
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,
)


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

__snake_case					:							int           =							'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=8							):




			"""simple docstring"""


			UpperCAmelCase__		:Dict 		=    height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCAmelCase__		:List[Any] 		=    width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=512     ,						SCREAMING_SNAKE_CASE=512							):




			"""simple docstring"""


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




class 	UpperCamelCase__       (  __SCREAMING_SNAKE_CASE):

						'''simple docstring'''



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

									self.register_modules(
									    unet=_A  , scheduler=_A  , movq=_A  , )
									UpperCAmelCase__		:List[Any] 		=    2 ** (len(self.movq.config.block_out_channels	) - 1)


						def    A__       (  self  , A  , A  , A	)  ->Optional[int]:
									# get the original timestep using init_timestep
									UpperCAmelCase__		:Union[str, Any] 		=    min(int(num_inference_steps * strength	)  , _A	)

									UpperCAmelCase__		:Optional[Any] 		=    max(num_inference_steps - init_timestep  , 0	)
									UpperCAmelCase__		:List[str] 		=    self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start


						def    A__       (  self  , A  , A  , A  , A  , A  , A  , A=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	)}"""	)

									UpperCAmelCase__		:List[str] 		=    image.to(device=_A  , dtype=_A	)

									UpperCAmelCase__		:Tuple 		=    batch_size * num_images_per_prompt

									if image.shape[1] == 4:
												UpperCAmelCase__		:str 		=    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	):
															UpperCAmelCase__		:List[Any] 		=    [
															    self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(_A	)
															]
															UpperCAmelCase__		:Optional[Any] 		=    torch.cat(_A  , dim=0	)
												else:
															UpperCAmelCase__		:List[str] 		=    self.movq.encode(_A	).latent_dist.sample(_A	)

												UpperCAmelCase__		:List[str] 		=    self.movq.config.scaling_factor * init_latents

									UpperCAmelCase__		:Dict 		=    torch.cat([init_latents]  , dim=0	)

									UpperCAmelCase__		:List[Any] 		=    init_latents.shape
									UpperCAmelCase__		:Any 		=    randn_tensor(_A  , generator=_A  , device=_A  , dtype=_A	)

									# get latents
									UpperCAmelCase__		:int 		=    self.scheduler.add_noise(_A  , _A  , _A	)

									UpperCAmelCase__		:str 		=    init_latents

									return latents


						def    A__       (  self  , A=0	)  ->Any:
									if is_accelerate_available():
												from accelerate import cpu_offload
									else:
												raise ImportError('Please install accelerate via `pip install accelerate`'	)

									UpperCAmelCase__		:Optional[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    A__       (  self  , A=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.'	)

									UpperCAmelCase__		:List[Any] 		=    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[Any] 		=    None
									for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    cpu_offload_with_hook(_A  , _A  , prev_module_hook=_A	)

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


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def    A__       (  self	)  ->Any:
									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  , A = 5_12  , A = 5_12  , A = 1_00  , A = 4.0  , A = 0.3  , A = 1  , A = None  , A = "pil"  , A = True  , )  ->List[str]:
									UpperCAmelCase__		:Dict 		=    self._execution_device

									UpperCAmelCase__		:Any 		=    guidance_scale > 1.0

									if isinstance(_A  , _A	):
												UpperCAmelCase__		:Optional[int] 		=    torch.cat(_A  , dim=0	)
									if isinstance(_A  , _A	):
												UpperCAmelCase__		:Optional[int] 		=    torch.cat(_A  , dim=0	)
									if isinstance(_A  , _A	):
												UpperCAmelCase__		:Dict 		=    torch.cat(_A  , dim=0	)

									UpperCAmelCase__		:Union[str, Any] 		=    image_embeds.shape[0]

									if do_classifier_free_guidance:
												UpperCAmelCase__		:List[Any] 		=    image_embeds.repeat_interleave(_A  , dim=0	)
												UpperCAmelCase__		:int 		=    negative_image_embeds.repeat_interleave(_A  , dim=0	)
												UpperCAmelCase__		:List[str] 		=    hint.repeat_interleave(_A  , dim=0	)

									UpperCAmelCase__		:Dict 		=    torch.cat([negative_image_embeds, image_embeds]  , dim=0	).to(dtype=self.unet.dtype  , device=_A	)
									UpperCAmelCase__		:Tuple 		=    torch.cat([hint, hint]  , dim=0	).to(dtype=self.unet.dtype  , device=_A	)

									if not isinstance(_A  , _A	):
												UpperCAmelCase__		:Union[str, Any] 		=    [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"""	)

									UpperCAmelCase__		:str 		=    torch.cat([prepare_image(_A  , _A  , _A	) for i in image]  , dim=0	)
									UpperCAmelCase__		:str 		=    image.to(dtype=image_embeds.dtype  , device=_A	)

									UpperCAmelCase__		:List[Any] 		=    self.movq.encode(_A	)['latents']
									UpperCAmelCase__		:Tuple 		=    latents.repeat_interleave(_A  , dim=0	)
									self.scheduler.set_timesteps(_A  , device=_A	)
									UpperCAmelCase__		,       UpperCAmelCase__		:Optional[int] 		=    self.get_timesteps(_A  , _A  , _A	)
									UpperCAmelCase__		:Union[str, Any] 		=    timesteps[:1].repeat(batch_size * num_images_per_prompt	)
									UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    downscale_height_and_width(_A  , _A  , self.movq_scale_factor	)
									UpperCAmelCase__		:str 		=    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
												UpperCAmelCase__		:int 		=    torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

												UpperCAmelCase__		:Tuple 		=    {'image_embeds': image_embeds, 'hint': hint}
												UpperCAmelCase__		:Any 		=    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__		:Optional[Any] 		=    noise_pred.split(latents.shape[1]  , dim=1	)
															UpperCAmelCase__		,       UpperCAmelCase__		:Optional[Any] 		=    noise_pred.chunk(2	)
															UpperCAmelCase__		,       UpperCAmelCase__		:List[str] 		=    variance_pred.chunk(2	)
															UpperCAmelCase__		:List[Any] 		=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
															UpperCAmelCase__		:Optional[Any] 		=    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__		:Dict 		=    noise_pred.split(latents.shape[1]  , dim=1	)

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

												UpperCAmelCase__		:List[Any] 		=    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__		:Dict 		=    image * 0.5 + 0.5
												UpperCAmelCase__		:Optional[int] 		=    image.clamp(0  , 1	)
												UpperCAmelCase__		:Union[str, Any] 		=    image.cpu().permute(0  , 2  , 3  , 1	).float().numpy()

									if output_type == "pil":
												UpperCAmelCase__		:Optional[int] 		=    self.numpy_to_pil(_A	)

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=_A	)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase   						=     {
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     [
       "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DonutSwinModel",
       "DonutSwinPreTrainedModel",
   ]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase   						=     ["DonutFeatureExtractor"]
   __UpperCAmelCase   						=     ["DonutImageProcessor"]


if TYPE_CHECKING:
   from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
   from .processing_donut import DonutProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_donut_swin import (
          DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          DonutSwinModel,
          DonutSwinPreTrainedModel,
      )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_donut import DonutFeatureExtractor
      from .image_processing_donut import DonutImageProcessor

else:
   import sys

   __UpperCAmelCase   						=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


