




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 DetrImageProcessor




class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      def __init__(				self    ,				A    ,				A=7    ,				A=3    ,				A=3_0    ,				A=4_0_0    ,				A=True    ,				A=None    ,				A=True    ,				A=1 / 2_5_5    ,				A=True    ,				A=[0.5, 0.5, 0.5]    ,				A=[0.5, 0.5, 0.5]    ,				A=True    ,				)		->    Optional[int]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         snake_case				:    List[str]           =	size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
         snake_case				:    List[str]           =	parent
         snake_case				:    List[str]           =	batch_size
         snake_case				:    Optional[Any]           =	num_channels
         snake_case				:    str           =	min_resolution
         snake_case				:    Optional[Any]           =	max_resolution
         snake_case				:    List[Any]           =	do_resize
         snake_case				:    Tuple           =	size
         snake_case				:    List[Any]           =	do_rescale
         snake_case				:    str           =	rescale_factor
         snake_case				:    Tuple           =	do_normalize
         snake_case				:    str           =	image_mean
         snake_case				:    Union[str, Any]           =	image_std
         snake_case				:    int           =	do_pad

      def      UpperCAmelCase				(				self     )		->    Dict:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }

      def      UpperCAmelCase				(				self    ,				A    ,				A=False     )		->    List[str]:
         if not batched:
            snake_case				:    Tuple           =	image_inputs[0]
            if isinstance(A    ,				Image.Image     ):
               snake_case     , snake_case				:    Tuple           =	image.size
            else:
               snake_case     , snake_case				:    int           =	image.shape[1], image.shape[2]
            if w < h:
               snake_case				:    Dict           =	int(self.size["""shortest_edge"""] * h / w     )
               snake_case				:    int           =	self.size["""shortest_edge"""]
            elif w > h:
               snake_case				:    Any           =	self.size["""shortest_edge"""]
               snake_case				:    Tuple           =	int(self.size["""shortest_edge"""] * w / h     )
            else:
               snake_case				:    Optional[Any]           =	self.size["""shortest_edge"""]
               snake_case				:    Union[str, Any]           =	self.size["""shortest_edge"""]

         else:
            snake_case				:    Any           =	[]
            for image in image_inputs:
               snake_case     , snake_case				:    Tuple           =	self.get_expected_values([image]     )
               expected_values.append((expected_height, expected_width)     )
            snake_case				:    Tuple           =	max(A    ,				key=lambda A     : item[0]     )[0]
            snake_case				:    Dict           =	max(A    ,				key=lambda A     : item[1]     )[1]

         return expected_height, expected_width




@require_torch
@require_vision
class 						__lowercase		(UpperCamelCase__					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						DetrImageProcessor if is_vision_available() else None

      def      UpperCAmelCase				(				self     )		->    Tuple:
         snake_case				:    str           =	DetrImageProcessingTester(self     )

      @property
      def      UpperCAmelCase				(				self     )		->    Any:
         return self.image_processor_tester.prepare_image_processor_dict()

      def      UpperCAmelCase				(				self     )		->    str:
         snake_case				:    List[str]           =	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_rescale"""     )     )
         self.assertTrue(hasattr(A    ,				"""rescale_factor"""     )     )
         self.assertTrue(hasattr(A    ,				"""do_resize"""     )     )
         self.assertTrue(hasattr(A    ,				"""size"""     )     )
         self.assertTrue(hasattr(A    ,				"""do_pad"""     )     )

      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    Optional[int]           =	self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size    ,				{"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}     )
         self.assertEqual(image_processor.do_pad    ,				A     )

         snake_case				:    str           =	self.image_processing_class.from_dict(
             self.image_processor_dict    ,				size=4_2    ,				max_size=8_4    ,				pad_and_return_pixel_mask=A     )
         self.assertEqual(image_processor.size    ,				{"""shortest_edge""": 4_2, """longest_edge""": 8_4}     )
         self.assertEqual(image_processor.do_pad    ,				A     )

      def      UpperCAmelCase				(				self     )		->    Dict:
         pass

      def      UpperCAmelCase				(				self     )		->    List[str]:
         # Initialize image_processing
         snake_case				:    Dict           =	self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				:    int           =	prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=A     )
         for image in image_inputs:
            self.assertIsInstance(A    ,				Image.Image     )

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

         snake_case     , snake_case				:    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
         snake_case     , snake_case				:    List[Any]           =	self.image_processor_tester.get_expected_values(A    ,				batched=A     )

         snake_case				:    int           =	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     )		->    Optional[Any]:
         # Initialize image_processing
         snake_case				:    Union[str, Any]           =	self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				:    str           =	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
         snake_case				:    List[str]           =	image_processing(image_inputs[0]    ,				return_tensors="""pt"""     ).pixel_values

         snake_case     , snake_case				:    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
         snake_case				:    Tuple           =	image_processing(A    ,				return_tensors="""pt"""     ).pixel_values

         snake_case     , snake_case				:    List[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     )		->    Any:
         # Initialize image_processing
         snake_case				:    Union[str, Any]           =	self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				:    Optional[Any]           =	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
         snake_case				:    List[Any]           =	image_processing(image_inputs[0]    ,				return_tensors="""pt"""     ).pixel_values

         snake_case     , snake_case				:    Optional[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
         snake_case				:    str           =	image_processing(A    ,				return_tensors="""pt"""     ).pixel_values

         snake_case     , snake_case				:    List[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,
             )    ,				)

      @slow
      def      UpperCAmelCase				(				self     )		->    List[str]:
         # prepare image and target
         snake_case				:    Optional[int]           =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    ,				"""r"""     ) as f:
            snake_case				:    List[str]           =	json.loads(f.read()     )

         snake_case				:    Tuple           =	{"""image_id""": 3_9_7_6_9, """annotations""": target}

         # encode them
         snake_case				:    str           =	DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""     )
         snake_case				:    Optional[Any]           =	image_processing(images=A    ,				annotations=A    ,				return_tensors="""pt"""     )

         # verify pixel values
         snake_case				:    List[str]           =	torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
         self.assertEqual(encoding["""pixel_values"""].shape    ,				A     )

         snake_case				:    str           =	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
         snake_case				:    Tuple           =	torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,				A     )     )
         # verify boxes
         snake_case				:    int           =	torch.Size([6, 4]     )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,				A     )
         snake_case				:    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
         snake_case				:    Optional[int]           =	torch.tensor([3_9_7_6_9]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,				A     )     )
         # verify is_crowd
         snake_case				:    int           =	torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,				A     )     )
         # verify class_labels
         snake_case				:    int           =	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,				A     )     )
         # verify orig_size
         snake_case				:    int           =	torch.tensor([4_8_0, 6_4_0]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,				A     )     )
         # verify size
         snake_case				:    Dict           =	torch.tensor([8_0_0, 1_0_6_6]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    ,				A     )     )

      @slow
      def      UpperCAmelCase				(				self     )		->    List[Any]:
         # prepare image, target and masks_path
         snake_case				:    Optional[int]           =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
         with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    ,				"""r"""     ) as f:
            snake_case				:    Any           =	json.loads(f.read()     )

         snake_case				:    Optional[int]           =	{"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

         snake_case				:    Union[str, Any]           =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""     )

         # encode them
         snake_case				:    Any           =	DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""     )
         snake_case				:    Dict           =	image_processing(images=A    ,				annotations=A    ,				masks_path=A    ,				return_tensors="""pt"""     )

         # verify pixel values
         snake_case				:    str           =	torch.Size([1, 3, 8_0_0, 1_0_6_6]     )
         self.assertEqual(encoding["""pixel_values"""].shape    ,				A     )

         snake_case				:    Union[str, 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
         snake_case				:    Optional[int]           =	torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    ,				A     )     )
         # verify boxes
         snake_case				:    Union[str, Any]           =	torch.Size([6, 4]     )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    ,				A     )
         snake_case				:    Union[str, 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
         snake_case				:    Tuple           =	torch.tensor([3_9_7_6_9]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    ,				A     )     )
         # verify is_crowd
         snake_case				:    str           =	torch.tensor([0, 0, 0, 0, 0, 0]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    ,				A     )     )
         # verify class_labels
         snake_case				:    int           =	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    ,				A     )     )
         # verify masks
         snake_case				:    Union[str, Any]           =	8_2_2_8_7_3
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    ,				A     )
         # verify orig_size
         snake_case				:    Dict           =	torch.tensor([4_8_0, 6_4_0]     )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    ,				A     )     )
         # verify size
         snake_case				:    Union[str, Any]           =	torch.tensor([8_0_0, 1_0_6_6]     )
         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,
)


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

lowerCamelCase	:	Dict				=   '\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__ (   lowercase						,lowercase						,lowercase=8					)  ->       Optional[int]:
   snake_case				:    Dict           =	height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   snake_case				:    int           =	width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





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

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

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    str:
         if latents is None:
            snake_case				:    List[str]           =	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}"""     )
            snake_case				:    Union[str, Any]           =	latents.to(A     )

         snake_case				:    Optional[Any]           =	latents * scheduler.init_noise_sigma
         return latents

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

         snake_case				:    Dict           =	torch.device(f"""cuda:{gpu_id}"""     )

         snake_case				:    Tuple           =	[
             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     )		->    Optional[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."""     )

         snake_case				:    int           =	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)

         snake_case				:    Tuple           =	None
         for cpu_offloaded_model in [self.unet, self.movq]:
            snake_case     , snake_case				:    Tuple           =	cpu_offload_with_hook(A    ,				A    ,				prev_module_hook=A     )

         # We'll offload the last model manually.
         snake_case				:    Tuple           =	hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def      UpperCAmelCase				(				self     )		->    str:
         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 = 5_1_2    ,				A = 5_1_2    ,				A = 1_0_0    ,				A = 4.0    ,				A = 1    ,				A = None    ,				A = None    ,				A = "pil"    ,				A = True    ,				)		->    Any:
         snake_case				:    int           =	self._execution_device

         snake_case				:    Tuple           =	guidance_scale > 1.0

         if isinstance(A    ,				A     ):
            snake_case				:    str           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    int           =	torch.cat(A    ,				dim=0     )
         if isinstance(A    ,				A     ):
            snake_case				:    Tuple           =	torch.cat(A    ,				dim=0     )

         snake_case				:    List[Any]           =	image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
            snake_case				:    Optional[int]           =	image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Dict           =	negative_image_embeds.repeat_interleave(A    ,				dim=0     )
            snake_case				:    Optional[Any]           =	hint.repeat_interleave(A    ,				dim=0     )

         snake_case				:    List[str]           =	torch.cat([negative_image_embeds, image_embeds]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         snake_case				:    Optional[Any]           =	torch.cat([hint, hint]    ,				dim=0     ).to(dtype=self.unet.dtype    ,				device=A     )
         self.scheduler.set_timesteps(A    ,				device=A     )
         snake_case				:    Tuple           =	self.scheduler.timesteps

         snake_case				:    Tuple           =	self.movq.config.latent_channels

         snake_case     , snake_case				:    Optional[int]           =	downscale_height_and_width(A    ,				A    ,				self.movq_scale_factor     )

         # create initial latent
         snake_case				:    str           =	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
            snake_case				:    str           =	torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

            snake_case				:    Any           =	{"""image_embeds""": image_embeds, """hint""": hint}
            snake_case				:    List[Any]           =	self.unet(
                sample=A    ,				timestep=A    ,				encoder_hidden_states=A    ,				added_cond_kwargs=A    ,				return_dict=A    ,				)[0]

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

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

         if output_type == "pil":
            snake_case				:    Tuple           =	self.numpy_to_pil(A     )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=A     )





