






import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

		if is_vision_available():
				from transformers import OneFormerImageProcessor
				from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
				from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
		from PIL import Image




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Dict="shi-labs/oneformer_demo"  ):


					'''simple docstring'''


					with open(hf_hub_download(__snake_case     ,       __snake_case     ,       repo_type='dataset'  )     ,       'r'  ) as f:
										UpperCAmelCase_    :      Optional[Any]            =						json.load(__snake_case  )
					UpperCAmelCase_    :      str            =						{}
					UpperCAmelCase_    :      str            =						[]
					UpperCAmelCase_    :      List[Any]            =						[]
					for key, info in class_info.items():
										UpperCAmelCase_    :      Dict            =						info['name']
										class_names.append(info['name']  )
										if info["isthing"]:
															thing_ids.append(int(__snake_case  )  )
					UpperCAmelCase_    :      Tuple            =						thing_ids
					UpperCAmelCase_    :      Optional[Any]            =						class_names
					return metadata



class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=7				,		_UpperCamelCase=3				,		_UpperCamelCase=3_0				,		_UpperCamelCase=4_0_0				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		_UpperCamelCase=True				,		_UpperCamelCase=[0.5, 0.5, 0.5]				,		_UpperCamelCase=[0.5, 0.5, 0.5]				,		_UpperCamelCase=1_0				,		_UpperCamelCase=False				,		_UpperCamelCase=2_5_5				,		_UpperCamelCase="shi-labs/oneformer_demo"				,		_UpperCamelCase="ade20k_panoptic.json"				,		_UpperCamelCase=1_0				,		)	->							Tuple:
							UpperCAmelCase_    :      Optional[int]            =						parent
							UpperCAmelCase_    :      int            =						batch_size
							UpperCAmelCase_    :      int            =						num_channels
							UpperCAmelCase_    :      int            =						min_resolution
							UpperCAmelCase_    :      Union[str, Any]            =						max_resolution
							UpperCAmelCase_    :      Optional[int]            =						do_resize
							UpperCAmelCase_    :      Any            =						{'shortest_edge': 3_2, 'longest_edge': 1_3_3_3} if size is None else size
							UpperCAmelCase_    :      Optional[int]            =						do_normalize
							UpperCAmelCase_    :      Optional[Any]            =						image_mean
							UpperCAmelCase_    :      List[str]            =						image_std
							UpperCAmelCase_    :      Union[str, Any]            =						class_info_file
							UpperCAmelCase_    :      str            =						prepare_metadata(_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						num_text
							UpperCAmelCase_    :      int            =						repo_path

							# for the post_process_functions
							UpperCAmelCase_    :      List[str]            =						2
							UpperCAmelCase_    :      Dict            =						1_0
							UpperCAmelCase_    :      Any            =						1_0
							UpperCAmelCase_    :      int            =						3
							UpperCAmelCase_    :      Union[str, Any]            =						4
							UpperCAmelCase_    :      Optional[int]            =						num_labels
							UpperCAmelCase_    :      int            =						do_reduce_labels
							UpperCAmelCase_    :      Tuple            =						ignore_index



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "num_labels": self.num_labels,
							    "do_reduce_labels": self.do_reduce_labels,
							    "ignore_index": self.ignore_index,
							    "class_info_file": self.class_info_file,
							    "metadata": self.metadata,
							    "num_text": self.num_text,
							}



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False   )	->							Dict:
							if not batched:
												UpperCAmelCase_    :      Any            =						image_inputs[0]
												if isinstance(_UpperCamelCase				,		Image.Image   ):
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						image.size
												else:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						image.shape[1], image.shape[2]
												if w < h:
																	UpperCAmelCase_    :      List[str]            =						int(self.size['shortest_edge'] * h / w   )
																	UpperCAmelCase_    :      Optional[int]            =						self.size['shortest_edge']
												elif w > h:
																	UpperCAmelCase_    :      Optional[int]            =						self.size['shortest_edge']
																	UpperCAmelCase_    :      Dict            =						int(self.size['shortest_edge'] * w / h   )
												else:
																	UpperCAmelCase_    :      Optional[Any]            =						self.size['shortest_edge']
																	UpperCAmelCase_    :      Dict            =						self.size['shortest_edge']

							else:
												UpperCAmelCase_    :      List[str]            =						[]
												for image in image_inputs:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						self.get_expected_values([image]   )
																	expected_values.append((expected_height, expected_width)   )
												UpperCAmelCase_    :      str            =						max(_UpperCamelCase				,		key=lambda _UpperCamelCase   : item[0]   )[0]
												UpperCAmelCase_    :      str            =						max(_UpperCamelCase				,		key=lambda _UpperCamelCase   : item[1]   )[1]

							return expected_height, expected_width



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							return OneFormerForUniversalSegmentationOutput(
							    # +1 for null class
							    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)   )				,		masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)   )				,		)





@require_torch
@require_vision
class   lowerCamelCase      (_snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    str       						=  OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
		# only for test_image_processing_common.test_image_proc_to_json_string
		_snake_case    :    Union[str, Any]       						=  image_processing_class



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						OneFormerImageProcessorTester(self   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							return self.image_processing_tester.prepare_image_processor_dict()



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      List[str]            =						self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'image_mean'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'image_std'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_normalize'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_resize'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'size'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'ignore_index'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'class_info_file'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'num_text'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'repo_path'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'metadata'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_reduce_labels'   )   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							pass



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							# Initialize image_processor
							UpperCAmelCase_    :      Optional[int]            =						self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							UpperCAmelCase_    :      List[str]            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		Image.Image   )

							# Test not batched input
							UpperCAmelCase_    :      Union[str, Any]            =						image_processor(image_inputs[0]				,		['semantic']				,		return_tensors='pt'   ).pixel_values

							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.image_processing_tester.get_expected_values(_UpperCamelCase   )

							self.assertEqual(
							    encoded_images.shape				,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,		)

							# Test batched
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.image_processing_tester.get_expected_values(_UpperCamelCase				,		batched=_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,		)



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							# Initialize image_processor
							UpperCAmelCase_    :      Tuple            =						self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							UpperCAmelCase_    :      Dict            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase				,		numpify=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		np.ndarray   )

							# Test not batched input
							UpperCAmelCase_    :      Optional[Any]            =						image_processor(image_inputs[0]				,		['semantic']				,		return_tensors='pt'   ).pixel_values

							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						self.image_processing_tester.get_expected_values(_UpperCamelCase   )

							self.assertEqual(
							    encoded_images.shape				,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,		)

							# Test batched
							UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						self.image_processing_tester.get_expected_values(_UpperCamelCase				,		batched=_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,		)



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							# Initialize image_processor
							UpperCAmelCase_    :      List[Any]            =						self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							UpperCAmelCase_    :      str            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase				,		torchify=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		torch.Tensor   )

							# Test not batched input
							UpperCAmelCase_    :      Optional[int]            =						image_processor(image_inputs[0]				,		['semantic']				,		return_tensors='pt'   ).pixel_values

							UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						self.image_processing_tester.get_expected_values(_UpperCamelCase   )

							self.assertEqual(
							    encoded_images.shape				,		(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,		)

							# Test batched
							UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						self.image_processing_tester.get_expected_values(_UpperCamelCase				,		batched=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=False				,		_UpperCamelCase=False				,		_UpperCamelCase="np"   )	->							Dict:
							UpperCAmelCase_    :      Optional[Any]            =						self.image_processing_class(**self.image_processor_dict   )
							# prepare image and target
							UpperCAmelCase_    :      Optional[Any]            =						self.image_processing_tester.num_labels
							UpperCAmelCase_    :      Tuple            =						None
							UpperCAmelCase_    :      Union[str, Any]            =						None
							UpperCAmelCase_    :      Dict            =						prepare_image_inputs(self.image_processing_tester				,		equal_resolution=_UpperCamelCase   )
							if with_segmentation_maps:
												UpperCAmelCase_    :      Tuple            =						num_labels
												if is_instance_map:
																	UpperCAmelCase_    :      Dict            =						list(range(_UpperCamelCase   )   ) * 2
																	UpperCAmelCase_    :      Any            =						dict(enumerate(_UpperCamelCase   )   )
												UpperCAmelCase_    :      Dict            =						[
												    np.random.randint(0				,		high * 2				,		(img.size[1], img.size[0])   ).astype(np.uinta   ) for img in image_inputs
												]
												if segmentation_type == "pil":
																	UpperCAmelCase_    :      Tuple            =						[Image.fromarray(_UpperCamelCase   ) for annotation in annotations]

							UpperCAmelCase_    :      Optional[int]            =						image_processor(
							    _UpperCamelCase				,		['semantic'] * len(_UpperCamelCase   )				,		_UpperCamelCase				,		return_tensors='pt'				,		instance_id_to_semantic_id=_UpperCamelCase				,		pad_and_return_pixel_mask=_UpperCamelCase				,		)

							return inputs



		def 					__UpperCAmelCase			(							self   )	->							Any:
							pass



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							def common(_UpperCamelCase=False				,		_UpperCamelCase=None   ):
												UpperCAmelCase_    :      int            =						self.comm_get_image_processor_inputs(
												    with_segmentation_maps=_UpperCamelCase				,		is_instance_map=_UpperCamelCase				,		segmentation_type=_UpperCamelCase   )

												UpperCAmelCase_    :      Union[str, Any]            =						inputs['mask_labels']
												UpperCAmelCase_    :      Optional[Any]            =						inputs['class_labels']
												UpperCAmelCase_    :      Dict            =						inputs['pixel_values']
												UpperCAmelCase_    :      List[str]            =						inputs['text_inputs']

												# check the batch_size
												for mask_label, class_label, text_input in zip(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ):
																	self.assertEqual(mask_label.shape[0]				,		class_label.shape[0]   )
																	# this ensure padding has happened
																	self.assertEqual(mask_label.shape[1:]				,		pixel_values.shape[2:]   )
																	self.assertEqual(len(_UpperCamelCase   )				,		self.image_processing_tester.num_text   )

							common()
							common(is_instance_map=_UpperCamelCase   )
							common(is_instance_map=_UpperCamelCase				,		segmentation_type='pil'   )
							common(is_instance_map=_UpperCamelCase				,		segmentation_type='pil'   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      Tuple            =						np.zeros((2_0, 5_0)   )
							UpperCAmelCase_    :      Optional[Any]            =						1
							UpperCAmelCase_    :      str            =						1
							UpperCAmelCase_    :      Union[str, Any]            =						1

							UpperCAmelCase_    :      List[Any]            =						binary_mask_to_rle(_UpperCamelCase   )
							self.assertEqual(len(_UpperCamelCase   )				,		4   )
							self.assertEqual(rle[0]				,		2_1   )
							self.assertEqual(rle[1]				,		4_5   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Optional[int]            =						self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes				,		max_seq_length=7_7				,		task_seq_length=7_7				,		class_info_file='ade20k_panoptic.json'				,		num_text=self.image_processing_tester.num_text				,		repo_path='shi-labs/oneformer_demo'				,		)
							UpperCAmelCase_    :      Optional[int]            =						self.image_processing_tester.get_fake_oneformer_outputs()

							UpperCAmelCase_    :      str            =						fature_extractor.post_process_semantic_segmentation(_UpperCamelCase   )

							self.assertEqual(len(_UpperCamelCase   )				,		self.image_processing_tester.batch_size   )
							self.assertEqual(
							    segmentation[0].shape				,		(
							        self.image_processing_tester.height,
							        self.image_processing_tester.width,
							    )				,		)

							UpperCAmelCase_    :      str            =						[(1, 4) for i in range(self.image_processing_tester.batch_size   )]
							UpperCAmelCase_    :      List[Any]            =						fature_extractor.post_process_semantic_segmentation(_UpperCamelCase				,		target_sizes=_UpperCamelCase   )

							self.assertEqual(segmentation[0].shape				,		target_sizes[0]   )



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							UpperCAmelCase_    :      Dict            =						self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes				,		max_seq_length=7_7				,		task_seq_length=7_7				,		class_info_file='ade20k_panoptic.json'				,		num_text=self.image_processing_tester.num_text				,		repo_path='shi-labs/oneformer_demo'				,		)
							UpperCAmelCase_    :      Any            =						self.image_processing_tester.get_fake_oneformer_outputs()
							UpperCAmelCase_    :      Tuple            =						image_processor.post_process_instance_segmentation(_UpperCamelCase				,		threshold=0   )

							self.assertTrue(len(_UpperCamelCase   ) == self.image_processing_tester.batch_size   )
							for el in segmentation:
												self.assertTrue('segmentation' in el   )
												self.assertTrue('segments_info' in el   )
												self.assertEqual(type(el['segments_info']   )				,		_UpperCamelCase   )
												self.assertEqual(
												    el['segmentation'].shape				,		(self.image_processing_tester.height, self.image_processing_tester.width)   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      List[Any]            =						self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes				,		max_seq_length=7_7				,		task_seq_length=7_7				,		class_info_file='ade20k_panoptic.json'				,		num_text=self.image_processing_tester.num_text				,		repo_path='shi-labs/oneformer_demo'				,		)
							UpperCAmelCase_    :      Tuple            =						self.image_processing_tester.get_fake_oneformer_outputs()
							UpperCAmelCase_    :      Optional[Any]            =						image_processor.post_process_panoptic_segmentation(_UpperCamelCase				,		threshold=0   )

							self.assertTrue(len(_UpperCamelCase   ) == self.image_processing_tester.batch_size   )
							for el in segmentation:
												self.assertTrue('segmentation' in el   )
												self.assertTrue('segments_info' in el   )
												self.assertEqual(type(el['segments_info']   )				,		_UpperCamelCase   )
												self.assertEqual(
												    el['segmentation'].shape				,		(self.image_processing_tester.height, self.image_processing_tester.width)   )














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

__UpperCAmelCase								=						'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							int     ,       __snake_case    :							Tuple=8  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Dict            =						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCAmelCase_    :      List[Any]            =						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor




def      lowercase__      (     __snake_case    :							Any     ,       __snake_case    :							int=512     ,       __snake_case    :							Dict=512  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						pil_image.resize((w, h)     ,       resample=Image.BICUBIC     ,       reducing_gap=1  )
					UpperCAmelCase_    :      Dict            =						np.array(pil_image.convert('RGB'  )  )
					UpperCAmelCase_    :      Any            =						arr.astype(np.floataa  ) / 127.5 - 1
					UpperCAmelCase_    :      Dict            =						np.transpose(__snake_case     ,       [2, 0, 1]  )
					UpperCAmelCase_    :      List[str]            =						torch.from_numpy(__snake_case  ).unsqueeze(0  )
					return image






class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Union[str, Any]:
							super().__init__()

							self.register_modules(
							    unet=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		movq=_UpperCamelCase				,		)
							UpperCAmelCase_    :      Tuple            =						2 ** (len(self.movq.config.block_out_channels   ) - 1)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							# get the original timestep using init_timestep
							UpperCAmelCase_    :      Any            =						min(int(num_inference_steps * strength   )				,		_UpperCamelCase   )

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

							return timesteps, num_inference_steps - t_start



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							Tuple:
							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_    :      List[str]            =						image.to(device=_UpperCamelCase				,		dtype=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						batch_size * num_images_per_prompt

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

												UpperCAmelCase_    :      int            =						self.movq.config.scaling_factor * init_latents

							UpperCAmelCase_    :      Optional[int]            =						torch.cat([init_latents]				,		dim=0   )

							UpperCAmelCase_    :      Tuple            =						init_latents.shape
							UpperCAmelCase_    :      List[Any]            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=_UpperCamelCase   )

							# get latents
							UpperCAmelCase_    :      str            =						self.scheduler.add_noise(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						init_latents

							return latents



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Any:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.device(f"cuda:{gpu_id}"   )

							UpperCAmelCase_    :      Optional[Any]            =						[
							    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   )	->							Union[str, Any]:
							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_    :      str            =						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_    :      Dict            =						cpu_offload_with_hook(_UpperCamelCase				,		_UpperCamelCase				,		prev_module_hook=_UpperCamelCase   )

							# We'll offload the last model manually.
							UpperCAmelCase_    :      Any            =						hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							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 = 5_1_2				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 1_0_0				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 0.3				,		_UpperCamelCase = 1				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							str:
							UpperCAmelCase_    :      Any            =						self._execution_device

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

							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						torch.cat(_UpperCamelCase				,		dim=0   )
							UpperCAmelCase_    :      Optional[Any]            =						image_embeds.shape[0]
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						torch.cat(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      int            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )
												UpperCAmelCase_    :      int            =						negative_image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.cat([negative_image_embeds, image_embeds]				,		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_    :      str            =						torch.cat([prepare_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ) for i in image]				,		dim=0   )
							UpperCAmelCase_    :      Any            =						image.to(dtype=image_embeds.dtype				,		device=_UpperCamelCase   )

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

												UpperCAmelCase_    :      str            =						{'image_embeds': image_embeds}
												UpperCAmelCase_    :      Union[str, 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_    :      Tuple            =						noise_pred.split(latents.shape[1]				,		dim=1   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						variance_pred.chunk(2   )
																	UpperCAmelCase_    :      Dict            =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCAmelCase_    :      Tuple            =						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_    :      int            =						noise_pred.split(latents.shape[1]				,		dim=1   )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_    :      List[str]            =						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_    :      List[str]            =						image * 0.5 + 0.5
												UpperCAmelCase_    :      List[Any]            =						image.clamp(0				,		1   )
												UpperCAmelCase_    :      Dict            =						image.cpu().permute(0				,		2				,		3				,		1   ).float().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[Any]            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase   )






