from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
					import PIL

SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    List[List[ImageInput]]:


				if isinstance(__A					, (list, tuple)	) and isinstance(videos[0]					, (list, tuple)	) and is_valid_image(videos[0][0]	):
								return videos

				elif isinstance(__A					, (list, tuple)	) and is_valid_image(videos[0]	):
								return [videos]

				elif is_valid_image(__A	):
								return [[videos]]

				raise ValueError(F'Could not make batched video from {videos}'	)





class 			a					(		UpperCAmelCase			):
						_lowercase        =     ['''pixel_values''']


						def __init__(    self  ,   A_ = True  ,   A_ = None  ,   A_ = PILImageResampling.BILINEAR  ,   A_ = True  ,   A_ = None  ,   A_ = True  ,   A_ = 1 / 255  ,   A_ = True  ,   A_ = True  ,   A_ = None  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										super().__init__(**_snake_case		)
										_UpperCAmelCase    :						Optional[int]							=      size if size is not None else {"shortest_edge": 256}
										_UpperCAmelCase    :						Any							=      get_size_dict(_snake_case  ,   default_to_square=_snake_case		)
										_UpperCAmelCase    :						Optional[int]							=      crop_size if crop_size is not None else {"height": 224, "width": 224}
										_UpperCAmelCase    :						List[Any]							=      get_size_dict(_snake_case  ,   param_name="crop_size"		)

										_UpperCAmelCase    :						Union[str, Any]							=      do_resize
										_UpperCAmelCase    :						Optional[int]							=      size
										_UpperCAmelCase    :						Optional[int]							=      do_center_crop
										_UpperCAmelCase    :						Union[str, Any]							=      crop_size
										_UpperCAmelCase    :						List[str]							=      resample
										_UpperCAmelCase    :						Any							=      do_rescale
										_UpperCAmelCase    :						List[Any]							=      rescale_factor
										_UpperCAmelCase    :						str							=      offset
										_UpperCAmelCase    :						Tuple							=      do_normalize
										_UpperCAmelCase    :						int							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase    :						Tuple							=      image_std if image_std is not None else IMAGENET_STANDARD_STD


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = PILImageResampling.BILINEAR  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      get_size_dict(_snake_case  ,   default_to_square=_snake_case		)
										if "shortest_edge" in size:
														_UpperCAmelCase    :						Optional[int]							=      get_resize_output_image_size(_snake_case  ,   size["shortest_edge"]  ,   default_to_square=_snake_case		)
										elif "height" in size and "width" in size:
														_UpperCAmelCase    :						Optional[int]							=      (size["height"], size["width"])
										else:
														raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
										return resize(_snake_case  ,   size=_snake_case  ,   resample=_snake_case  ,   data_format=_snake_case  ,   **_snake_case		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      get_size_dict(_snake_case		)
										if "height" not in size or "width" not in size:
														raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'		)
										return center_crop(_snake_case  ,   size=(size["height"], size["width"])  ,   data_format=_snake_case  ,   **_snake_case		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = True  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      image.astype(np.floataa		)
										if offset:
														_UpperCAmelCase    :						str							=      image - (scale / 2)
										return rescale(_snake_case  ,   scale=_snake_case  ,   data_format=_snake_case  ,   **_snake_case		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_ = None  ,   **A_  ,   ):







										'''simple docstring'''


										return normalize(_snake_case  ,   mean=_snake_case  ,   std=_snake_case  ,   data_format=_snake_case  ,   **_snake_case		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = ChannelDimension.FIRST  ,   ):







										'''simple docstring'''


										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_center_crop and crop_size is None:
														raise ValueError("Crop size must be specified if do_center_crop 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."		)

										if offset and not do_rescale:
														raise ValueError("For offset, do_rescale must also be set to True."		)

										# All transformations expect numpy arrays.
										_UpperCAmelCase    :						Optional[int]							=      to_numpy_array(_snake_case		)

										if do_resize:
														_UpperCAmelCase    :						str							=      self.resize(image=_snake_case  ,   size=_snake_case  ,   resample=_snake_case		)

										if do_center_crop:
														_UpperCAmelCase    :						Union[str, Any]							=      self.center_crop(_snake_case  ,   size=_snake_case		)

										if do_rescale:
														_UpperCAmelCase    :						Tuple							=      self.rescale(image=_snake_case  ,   scale=_snake_case  ,   offset=_snake_case		)

										if do_normalize:
														_UpperCAmelCase    :						int							=      self.normalize(image=_snake_case  ,   mean=_snake_case  ,   std=_snake_case		)

										_UpperCAmelCase    :						Tuple							=      to_channel_dimension_format(_snake_case  ,   _snake_case		)
										return image





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = ChannelDimension.FIRST  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase    :						List[str]							=      resample if resample is not None else self.resample
										_UpperCAmelCase    :						str							=      do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCAmelCase    :						int							=      do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase    :						Optional[int]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase    :						Union[str, Any]							=      offset if offset is not None else self.offset
										_UpperCAmelCase    :						List[Any]							=      do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase    :						Tuple							=      image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase    :						List[str]							=      image_std if image_std is not None else self.image_std

										_UpperCAmelCase    :						List[str]							=      size if size is not None else self.size
										_UpperCAmelCase    :						int							=      get_size_dict(_snake_case  ,   default_to_square=_snake_case		)
										_UpperCAmelCase    :						List[Any]							=      crop_size if crop_size is not None else self.crop_size
										_UpperCAmelCase    :						List[str]							=      get_size_dict(_snake_case  ,   param_name="crop_size"		)

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

										_UpperCAmelCase    :						str							=      make_batched(_snake_case		)

										_UpperCAmelCase    :						Optional[Any]							=      [
										    [
										        self._preprocess_image(
										            image=_snake_case  ,   do_resize=_snake_case  ,   size=_snake_case  ,   resample=_snake_case  ,   do_center_crop=_snake_case  ,   crop_size=_snake_case  ,   do_rescale=_snake_case  ,   rescale_factor=_snake_case  ,   offset=_snake_case  ,   do_normalize=_snake_case  ,   image_mean=_snake_case  ,   image_std=_snake_case  ,   data_format=_snake_case  ,   )
										        for img in video
										    ]
										    for video in videos
										]

										_UpperCAmelCase    :						List[str]							=      {"pixel_values": videos}
										return BatchFeature(data=_snake_case  ,   tensor_type=_snake_case		)





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_		)


