
"""simple docstring"""




from typing import List, Optional, Union

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

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


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

__lowerCAmelCase							:  Dict 				=		'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

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


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


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

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

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

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


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

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

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

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

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

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

def __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase=8    )       ->    List[Any]:


		"""simple docstring"""





		a__   		=     height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		a__   		=     width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor

def __snake_case (      UpperCamelCase						,					UpperCamelCase=512						,					UpperCamelCase=512    )       ->    Optional[Any]:


		"""simple docstring"""





		a__   		=     pil_image.resize((w, h)						,					resample=Image.BICUBIC						,					reducing_gap=1    )
		a__   		=     np.array(pil_image.convert('''RGB'''    )    )
		a__   		=     arr.astype(np.floataa    ) / 1_27.5 - 1
		a__   		=     np.transpose(UpperCamelCase						,					[2, 0, 1]    )
		a__   		=     torch.from_numpy(UpperCamelCase    ).unsqueeze(0    )
		return image




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def __init__(  self :List[Any]       ,    __magic_name__ :UNetaDConditionModel       ,    __magic_name__ :DDPMScheduler       ,    __magic_name__ :VQModel       ,    )			->   List[Any]:

			'''simple docstring'''






			super().__init__()

			self.register_modules(
			    unet=__magic_name__       ,    scheduler=__magic_name__       ,    movq=__magic_name__       ,    )
			a__   		=     2 ** (len(self.movq.config.block_out_channels			) - 1)


	def    _UpperCamelCase							(  self :Optional[Any]       ,    __magic_name__ :Dict       ,    __magic_name__ :List[str]       ,    __magic_name__ :Union[str, Any]			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     min(int(num_inference_steps * strength			)       ,    __magic_name__			)

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

			return timesteps, num_inference_steps - t_start


	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :List[Any]       ,    __magic_name__ :List[Any]       ,    __magic_name__ :Optional[Any]       ,    __magic_name__ :str       ,    __magic_name__ :Union[str, Any]       ,    __magic_name__ :str       ,    __magic_name__ :List[Any]=None			)			->   Dict:

			'''simple docstring'''






			if not isinstance(__magic_name__       ,    (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(__magic_name__			)}"			)

			a__   		=     image.to(device=__magic_name__       ,    dtype=__magic_name__			)

			a__   		=     batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					a__   		=     image

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

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

					a__   		=     self.movq.config.scaling_factor * init_latents

			a__   		=     torch.cat([init_latents]       ,    dim=0			)

			a__   		=     init_latents.shape
			a__   		=     randn_tensor(__magic_name__       ,    generator=__magic_name__       ,    device=__magic_name__       ,    dtype=__magic_name__			)

			# get latents
			a__   		=     self.scheduler.add_noise(__magic_name__       ,    __magic_name__       ,    __magic_name__			)

			a__   		=     init_latents

			return latents


	def    _UpperCamelCase							(  self :Optional[Any]       ,    __magic_name__ :int=0			)			->   Dict:

			'''simple docstring'''






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

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

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


	def    _UpperCamelCase							(  self :Optional[Any]       ,    __magic_name__ :Optional[int]=0			)			->   Any:

			'''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.'''			)

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

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

			a__   		=     None
			for cpu_offloaded_model in [self.unet, self.movq]:
					a__		,	a__   		=     cpu_offload_with_hook(__magic_name__       ,    __magic_name__       ,    prev_module_hook=__magic_name__			)

			# We'll offload the last model manually.
			a__   		=     hook


	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   Optional[Any]:

			'''simple docstring'''






			if not hasattr(self.unet       ,    '''_hf_hook'''			):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(__magic_name__       ,    '''_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(__magic_name__			)
	def __call__(  self :Union[str, Any]       ,    __magic_name__ :Union[torch.FloatTensor, List[torch.FloatTensor]]       ,    __magic_name__ :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]       ,    __magic_name__ :Union[torch.FloatTensor, List[torch.FloatTensor]]       ,    __magic_name__ :torch.FloatTensor       ,    __magic_name__ :int = 512       ,    __magic_name__ :int = 512       ,    __magic_name__ :int = 100       ,    __magic_name__ :float = 4.0       ,    __magic_name__ :float = 0.3       ,    __magic_name__ :int = 1       ,    __magic_name__ :Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,    __magic_name__ :Optional[str] = "pil"       ,    __magic_name__ :bool = True       ,    )			->   int:

			'''simple docstring'''






			a__   		=     self._execution_device

			a__   		=     guidance_scale > 1.0

			if isinstance(__magic_name__       ,    __magic_name__			):
					a__   		=     torch.cat(__magic_name__       ,    dim=0			)
			if isinstance(__magic_name__       ,    __magic_name__			):
					a__   		=     torch.cat(__magic_name__       ,    dim=0			)
			if isinstance(__magic_name__       ,    __magic_name__			):
					a__   		=     torch.cat(__magic_name__       ,    dim=0			)

			a__   		=     image_embeds.shape[0]

			if do_classifier_free_guidance:
					a__   		=     image_embeds.repeat_interleave(__magic_name__       ,    dim=0			)
					a__   		=     negative_image_embeds.repeat_interleave(__magic_name__       ,    dim=0			)
					a__   		=     hint.repeat_interleave(__magic_name__       ,    dim=0			)

			a__   		=     torch.cat([negative_image_embeds, image_embeds]       ,    dim=0			).to(dtype=self.unet.dtype       ,    device=__magic_name__			)
			a__   		=     torch.cat([hint, hint]       ,    dim=0			).to(dtype=self.unet.dtype       ,    device=__magic_name__			)

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

			a__   		=     torch.cat([prepare_image(__magic_name__       ,    __magic_name__       ,    __magic_name__			) for i in image]       ,    dim=0			)
			a__   		=     image.to(dtype=image_embeds.dtype       ,    device=__magic_name__			)

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

					a__   		=     {'''image_embeds''': image_embeds, '''hint''': hint}
					a__   		=     self.unet(
					    sample=__magic_name__       ,    timestep=__magic_name__       ,    encoder_hidden_states=__magic_name__       ,    added_cond_kwargs=__magic_name__       ,    return_dict=__magic_name__       ,    )[0]

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

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

					a__   		=     self.scheduler.step(
					    __magic_name__       ,    __magic_name__       ,    __magic_name__       ,    generator=__magic_name__       ,    )[0]

			# post-processing
			a__   		=     self.movq.decode(__magic_name__       ,    force_not_quantize=__magic_name__			)['''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"]:
					a__   		=     image * 0.5 + 0.5
					a__   		=     image.clamp(0       ,    1			)
					a__   		=     image.cpu().permute(0       ,    2       ,    3       ,    1			).float().numpy()

			if output_type == "pil":
					a__   		=     self.numpy_to_pil(__magic_name__			)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=__magic_name__			)






"""simple docstring"""




import unittest

import numpy as np

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 PIL import Image

					from transformers import MobileNetVaImageProcessor
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def __init__(  self :Tuple       ,    __magic_name__ :str       ,    __magic_name__ :Optional[Any]=7       ,    __magic_name__ :int=3       ,    __magic_name__ :Union[str, Any]=18       ,    __magic_name__ :Union[str, Any]=30       ,    __magic_name__ :str=400       ,    __magic_name__ :str=True       ,    __magic_name__ :Any=None       ,    __magic_name__ :Tuple=True       ,    __magic_name__ :List[str]=None       ,    )			->   Optional[int]:

			'''simple docstring'''






			a__   		=     size if size is not None else {'''shortest_edge''': 20}
			a__   		=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			a__   		=     parent
			a__   		=     batch_size
			a__   		=     num_channels
			a__   		=     image_size
			a__   		=     min_resolution
			a__   		=     max_resolution
			a__   		=     do_resize
			a__   		=     size
			a__   		=     do_center_crop
			a__   		=     crop_size






	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		str         =		MobileNetVaImageProcessor if is_vision_available() else None


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

			'''simple docstring'''






			a__   		=     MobileNetVaImageProcessingTester(self			)


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[Any]:

			'''simple docstring'''






			return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCamelCase							(  self :Tuple			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_resize'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''size'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''do_center_crop'''			)			)
			self.assertTrue(hasattr(__magic_name__       ,    '''center_crop'''			)			)


	def    _UpperCamelCase							(  self :List[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 20}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 18, '''width''': 18}			)

			a__   		=     self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42       ,    crop_size=84			)
			self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 42}			)
			self.assertEqual(image_processor.crop_size       ,    {'''height''': 84, '''width''': 84}			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :Dict			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PIL images
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    Image.Image			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random numpy tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    numpify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    np.ndarray			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )






	def    _UpperCamelCase							(  self :List[Any]			)			->   str:

			'''simple docstring'''






			a__   		=     self.image_processing_class(**self.image_processor_dict			)
			# create random PyTorch tensors
			a__   		=     prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__magic_name__       ,    torchify=__magic_name__			)
			for image in image_inputs:
					self.assertIsInstance(__magic_name__       ,    torch.Tensor			)

			# Test not batched input
			a__   		=     image_processing(image_inputs[0]       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )

			# Test batched
			a__   		=     image_processing(__magic_name__       ,    return_tensors='''pt'''			).pixel_values
			self.assertEqual(
			    encoded_images.shape       ,    (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )       ,    )




