase_					) and len(UpperCAmelCase_					) != batch_size:
							raise ValueError(
							    F"""You have passed a list of generators of length {len(UpperCAmelCase_					)}, but requested an effective batch"""
							    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

					elif isinstance(UpperCAmelCase_		, UpperCAmelCase_					):
							lowerCamelCase     :			List[Any]		=    [
							    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(UpperCAmelCase_					)
							]
							lowerCamelCase     :			str		=    torch.cat(UpperCAmelCase_		, dim=0					)
					else:
							lowerCamelCase     :			List[str]		=    self.movq.encode(UpperCAmelCase_					).latent_dist.sample(UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    self.movq.config.scaling_factor * init_latents

			lowerCamelCase     :			List[str]		=    torch.cat([init_latents]		, dim=0					)

			lowerCamelCase     :			Union[str, Any]		=    init_latents.shape
			lowerCamelCase     :			Dict		=    randn_tensor(UpperCAmelCase_		, generator=UpperCAmelCase_		, device=UpperCAmelCase_		, dtype=UpperCAmelCase_					)

			# get latents
			lowerCamelCase     :			Optional[int]		=    self.scheduler.add_noise(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)

			lowerCamelCase     :			List[str]		=    init_latents

			return latents




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=0					)		->							List[str]:
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('Please install accelerate via `pip install accelerate`'					)

			lowerCamelCase     :			List[Any]		=    torch.device(F"""cuda:{gpu_id}"""					)

			lowerCamelCase     :			Dict		=    [
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(UpperCAmelCase_		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_=0					)		->							Dict:
			if is_accelerate_available() and is_accelerate_version('>='		, '0.17.0.dev0'					):
					from accelerate import cpu_offload_with_hook
			else:
					raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'					)

			lowerCamelCase     :			Dict		=    torch.device(F"""cuda:{gpu_id}"""					)

			if self.device.type != "cpu":
					self.to('cpu'		, silence_dtype_warnings=UpperCAmelCase_					)
					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			lowerCamelCase     :			Optional[int]		=    None
			for cpu_offloaded_model in [self.unet, self.movq]:
					lowerCamelCase	,    lowerCamelCase     :			List[str]		=    cpu_offload_with_hook(UpperCAmelCase_		, UpperCAmelCase_		, prev_module_hook=UpperCAmelCase_					)

			# We'll offload the last model manually.
			lowerCamelCase     :			Optional[Any]		=    hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			if not hasattr(self.unet		, '_hf_hook'					):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(UpperCAmelCase_		, '_hf_hook'					)
					    and hasattr(module._hf_hook		, 'execution_device'					)
					    and module._hf_hook.execution_device is not None
					):
							return torch.device(module._hf_hook.execution_device					)
			return self.device




	@torch.no_grad()
	@replace_example_docstring(UpperCAmelCase_					)
	def __call__( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_ = 512		, UpperCAmelCase_ = 512		, UpperCAmelCase_ = 100		, UpperCAmelCase_ = 4.0		, UpperCAmelCase_ = 0.3		, UpperCAmelCase_ = 1		, UpperCAmelCase_ = None		, UpperCAmelCase_ = "pil"		, UpperCAmelCase_ = True		, )		->							str:
			lowerCamelCase     :			List[str]		=    self._execution_device

			lowerCamelCase     :			Tuple		=    guidance_scale > 1.0

			if isinstance(UpperCAmelCase_		, UpperCAmelCase_					):
					lowerCamelCase     :			int		=    torch.cat(UpperCAmelCase_		, dim=0					)
			if isinstance(UpperCAmelCase_		, UpperCAmelCase_					):
					lowerCamelCase     :			Tuple		=    torch.cat(UpperCAmelCase_		, dim=0					)
			if isinstance(UpperCAmelCase_		, UpperCAmelCase_					):
					lowerCamelCase     :			Union[str, Any]		=    torch.cat(UpperCAmelCase_		, dim=0					)

			lowerCamelCase     :			str		=    image_embeds.shape[0]

			if do_classifier_free_guidance:
					lowerCamelCase     :			Dict		=    image_embeds.repeat_interleave(UpperCAmelCase_		, dim=0					)
					lowerCamelCase     :			int		=    negative_image_embeds.repeat_interleave(UpperCAmelCase_		, dim=0					)
					lowerCamelCase     :			str		=    hint.repeat_interleave(UpperCAmelCase_		, dim=0					)

			lowerCamelCase     :			int		=    torch.cat([negative_image_embeds, image_embeds]		, dim=0					).to(dtype=self.unet.dtype		, device=UpperCAmelCase_					)
			lowerCamelCase     :			Optional[Any]		=    torch.cat([hint, hint]		, dim=0					).to(dtype=self.unet.dtype		, device=UpperCAmelCase_					)

			if not isinstance(UpperCAmelCase_		, UpperCAmelCase_					):
					lowerCamelCase     :			Tuple		=    [image]
			if not all(isinstance(UpperCAmelCase_		, (PIL.Image.Image, torch.Tensor)					) for i in image					):
					raise ValueError(
					    F"""Input is in incorrect format: {[type(UpperCAmelCase_					) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""					)

			lowerCamelCase     :			Union[str, Any]		=    torch.cat([prepare_image(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					) for i in image]		, dim=0					)
			lowerCamelCase     :			Tuple		=    image.to(dtype=image_embeds.dtype		, device=UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    self.movq.encode(UpperCAmelCase_					)['latents']
			lowerCamelCase     :			int		=    latents.repeat_interleave(UpperCAmelCase_		, dim=0					)
			self.scheduler.set_timesteps(UpperCAmelCase_		, device=UpperCAmelCase_					)
			lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    self.get_timesteps(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)
			lowerCamelCase     :			Optional[Any]		=    timesteps[:1].repeat(batch_size * num_images_per_prompt					)
			lowerCamelCase	,    lowerCamelCase     :			Dict		=    downscale_height_and_width(UpperCAmelCase_		, UpperCAmelCase_		, self.movq_scale_factor					)
			lowerCamelCase     :			Optional[int]		=    self.prepare_latents(
			    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, image_embeds.dtype		, UpperCAmelCase_		, UpperCAmelCase_					)
			for i, t in enumerate(self.progress_bar(UpperCAmelCase_					)					):
					# expand the latents if we are doing classifier free guidance
					lowerCamelCase     :			Dict		=    torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

					lowerCamelCase     :			str		=    {'image_embeds': image_embeds, 'hint': hint}
					lowerCamelCase     :			Any		=    self.unet(
					    sample=UpperCAmelCase_		, timestep=UpperCAmelCase_		, encoder_hidden_states=UpperCAmelCase_		, added_cond_kwargs=UpperCAmelCase_		, return_dict=UpperCAmelCase_		, )[0]

					if do_classifier_free_guidance:
							lowerCamelCase	,    lowerCamelCase     :			Any		=    noise_pred.split(latents.shape[1]		, dim=1					)
							lowerCamelCase	,    lowerCamelCase     :			Dict		=    noise_pred.chunk(2					)
							lowerCamelCase	,    lowerCamelCase     :			Dict		=    variance_pred.chunk(2					)
							lowerCamelCase     :			str		=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							lowerCamelCase     :			Union[str, Any]		=    torch.cat([noise_pred, variance_pred_text]		, dim=1					)

					if not (
					    hasattr(self.scheduler.config		, 'variance_type'					)
					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
					):
							lowerCamelCase	,    lowerCamelCase     :			Union[str, Any]		=    noise_pred.split(latents.shape[1]		, dim=1					)

					# compute the previous noisy sample x_t -> x_t-1

					lowerCamelCase     :			Dict		=    self.scheduler.step(
					    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_		, )[0]

			# post-processing
			lowerCamelCase     :			Tuple		=    self.movq.decode(UpperCAmelCase_		, force_not_quantize=UpperCAmelCase_					)['sample']

			if output_type not in ["pt", "np", "pil"]:
					raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""					)

			if output_type in ["np", "pil"]:
					lowerCamelCase     :			Optional[int]		=    image * 0.5 + 0.5
					lowerCamelCase     :			str		=    image.clamp(0		, 1					)
					lowerCamelCase     :			int		=    image.cpu().permute(0		, 2		, 3		, 1					).float().numpy()

			if output_type == "pil":
					lowerCamelCase     :			Tuple		=    self.numpy_to_pil(UpperCAmelCase_					)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_					)








"""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        _lowercase   :




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=2		, UpperCAmelCase_=32		, UpperCAmelCase_=16		, UpperCAmelCase_=3		, UpperCAmelCase_=True		, UpperCAmelCase_=True		, UpperCAmelCase_=32		, UpperCAmelCase_=4		, UpperCAmelCase_=[0, 1, 2, 3]		, UpperCAmelCase_=4		, UpperCAmelCase_=37		, UpperCAmelCase_="gelu"		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.02		, UpperCAmelCase_=3		, UpperCAmelCase_=False		, UpperCAmelCase_=None		, )		->							List[str]:
			lowerCamelCase     :			List[str]		=    parent
			lowerCamelCase     :			Optional[int]		=    batch_size
			lowerCamelCase     :			Union[str, Any]		=    image_size
			lowerCamelCase     :			str		=    patch_size
			lowerCamelCase     :			Optional[Any]		=    num_channels
			lowerCamelCase     :			Tuple		=    is_training
			lowerCamelCase     :			int		=    use_labels
			lowerCamelCase     :			Union[str, Any]		=    hidden_size
			lowerCamelCase     :			List[Any]		=    num_hidden_layers
			lowerCamelCase     :			List[str]		=    backbone_out_indices
			lowerCamelCase     :			str		=    num_attention_heads
			lowerCamelCase     :			str		=    intermediate_size
			lowerCamelCase     :			str		=    hidden_act
			lowerCamelCase     :			Union[str, Any]		=    hidden_dropout_prob
			lowerCamelCase     :			Tuple		=    attention_probs_dropout_prob
			lowerCamelCase     :			List[Any]		=    initializer_range
			lowerCamelCase     :			Any		=    num_labels
			lowerCamelCase     :			Optional[Any]		=    scope
			lowerCamelCase     :			List[str]		=    is_hybrid
			# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
			lowerCamelCase     :			Optional[int]		=    (image_size // patch_size) ** 2
			lowerCamelCase     :			Tuple		=    num_patches + 1




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

			lowerCamelCase     :			Union[str, Any]		=    self.get_config()

			return config, pixel_values, labels




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			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=UpperCAmelCase_		, initializer_range=self.initializer_range		, is_hybrid=self.is_hybrid		, )




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			Dict		=    DPTModel(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    model(UpperCAmelCase_					)
			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							List[Any]:
			lowerCamelCase     :			str		=    self.num_labels
			lowerCamelCase     :			Optional[int]		=    DPTForDepthEstimation(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			str		=    model(UpperCAmelCase_					)
			self.parent.assertEqual(result.predicted_depth.shape		, (self.batch_size, self.image_size, self.image_size)					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Tuple:
			lowerCamelCase     :			Optional[Any]		=    self.num_labels
			lowerCamelCase     :			str		=    DPTForSemanticSegmentation(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    model(UpperCAmelCase_		, labels=UpperCAmelCase_					)
			self.parent.assertEqual(
			    result.logits.shape		, (self.batch_size, self.num_labels, self.image_size, self.image_size)					)




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			int		=    self.prepare_config_and_inputs()
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			List[str]		=    config_and_inputs
			lowerCamelCase     :			int		=    {'pixel_values': pixel_values}
			return config, inputs_dict






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

	lowercase_        =	False
	lowercase_        =	False
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    DPTModelTester(self					)
			lowerCamelCase     :			Optional[int]		=    ConfigTester(self		, config_class=UpperCAmelCase_		, has_text_modality=UpperCAmelCase_		, hidden_size=37					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			self.config_tester.run_common_tests()




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




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

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




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase	,    lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCamelCase     :			Any		=    ['pixel_values']
					self.assertListEqual(arg_names[:1]		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			str		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			int		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			for model_class in self.all_model_classes:
					if model_class.__name__ == "DPTForDepthEstimation":
							continue

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

					if model_class in get_values(UpperCAmelCase_					):
							continue

					lowerCamelCase     :			Tuple		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.train()
					lowerCamelCase     :			Tuple		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			int		=    model(**UpperCAmelCase_					).loss
					loss.backward()




	def 			_UpperCamelCase    ( self					)		->							str:
			for model_class in self.all_model_classes:
					if model_class.__name__ == "DPTForDepthEstimation":
							continue

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

					if model_class in get_values(UpperCAmelCase_					) or not model_class.supports_gradient_checkpointing:
							continue
					lowerCamelCase     :			Optional[int]		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.gradient_checkpointing_enable()
					model.train()
					lowerCamelCase     :			Optional[Any]		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    model(**UpperCAmelCase_					).loss
					loss.backward()




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

			lowerCamelCase     :			Union[str, Any]		=    _config_zero_init(UpperCAmelCase_					)
			for model_class in self.all_model_classes:
					lowerCamelCase     :			List[str]		=    model_class(config=UpperCAmelCase_					)
					# Skip the check for the backbone
					lowerCamelCase     :			Optional[int]		=    []
					for name, module in model.named_modules():
							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
									lowerCamelCase     :			Optional[int]		=    [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:
			pass




	@slow
	def 			_UpperCamelCase    ( self					)		->							str:
			for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase     :			List[str]		=    DPTModel.from_pretrained(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)


def 		UpperCAmelCase					(		):





		'''simple docstring'''


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






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




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			int		=    DPTImageProcessor.from_pretrained('Intel/dpt-large'					)
			lowerCamelCase     :			List[str]		=    DPTForDepthEstimation.from_pretrained('Intel/dpt-large'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			str		=    prepare_img()
			lowerCamelCase     :			Tuple		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			Dict		=    model(**UpperCAmelCase_					)
					lowerCamelCase     :			str		=    outputs.predicted_depth

			# verify the predicted depth
			lowerCamelCase     :			List[str]		=    torch.Size((1, 384, 384)					)
			self.assertEqual(predicted_depth.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.tensor(
			    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]					).to(UpperCAmelCase_					)

			self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]		, UpperCAmelCase_		, atol=1E-4					)					)




	def 			_UpperCamelCase    ( self					)		->							Tuple:
			lowerCamelCase     :			int		=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'					)
			lowerCamelCase     :			Optional[Any]		=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    prepare_img()
			lowerCamelCase     :			Tuple		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			List[str]		=    model(**UpperCAmelCase_					)

			# verify the logits
			lowerCamelCase     :			int		=    torch.Size((1, 150, 480, 480)					)
			self.assertEqual(outputs.logits.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			Tuple		=    torch.tensor(
			    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]					).to(UpperCAmelCase_					)

			self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		, UpperCAmelCase_		, atol=1E-4					)					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Dict		=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'					)
			lowerCamelCase     :			str		=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'					).to(UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    prepare_img()
			lowerCamelCase     :			Any		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			Dict		=    model(**UpperCAmelCase_					)

			lowerCamelCase     :			int		=    outputs.logits.detach().cpu()

			lowerCamelCase     :			int		=    image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_		, target_sizes=[(500, 300)]					)
			lowerCamelCase     :			Optional[Any]		=    torch.Size((500, 300)					)
			self.assertEqual(segmentation[0].shape		, UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    image_processor.post_process_semantic_segmentation(outputs=UpperCAmelCase_					)
			lowerCamelCase     :			Any		=    torch.Size((480, 480)					)
			self.assertEqual(segmentation[0].shape		, UpperCAmelCase_					)






