
"""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,
)


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

_lowercase				 =					'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''






def    _snake_case					(		snake_case__ :  Union[str, Any]						,   snake_case__ :  Union[str, Any]						,   snake_case__ :  str=8  ):
	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					(		snake_case__ :  List[Any]						,   snake_case__ :  Optional[int]=512						,   snake_case__ :  Tuple=512  ):
	A     = pil_image.resize((w, h)						,   resample=Image.BICUBIC						,   reducing_gap=1  )
	A     = np.array(pil_image.convert('RGB'  )  )
	A     = arr.astype(np.floataa  ) / 127.5 - 1
	A     = np.transpose(snake_case__						,   [2, 0, 1]  )
	A     = torch.from_numpy(snake_case__  ).unsqueeze(0  )
	return image







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   UNetaDConditionModel	,A_  :   DDPMScheduler	,A_  :   VQModel	,)  -> Union[str, Any]:
							super().__init__()

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   int	,A_  :   Tuple	,A_  :   Tuple   )  -> List[Any]:
							# get the original timestep using init_timestep
							A     = min(int(num_inference_steps * strength   )	,A_   )

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

							return timesteps, num_inference_steps - t_start


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   Optional[int]	,A_  :   Tuple	,A_  :   Optional[int]	,A_  :   str	,A_  :   Union[str, Any]	,A_  :   Optional[Any]	,A_  :   Union[str, Any]=None   )  -> List[Any]:
							if not isinstance(A_	,(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(A_   )}'   )

							A     = image.to(device=A_	,dtype=A_   )

							A     = batch_size * num_images_per_prompt

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

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

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

								A     = self.movq.config.scaling_factor * init_latents

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

							A     = init_latents.shape
							A     = randn_tensor(A_	,generator=A_	,device=A_	,dtype=A_   )

							# get latents
							A     = self.scheduler.add_noise(A_	,A_	,A_   )

							A     = init_latents

							return latents


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   str=0   )  -> Optional[Any]:
							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(A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Tuple=0   )  -> List[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.'   )

							A     = 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)

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

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


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> 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  :   Optional[Any]	,A_  :   Union[torch.FloatTensor, List[torch.FloatTensor]]	,A_  :   Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]	,A_  :   Union[torch.FloatTensor, List[torch.FloatTensor]]	,A_  :   int = 512	,A_  :   int = 512	,A_  :   int = 100	,A_  :   float = 4.0	,A_  :   float = 0.3	,A_  :   int = 1	,A_  :   Optional[Union[torch.Generator, List[torch.Generator]]] = None	,A_  :   Optional[str] = "pil"	,A_  :   bool = True	,)  -> List[Any]:
							A     = self._execution_device

							A     = guidance_scale > 1.0

							if isinstance(A_	,A_   ):
								A     = torch.cat(A_	,dim=0   )
							A     = image_embeds.shape[0]
							if isinstance(A_	,A_   ):
								A     = torch.cat(A_	,dim=0   )

							if do_classifier_free_guidance:
								A     = image_embeds.repeat_interleave(A_	,dim=0   )
								A     = negative_image_embeds.repeat_interleave(A_	,dim=0   )

							A     = torch.cat([negative_image_embeds, image_embeds]	,dim=0   ).to(dtype=self.unet.dtype	,device=A_   )

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

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

							A     = self.movq.encode(A_   )['latents']
							A     = latents.repeat_interleave(A_	,dim=0   )
							self.scheduler.set_timesteps(A_	,device=A_   )
							A ,   A     = self.get_timesteps(A_	,A_	,A_   )
							A     = timesteps[:1].repeat(batch_size * num_images_per_prompt   )
							A ,   A     = downscale_height_and_width(A_	,A_	,self.movq_scale_factor   )
							A     = self.prepare_latents(
							    A_	,A_	,A_	,A_	,image_embeds.dtype	,A_	,A_   )
							for i, t in enumerate(self.progress_bar(A_   )   ):
								# 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}
								A     = self.unet(
								    sample=A_	,timestep=A_	,encoder_hidden_states=A_	,added_cond_kwargs=A_	,return_dict=A_	,)[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(
								    A_	,A_	,A_	,generator=A_	,)[0]

							# post-processing
							A     = 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"]:
								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(A_   )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_   )






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_lowercase		:  Tuple			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):
					_a      :						List[str]						    =					['pixel_values']



					def __init__(		self     :     List[Any]       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Dict[str, int] = None       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BICUBIC       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Union[int, float] = 1 / 2_55       ,      lowerCamelCase     :     bool = True       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     bool = True       ,      **lowerCamelCase     :     Optional[Any]       ,      ):
							super().__init__(**lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      size if size is not None else {'height': 3_84, 'width': 3_84}
							lowerCamelCase_	:     List[str] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      do_resize
							lowerCamelCase_	:     int 						=      size
							lowerCamelCase_	:     Union[str, Any] 						=      resample
							lowerCamelCase_	:     Tuple 						=      do_rescale
							lowerCamelCase_	:     Dict 						=      rescale_factor
							lowerCamelCase_	:     int 						=      do_normalize
							lowerCamelCase_	:     Any 						=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							lowerCamelCase_	:     str 						=      image_std if image_std is not None else OPENAI_CLIP_STD
							lowerCamelCase_	:     int 						=      do_convert_rgb



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Dict[str, int]       ,      lowerCamelCase     :     PILImageResampling = PILImageResampling.BICUBIC       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     List[Any]       ,      ):
							lowerCamelCase_	:     List[Any] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)
							if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"						)
							lowerCamelCase_	:     Optional[Any] 						=      (size['height'], size['width'])
							return resize(lowerCamelCase       ,      size=lowerCamelCase       ,      resample=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Union[int, float]       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Union[str, Any]       ,      ):
							return rescale(lowerCamelCase       ,      scale=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)



					def   __a     (		self     :     str       ,      lowerCamelCase     :     np.ndarray       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Union[float, List[float]]       ,      lowerCamelCase     :     Optional[Union[str, ChannelDimension]] = None       ,      **lowerCamelCase     :     Optional[int]       ,      ):
							return normalize(lowerCamelCase       ,      mean=lowerCamelCase       ,      std=lowerCamelCase       ,      data_format=lowerCamelCase       ,      **lowerCamelCase						)





					def   __a     (		self     :     List[Any]       ,      lowerCamelCase     :     ImageInput       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[Dict[str, int]] = None       ,      lowerCamelCase     :     PILImageResampling = None       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[float] = None       ,      lowerCamelCase     :     Optional[bool] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[float, List[float]]] = None       ,      lowerCamelCase     :     Optional[Union[str, TensorType]] = None       ,      lowerCamelCase     :     bool = None       ,      lowerCamelCase     :     ChannelDimension = ChannelDimension.FIRST       ,      **lowerCamelCase     :     Any       ,      ):
							lowerCamelCase_	:     Optional[Any] 						=      do_resize if do_resize is not None else self.do_resize
							lowerCamelCase_	:     int 						=      resample if resample is not None else self.resample
							lowerCamelCase_	:     Dict 						=      do_rescale if do_rescale is not None else self.do_rescale
							lowerCamelCase_	:     Any 						=      rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCamelCase_	:     str 						=      do_normalize if do_normalize is not None else self.do_normalize
							lowerCamelCase_	:     List[str] 						=      image_mean if image_mean is not None else self.image_mean
							lowerCamelCase_	:     str 						=      image_std if image_std is not None else self.image_std
							lowerCamelCase_	:     int 						=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							lowerCamelCase_	:     Union[str, Any] 						=      size if size is not None else self.size
							lowerCamelCase_	:     Optional[int] 						=      get_size_dict(lowerCamelCase       ,      default_to_square=lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      make_list_of_images(lowerCamelCase						)

							if not valid_images(lowerCamelCase						):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.'						)

							if do_resize and size is None or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.'						)

							if do_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.'						)

							if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and std must be specified if do_normalize is True.'						)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
									lowerCamelCase_	:     int 						=      [convert_to_rgb(lowerCamelCase						) for image in images]

							# All transformations expect numpy arrays.
							lowerCamelCase_	:     List[Any] 						=      [to_numpy_array(lowerCamelCase						) for image in images]

							if do_resize:
									lowerCamelCase_	:     Optional[int] 						=      [self.resize(image=lowerCamelCase       ,      size=lowerCamelCase       ,      resample=lowerCamelCase						) for image in images]

							if do_rescale:
									lowerCamelCase_	:     Dict 						=      [self.rescale(image=lowerCamelCase       ,      scale=lowerCamelCase						) for image in images]

							if do_normalize:
									lowerCamelCase_	:     Optional[int] 						=      [self.normalize(image=lowerCamelCase       ,      mean=lowerCamelCase       ,      std=lowerCamelCase						) for image in images]

							lowerCamelCase_	:     Optional[int] 						=      [to_channel_dimension_format(lowerCamelCase       ,      lowerCamelCase						) for image in images]

							lowerCamelCase_	:     Optional[int] 						=      BatchFeature(data={'pixel_values': images}       ,      tensor_type=lowerCamelCase						)

							return encoded_outputs
