import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 PIL import Image

					from transformers import YolosImageProcessor

class 			a					(		unittest.TestCase			):


						def __init__(    self  ,   A_  ,   A_=7  ,   A_=3  ,   A_=30  ,   A_=400  ,   A_=True  ,   A_=None  ,   A_=True  ,   A_=[0.5, 0.5, 0.5]  ,   A_=[0.5, 0.5, 0.5]  ,   A_=True  ,   A_=1 / 255  ,   A_=True  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
										_UpperCAmelCase    :						Union[str, Any]							=      parent
										_UpperCAmelCase    :						Optional[int]							=      batch_size
										_UpperCAmelCase    :						Any							=      num_channels
										_UpperCAmelCase    :						int							=      min_resolution
										_UpperCAmelCase    :						Dict							=      max_resolution
										_UpperCAmelCase    :						Tuple							=      do_resize
										_UpperCAmelCase    :						List[str]							=      size
										_UpperCAmelCase    :						Any							=      do_normalize
										_UpperCAmelCase    :						Optional[Any]							=      image_mean
										_UpperCAmelCase    :						Optional[int]							=      image_std
										_UpperCAmelCase    :						str							=      do_rescale
										_UpperCAmelCase    :						str							=      rescale_factor
										_UpperCAmelCase    :						List[Any]							=      do_pad


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=False		):







										'''simple docstring'''


										if not batched:
														_UpperCAmelCase    :						Any							=      image_inputs[0]
														if isinstance(A_  ,   Image.Image		):
																		_UpperCAmelCase    :						str							=      image.size
														else:
																		_UpperCAmelCase    :						Any							=      image.shape[1], image.shape[2]
														if w < h:
																		_UpperCAmelCase    :						int							=      int(self.size["shortest_edge"] * h / w		)
																		_UpperCAmelCase    :						Tuple							=      self.size["shortest_edge"]
														elif w > h:
																		_UpperCAmelCase    :						Dict							=      self.size["shortest_edge"]
																		_UpperCAmelCase    :						Dict							=      int(self.size["shortest_edge"] * w / h		)
														else:
																		_UpperCAmelCase    :						str							=      self.size["shortest_edge"]
																		_UpperCAmelCase    :						int							=      self.size["shortest_edge"]

										else:
														_UpperCAmelCase    :						Any							=      []
														for image in image_inputs:
																		_UpperCAmelCase    :						Union[str, Any]							=      self.get_expected_values([image]		)
																		expected_values.append((expected_height, expected_width)		)
														_UpperCAmelCase    :						Tuple							=      max(A_  ,   key=lambda A_		: item[0]		)[0]
														_UpperCAmelCase    :						Any							=      max(A_  ,   key=lambda A_		: item[1]		)[1]

										return expected_height, expected_width







@require_torch
@require_vision
class 			a					(		UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     YolosImageProcessor if is_vision_available() else None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      YolosImageProcessingTester(self		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.image_processor_tester.prepare_image_processor_dict()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.image_processing_class(**self.image_processor_dict		)
										self.assertTrue(hasattr(A_  ,   "image_mean"		)		)
										self.assertTrue(hasattr(A_  ,   "image_std"		)		)
										self.assertTrue(hasattr(A_  ,   "do_normalize"		)		)
										self.assertTrue(hasattr(A_  ,   "do_resize"		)		)
										self.assertTrue(hasattr(A_  ,   "size"		)		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.image_processing_class.from_dict(self.image_processor_dict		)
										self.assertEqual(image_processor.size  ,   {"shortest_edge": 18, "longest_edge": 1333}		)
										self.assertEqual(image_processor.do_pad  ,   A_		)

										_UpperCAmelCase    :						Optional[Any]							=      self.image_processing_class.from_dict(
										    self.image_processor_dict  ,   size=42  ,   max_size=84  ,   pad_and_return_pixel_mask=A_		)
										self.assertEqual(image_processor.size  ,   {"shortest_edge": 42, "longest_edge": 84}		)
										self.assertEqual(image_processor.do_pad  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PIL images
										_UpperCAmelCase    :						Union[str, Any]							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   Image.Image		)

										# Test not batched input
										_UpperCAmelCase    :						str							=      image_processing(image_inputs[0]  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase    :						str							=      self.image_processor_tester.get_expected_values(A_		)

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

										# Test batched
										_UpperCAmelCase    :						Tuple							=      self.image_processor_tester.get_expected_values(A_  ,   batched=A_		)

										_UpperCAmelCase    :						Any							=      image_processing(A_  ,   return_tensors="pt"		).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,   (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.image_processing_class(**self.image_processor_dict		)
										# create random numpy tensors
										_UpperCAmelCase    :						Dict							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_  ,   numpify=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   np.ndarray		)

										# Test not batched input
										_UpperCAmelCase    :						Union[str, Any]							=      image_processing(image_inputs[0]  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase    :						Any							=      self.image_processor_tester.get_expected_values(A_		)

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

										# Test batched
										_UpperCAmelCase    :						Any							=      image_processing(A_  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase    :						Optional[Any]							=      self.image_processor_tester.get_expected_values(A_  ,   batched=A_		)

										self.assertEqual(
										    encoded_images.shape  ,   (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PyTorch tensors
										_UpperCAmelCase    :						Optional[int]							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_  ,   torchify=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   torch.Tensor		)

										# Test not batched input
										_UpperCAmelCase    :						List[str]							=      image_processing(image_inputs[0]  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase    :						List[str]							=      self.image_processor_tester.get_expected_values(A_		)

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

										# Test batched
										_UpperCAmelCase    :						Tuple							=      image_processing(A_  ,   return_tensors="pt"		).pixel_values

										_UpperCAmelCase    :						Dict							=      self.image_processor_tester.get_expected_values(A_  ,   batched=A_		)

										self.assertEqual(
										    encoded_images.shape  ,   (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.image_processing_class(**self.image_processor_dict		)
										_UpperCAmelCase    :						Union[str, Any]							=      self.image_processing_class(do_resize=A_  ,   do_normalize=A_  ,   do_rescale=A_		)
										# create random PyTorch tensors
										_UpperCAmelCase    :						Any							=      prepare_image_inputs(self.image_processor_tester  ,   equal_resolution=A_  ,   torchify=A_		)
										for image in image_inputs:
														self.assertIsInstance(A_  ,   torch.Tensor		)

										# Test whether the method "pad" and calling the image processor return the same tensors
										_UpperCAmelCase    :						Optional[int]							=      image_processing_a.pad(A_  ,   return_tensors="pt"		)
										_UpperCAmelCase    :						List[str]							=      image_processing_a(A_  ,   return_tensors="pt"		)

										self.assertTrue(
										    torch.allclose(encoded_images_with_method["pixel_values"]  ,   encoded_images["pixel_values"]  ,   atol=1e-4		)		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"  ,   "r"		) as f:
														_UpperCAmelCase    :						Optional[Any]							=      json.loads(f.read()		)

										_UpperCAmelCase    :						Union[str, Any]							=      {"image_id": 39769, "annotations": target}

										# encode them
										_UpperCAmelCase    :						Union[str, Any]							=      YolosImageProcessor.from_pretrained("hustvl/yolos-small"		)
										_UpperCAmelCase    :						Optional[int]							=      image_processing(images=A_  ,   annotations=A_  ,   return_tensors="pt"		)

										# verify pixel values
										_UpperCAmelCase    :						List[Any]							=      torch.Size([1, 3, 800, 1066]		)
										self.assertEqual(encoding["pixel_values"].shape  ,   A_		)

										_UpperCAmelCase    :						Optional[Any]							=      torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,   A_  ,   atol=1e-4		)		)

										# verify area
										_UpperCAmelCase    :						Optional[int]							=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,   A_		)		)
										# verify boxes
										_UpperCAmelCase    :						Union[str, Any]							=      torch.Size([6, 4]		)
										self.assertEqual(encoding["labels"][0]["boxes"].shape  ,   A_		)
										_UpperCAmelCase    :						List[str]							=      torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,   A_  ,   atol=1e-3		)		)
										# verify image_id
										_UpperCAmelCase    :						Optional[int]							=      torch.tensor([39769]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,   A_		)		)
										# verify is_crowd
										_UpperCAmelCase    :						List[str]							=      torch.tensor([0, 0, 0, 0, 0, 0]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,   A_		)		)
										# verify class_labels
										_UpperCAmelCase    :						int							=      torch.tensor([75, 75, 63, 65, 17, 17]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,   A_		)		)
										# verify orig_size
										_UpperCAmelCase    :						Optional[int]							=      torch.tensor([480, 640]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,   A_		)		)
										# verify size
										_UpperCAmelCase    :						Any							=      torch.tensor([800, 1066]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,   A_		)		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
										with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"  ,   "r"		) as f:
														_UpperCAmelCase    :						int							=      json.loads(f.read()		)

										_UpperCAmelCase    :						List[Any]							=      {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

										_UpperCAmelCase    :						Tuple							=      pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

										# encode them
										_UpperCAmelCase    :						Optional[Any]							=      YolosImageProcessor(format="coco_panoptic"		)
										_UpperCAmelCase    :						Optional[Any]							=      image_processing(images=A_  ,   annotations=A_  ,   masks_path=A_  ,   return_tensors="pt"		)

										# verify pixel values
										_UpperCAmelCase    :						Tuple							=      torch.Size([1, 3, 800, 1066]		)
										self.assertEqual(encoding["pixel_values"].shape  ,   A_		)

										_UpperCAmelCase    :						List[Any]							=      torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,   A_  ,   atol=1e-4		)		)

										# verify area
										_UpperCAmelCase    :						int							=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,   A_		)		)
										# verify boxes
										_UpperCAmelCase    :						List[str]							=      torch.Size([6, 4]		)
										self.assertEqual(encoding["labels"][0]["boxes"].shape  ,   A_		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,   A_  ,   atol=1e-3		)		)
										# verify image_id
										_UpperCAmelCase    :						Tuple							=      torch.tensor([39769]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,   A_		)		)
										# verify is_crowd
										_UpperCAmelCase    :						Optional[Any]							=      torch.tensor([0, 0, 0, 0, 0, 0]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,   A_		)		)
										# verify class_labels
										_UpperCAmelCase    :						Optional[Any]							=      torch.tensor([17, 17, 63, 75, 75, 93]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,   A_		)		)
										# verify masks
										_UpperCAmelCase    :						List[Any]							=      822873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()  ,   A_		)
										# verify orig_size
										_UpperCAmelCase    :						str							=      torch.tensor([480, 640]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,   A_		)		)
										# verify size
										_UpperCAmelCase    :						Optional[int]							=      torch.tensor([800, 1066]		)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,   A_		)		)





from typing import List, Optional, Union

import torch

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,
)


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

SCREAMING_SNAKE_CASE_            = '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\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 = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\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        >>> 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        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					List[str]					, lowerCAmelCase:					int=8	)				->    int:
				_UpperCAmelCase    :						int							=      height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				_UpperCAmelCase    :						List[str]							=      width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


										super().__init__()

										self.register_modules(
										    unet=A_  ,   scheduler=A_  ,   movq=A_  ,   )
										_UpperCAmelCase    :						Optional[int]							=      2 ** (len(self.movq.config.block_out_channels		) - 1)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase    :						Any							=      randn_tensor(A_  ,   generator=A_  ,   device=A_  ,   dtype=A_		)
										else:
														if latents.shape != shape:
																		raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
														_UpperCAmelCase    :						Optional[int]							=      latents.to(A_		)

										_UpperCAmelCase    :						Tuple							=      latents * scheduler.init_noise_sigma
										return latents


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


										if is_accelerate_available():
														from accelerate import cpu_offload
										else:
														raise ImportError("Please install accelerate via `pip install accelerate`"		)

										_UpperCAmelCase    :						Union[str, Any]							=      torch.device(f'cuda:{gpu_id}'		)

										_UpperCAmelCase    :						Any							=      [
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(A_  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


										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=A_		)
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

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

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


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if not hasattr(self.unet  ,   "_hf_hook"		):
														return self.device
										for module in self.unet.modules():
														if (
														    hasattr(A_  ,   "_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(A_		)
						def __call__(    self  ,   A_  ,   A_  ,   A_  ,   A_ = 512  ,   A_ = 512  ,   A_ = 100  ,   A_ = 4.0  ,   A_ = 1  ,   A_ = None  ,   A_ = None  ,   A_ = "pil"  ,   A_ = True  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self._execution_device

										_UpperCAmelCase    :						Tuple							=      guidance_scale > 1.0

										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Dict							=      torch.cat(A_  ,   dim=0		)
										if isinstance(A_  ,   A_		):
														_UpperCAmelCase    :						Any							=      torch.cat(A_  ,   dim=0		)

										_UpperCAmelCase    :						Optional[Any]							=      image_embeds.shape[0] * num_images_per_prompt

										if do_classifier_free_guidance:
														_UpperCAmelCase    :						Optional[int]							=      image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Union[str, Any]							=      negative_image_embeds.repeat_interleave(A_  ,   dim=0		)
														_UpperCAmelCase    :						Tuple							=      hint.repeat_interleave(A_  ,   dim=0		)

										_UpperCAmelCase    :						List[Any]							=      torch.cat([negative_image_embeds, image_embeds]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										_UpperCAmelCase    :						List[Any]							=      torch.cat([hint, hint]  ,   dim=0		).to(dtype=self.unet.dtype  ,   device=A_		)
										self.scheduler.set_timesteps(A_  ,   device=A_		)
										_UpperCAmelCase    :						Dict							=      self.scheduler.timesteps

										_UpperCAmelCase    :						Union[str, Any]							=      self.movq.config.latent_channels

										_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      downscale_height_and_width(A_  ,   A_  ,   self.movq_scale_factor		)

										# create initial latent
										_UpperCAmelCase    :						Tuple							=      self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)  ,   image_embeds.dtype  ,   A_  ,   A_  ,   A_  ,   self.scheduler  ,   )

										for i, t in enumerate(self.progress_bar(A_		)		):
														# expand the latents if we are doing classifier free guidance
														_UpperCAmelCase    :						List[str]							=      torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

														_UpperCAmelCase    :						Optional[Any]							=      {"image_embeds": image_embeds, "hint": hint}
														_UpperCAmelCase    :						Optional[int]							=      self.unet(
														    sample=A_  ,   timestep=A_  ,   encoder_hidden_states=A_  ,   added_cond_kwargs=A_  ,   return_dict=A_  ,   )[0]

														if do_classifier_free_guidance:
																		_UpperCAmelCase						,							_UpperCAmelCase    :						Any							=      noise_pred.split(latents.shape[1]  ,   dim=1		)
																		_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      noise_pred.chunk(2		)
																		_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      variance_pred.chunk(2		)
																		_UpperCAmelCase    :						Optional[Any]							=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		_UpperCAmelCase    :						int							=      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    :						Optional[Any]							=      noise_pred.split(latents.shape[1]  ,   dim=1		)

														# compute the previous noisy sample x_t -> x_t-1
														_UpperCAmelCase    :						int							=      self.scheduler.step(
														    A_  ,   A_  ,   A_  ,   generator=A_  ,   )[0]
										# post-processing
										_UpperCAmelCase    :						Optional[Any]							=      self.movq.decode(A_  ,   force_not_quantize=A_		)["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    :						Union[str, Any]							=      image * 0.5 + 0.5
														_UpperCAmelCase    :						Dict							=      image.clamp(0  ,   1		)
														_UpperCAmelCase    :						List[str]							=      image.cpu().permute(0  ,   2  ,   3  ,   1		).float().numpy()

										if output_type == "pil":
														_UpperCAmelCase    :						str							=      self.numpy_to_pil(A_		)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=A_		)


