
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_			:




							'''simple docstring'''

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

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

										UpperCamelCase__    :		int     =				self.get_config()

										return config, pixel_values, labels
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										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        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Optional[int]:
										UpperCamelCase__    :		Optional[Any]     =				DPTModel(config=UpperCamelCase)
										model.to(UpperCamelCase)
										model.eval()
										UpperCamelCase__    :		List[Any]     =				model(UpperCamelCase)
										self.parent.assertEqual(result.last_hidden_state.shape     ,						(self.batch_size, self.seq_length, self.hidden_size))
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
										UpperCamelCase__    :		List[Any]     =				self.num_labels
										UpperCamelCase__    :		Any     =				DPTForDepthEstimation(UpperCamelCase)
										model.to(UpperCamelCase)
										model.eval()
										UpperCamelCase__    :		Any     =				model(UpperCamelCase)
										self.parent.assertEqual(result.predicted_depth.shape     ,						(self.batch_size, self.image_size, self.image_size))
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	List[Any]:
										UpperCamelCase__    :		List[str]     =				self.num_labels
										UpperCamelCase__    :		List[Any]     =				DPTForSemanticSegmentation(UpperCamelCase)
										model.to(UpperCamelCase)
										model.eval()
										UpperCamelCase__    :		str     =				model(UpperCamelCase     ,						labels=UpperCamelCase)
										self.parent.assertEqual(
										    result.logits.shape     ,						(self.batch_size, self.num_labels, self.image_size, self.image_size))






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



@require_torch
class 			UpperCamelCase_			(						__UpperCamelCase	,     __UpperCamelCase	,     unittest.TestCase   ):




							'''simple docstring'''

							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        lowerCAmelCase__ (				self) ->	int:
										UpperCamelCase__    :		List[Any]     =				DPTModelTester(self)
										UpperCamelCase__    :		Optional[Any]     =				ConfigTester(self     ,						config_class=UpperCamelCase     ,						has_text_modality=UpperCamelCase     ,						hidden_size=37)
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										self.config_tester.run_common_tests()
							@unittest.skip(reason='DPT does not use inputs_embeds')
							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										pass
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										UpperCamelCase__   ,			UpperCamelCase__    :		Any     =				self.model_tester.prepare_config_and_inputs_for_common()

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

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

													UpperCamelCase__    :		Union[str, Any]     =				['pixel_values']
													self.assertListEqual(arg_names[:1]     ,						UpperCamelCase)
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		int     =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCamelCase)
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										UpperCamelCase__    :		Dict     =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase)
							def        lowerCAmelCase__ (				self) ->	Optional[int]:
										UpperCamelCase__    :		Dict     =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase)
							def        lowerCAmelCase__ (				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__    :		Union[str, Any]     =				True

													if model_class in get_values(UpperCamelCase):
																continue

													UpperCamelCase__    :		int     =				model_class(UpperCamelCase)
													model.to(UpperCamelCase)
													model.train()
													UpperCamelCase__    :		Dict     =				self._prepare_for_class(UpperCamelCase     ,						UpperCamelCase     ,						return_labels=UpperCamelCase)
													UpperCamelCase__    :		Tuple     =				model(**UpperCamelCase).loss
													loss.backward()
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

													if model_class in get_values(UpperCamelCase) or not model_class.supports_gradient_checkpointing:
																continue
													UpperCamelCase__    :		Dict     =				model_class(UpperCamelCase)
													model.to(UpperCamelCase)
													model.gradient_checkpointing_enable()
													model.train()
													UpperCamelCase__    :		Tuple     =				self._prepare_for_class(UpperCamelCase     ,						UpperCamelCase     ,						return_labels=UpperCamelCase)
													UpperCamelCase__    :		List[Any]     =				model(**UpperCamelCase).loss
													loss.backward()
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__   ,			UpperCamelCase__    :		Any     =				self.model_tester.prepare_config_and_inputs_for_common()

										UpperCamelCase__    :		List[Any]     =				_config_zero_init(UpperCamelCase)
										for model_class in self.all_model_classes:
													UpperCamelCase__    :		Optional[Any]     =				model_class(config=UpperCamelCase)
													# Skip the check for the backbone
													UpperCamelCase__    :		Dict     =				[]
													for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																			UpperCamelCase__    :		List[str]     =				[F"""{name}.{key}""" for key in module.state_dict().keys()]
																			break

													for name, param in model.named_parameters():
																if param.requires_grad:
																			if name in backbone_params:
																						continue
																			self.assertIn(
																			    ((param.data.mean() * 1E9).round() / 1E9).item()     ,						[0.0, 1.0]     ,						msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,						)
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
							def        lowerCAmelCase__ (				self) ->	Tuple:
										pass






							@slow
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCamelCase__    :		Union[str, Any]     =				DPTModel.from_pretrained(UpperCamelCase)
													self.assertIsNotNone(UpperCamelCase)



def       _lowercase					(      )	->     int:
			UpperCamelCase__    :		Tuple     =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
			return image



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




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	List[Any]:
										UpperCamelCase__    :		List[str]     =				DPTImageProcessor.from_pretrained('Intel/dpt-large')
										UpperCamelCase__    :		Optional[int]     =				DPTForDepthEstimation.from_pretrained('Intel/dpt-large').to(UpperCamelCase)

										UpperCamelCase__    :		int     =				prepare_img()
										UpperCamelCase__    :		Dict     =				image_processor(images=UpperCamelCase     ,						return_tensors='pt').to(UpperCamelCase)

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

										# verify the predicted depth
										UpperCamelCase__    :		int     =				torch.Size((1, 3_84, 3_84))
										self.assertEqual(predicted_depth.shape     ,						UpperCamelCase)

										UpperCamelCase__    :		int     =				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        lowerCAmelCase__ (				self) ->	Optional[int]:
										UpperCamelCase__    :		int     =				DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
										UpperCamelCase__    :		Optional[int]     =				DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(UpperCamelCase)

										UpperCamelCase__    :		int     =				prepare_img()
										UpperCamelCase__    :		str     =				image_processor(images=UpperCamelCase     ,						return_tensors='pt').to(UpperCamelCase)

										# forward pass
										with torch.no_grad():
													UpperCamelCase__    :		Tuple     =				model(**UpperCamelCase)

										# verify the logits
										UpperCamelCase__    :		Optional[Any]     =				torch.Size((1, 1_50, 4_80, 4_80))
										self.assertEqual(outputs.logits.shape     ,						UpperCamelCase)

										UpperCamelCase__    :		Optional[int]     =				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        lowerCAmelCase__ (				self) ->	Tuple:
										UpperCamelCase__    :		List[str]     =				DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
										UpperCamelCase__    :		Tuple     =				DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(UpperCamelCase)

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

										# forward pass
										with torch.no_grad():
													UpperCamelCase__    :		Optional[int]     =				model(**UpperCamelCase)

										UpperCamelCase__    :		List[str]     =				outputs.logits.detach().cpu()

										UpperCamelCase__    :		Optional[int]     =				image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase     ,						target_sizes=[(5_00, 3_00)])
										UpperCamelCase__    :		List[Any]     =				torch.Size((5_00, 3_00))
										self.assertEqual(segmentation[0].shape     ,						UpperCamelCase)

										UpperCamelCase__    :		Union[str, Any]     =				image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				torch.Size((4_80, 4_80))
										self.assertEqual(segmentation[0].shape     ,						UpperCamelCase)





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,
)


UpperCAmelCase__				:  Optional[int]       					=				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__				:  Optional[int]       					=				'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=8 )	->     List[Any]:
			UpperCamelCase__    :		Union[str, Any]     =				height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCamelCase__    :		Tuple     =				width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=512				,    __SCREAMING_SNAKE_CASE=512 )	->     Union[str, Any]:
			UpperCamelCase__    :		List[str]     =				pil_image.resize((w, h)				,    resample=Image.BICUBIC				,    reducing_gap=1 )
			UpperCamelCase__    :		List[str]     =				np.array(pil_image.convert('RGB' ) )
			UpperCamelCase__    :		Tuple     =				arr.astype(np.floataa ) / 127.5 - 1
			UpperCamelCase__    :		Tuple     =				np.transpose(__SCREAMING_SNAKE_CASE				,    [2, 0, 1] )
			UpperCamelCase__    :		int     =				torch.from_numpy(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
			return image



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						) ->	Any:
										super().__init__()

										self.register_modules(
										    unet=UpperCamelCase     ,						scheduler=UpperCamelCase     ,						movq=UpperCamelCase     ,						)
										UpperCamelCase__    :		str     =				2 ** (len(self.movq.config.block_out_channels) - 1)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
										# get the original timestep using init_timestep
										UpperCamelCase__    :		str     =				min(int(num_inference_steps * strength)     ,						UpperCamelCase)

										UpperCamelCase__    :		int     =				max(num_inference_steps - init_timestep     ,						0)
										UpperCamelCase__    :		str     =				self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start
							def        lowerCAmelCase__ (				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)}""")

										UpperCamelCase__    :		int     =				image.to(device=UpperCamelCase     ,						dtype=UpperCamelCase)

										UpperCamelCase__    :		int     =				batch_size * num_images_per_prompt

										if image.shape[1] == 4:
													UpperCamelCase__    :		Tuple     =				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):
																UpperCamelCase__    :		Union[str, Any]     =				[
																    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(UpperCamelCase)
																]
																UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)
													else:
																UpperCamelCase__    :		Any     =				self.movq.encode(UpperCamelCase).latent_dist.sample(UpperCamelCase)

													UpperCamelCase__    :		List[Any]     =				self.movq.config.scaling_factor * init_latents

										UpperCamelCase__    :		List[Any]     =				torch.cat([init_latents]     ,						dim=0)

										UpperCamelCase__    :		Union[str, Any]     =				init_latents.shape
										UpperCamelCase__    :		Union[str, Any]     =				randn_tensor(UpperCamelCase     ,						generator=UpperCamelCase     ,						device=UpperCamelCase     ,						dtype=UpperCamelCase)

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

										UpperCamelCase__    :		List[Any]     =				init_latents

										return latents
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	str:
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError('Please install accelerate via `pip install accelerate`')

										UpperCamelCase__    :		Any     =				torch.device(F"""cuda:{gpu_id}""")

										UpperCamelCase__    :		int     =				[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(UpperCamelCase     ,						UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	Tuple:
										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.')

										UpperCamelCase__    :		int     =				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)

										UpperCamelCase__    :		Dict     =				None
										for cpu_offloaded_model in [self.unet, self.movq]:
													UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				cpu_offload_with_hook(UpperCamelCase     ,						UpperCamelCase     ,						prev_module_hook=UpperCamelCase)

										# We'll offload the last model manually.
										UpperCamelCase__    :		List[Any]     =				hook
							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        lowerCAmelCase__ (				self) ->	Optional[Any]:
										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 = 5_12     ,						UpperCamelCase = 5_12     ,						UpperCamelCase = 1_00     ,						UpperCamelCase = 4.0     ,						UpperCamelCase = 0.3     ,						UpperCamelCase = 1     ,						UpperCamelCase = None     ,						UpperCamelCase = "pil"     ,						UpperCamelCase = True     ,						) ->	Optional[Any]:
										UpperCamelCase__    :		List[Any]     =				self._execution_device

										UpperCamelCase__    :		Union[str, Any]     =				guidance_scale > 1.0

										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Dict     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		List[Any]     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)

										UpperCamelCase__    :		Optional[Any]     =				image_embeds.shape[0]

										if do_classifier_free_guidance:
													UpperCamelCase__    :		List[str]     =				image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		Any     =				negative_image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		str     =				hint.repeat_interleave(UpperCamelCase     ,						dim=0)

										UpperCamelCase__    :		int     =				torch.cat([negative_image_embeds, image_embeds]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)
										UpperCamelCase__    :		Tuple     =				torch.cat([hint, hint]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)

										if not isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		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""")

										UpperCamelCase__    :		List[str]     =				torch.cat([prepare_image(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) for i in image]     ,						dim=0)
										UpperCamelCase__    :		int     =				image.to(dtype=image_embeds.dtype     ,						device=UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				self.movq.encode(UpperCamelCase)['latents']
										UpperCamelCase__    :		str     =				latents.repeat_interleave(UpperCamelCase     ,						dim=0)
										self.scheduler.set_timesteps(UpperCamelCase     ,						device=UpperCamelCase)
										UpperCamelCase__   ,			UpperCamelCase__    :		List[str]     =				self.get_timesteps(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				timesteps[:1].repeat(batch_size * num_images_per_prompt)
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[Any]     =				downscale_height_and_width(UpperCamelCase     ,						UpperCamelCase     ,						self.movq_scale_factor)
										UpperCamelCase__    :		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
													UpperCamelCase__    :		List[Any]     =				torch.cat([latents] * 2) if do_classifier_free_guidance else latents

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

													if do_classifier_free_guidance:
																UpperCamelCase__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				noise_pred.chunk(2)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				variance_pred.chunk(2)
																UpperCamelCase__    :		Tuple     =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																UpperCamelCase__    :		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"]
													):
																UpperCamelCase__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)

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

													UpperCamelCase__    :		Union[str, Any]     =				self.scheduler.step(
													    UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						generator=UpperCamelCase     ,						)[0]

										# post-processing
										UpperCamelCase__    :		Optional[Any]     =				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"]:
													UpperCamelCase__    :		Any     =				image * 0.5 + 0.5
													UpperCamelCase__    :		Tuple     =				image.clamp(0     ,						1)
													UpperCamelCase__    :		Tuple     =				image.cpu().permute(0     ,						2     ,						3     ,						1).float().numpy()

										if output_type == "pil":
													UpperCamelCase__    :		Dict     =				self.numpy_to_pil(UpperCamelCase)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=UpperCamelCase)



