

'''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__         =      "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=8     ):
 UpperCAmelCase	:     Tuple						 =			height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCAmelCase	:     Union[str, Any]						 =			width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_=5_12  ,	UpperCAmelCase_=5_12     ):
 UpperCAmelCase	:     List[Any]						 =			pil_image.resize((w, h)  ,	resample=Image.BICUBIC  ,	reducing_gap=1     )
 UpperCAmelCase	:     Tuple						 =			np.array(pil_image.convert('RGB'     )     )
 UpperCAmelCase	:     List[Any]						 =			arr.astype(np.floataa     ) / 127.5 - 1
 UpperCAmelCase	:     List[str]						 =			np.transpose(UpperCAmelCase_  ,	[2, 0, 1]     )
 UpperCAmelCase	:     Tuple						 =			torch.from_numpy(UpperCAmelCase_     ).unsqueeze(0     )
 return image




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[str]					,      lowercase_			:					UNetaDConditionModel					,      lowercase_			:					DDPMScheduler					,      lowercase_			:					VQModel					,      )						->     str:
      super().__init__()

      self.register_modules(
          unet=lowercase_					,      scheduler=lowercase_					,      movq=lowercase_					,      )
      UpperCAmelCase	:     Dict						 =			2 ** (len(self.movq.config.block_out_channels	) - 1)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					Tuple					,      lowercase_			:					str	)						->     Dict:
      # get the original timestep using init_timestep
      UpperCAmelCase	:     Tuple						 =			min(int(num_inference_steps * strength	)					,      lowercase_	)

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

      return timesteps, num_inference_steps - t_start


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					List[Any]					,      lowercase_			:					Dict					,      lowercase_			:					Any					,      lowercase_			:					Optional[int]					,      lowercase_			:					Dict					,      lowercase_			:					Dict					,      lowercase_			:					Any=None	)						->     Optional[Any]:
      if not isinstance(lowercase_					,      (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(lowercase_	)}"""	)

      UpperCAmelCase	:     Any						 =			image.to(device=lowercase_					,      dtype=lowercase_	)

      UpperCAmelCase	:     List[Any]						 =			batch_size * num_images_per_prompt

      if image.shape[1] == 4:
       UpperCAmelCase	:     List[Any]						 =			image

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

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

       UpperCAmelCase	:     Optional[Any]						 =			self.movq.config.scaling_factor * init_latents

      UpperCAmelCase	:     int						 =			torch.cat([init_latents]					,      dim=0	)

      UpperCAmelCase	:     Union[str, Any]						 =			init_latents.shape
      UpperCAmelCase	:     Union[str, Any]						 =			randn_tensor(lowercase_					,      generator=lowercase_					,      device=lowercase_					,      dtype=lowercase_	)

      # get latents
      UpperCAmelCase	:     Union[str, Any]						 =			self.scheduler.add_noise(lowercase_					,      lowercase_					,      lowercase_	)

      UpperCAmelCase	:     Optional[Any]						 =			init_latents

      return latents


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]=0	)						->     Tuple:
      if is_accelerate_available():
       from accelerate import cpu_offload
      else:
       raise ImportError('Please install accelerate via `pip install accelerate`'	)

      UpperCAmelCase	:     int						 =			torch.device(f"""cuda:{gpu_id}"""	)

      UpperCAmelCase	:     Union[str, Any]						 =			[
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
        cpu_offload(lowercase_					,      lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					int=0	)						->     str:
      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	:     int						 =			torch.device(f"""cuda:{gpu_id}"""	)

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

      UpperCAmelCase	:     Any						 =			None
      for cpu_offloaded_model in [self.unet, self.movq]:
       UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			cpu_offload_with_hook(lowercase_					,      lowercase_					,      prev_module_hook=lowercase_	)

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

      UpperCAmelCase	:     int						 =			guidance_scale > 1.0

      if isinstance(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     int						 =			torch.cat(lowercase_					,      dim=0	)
      UpperCAmelCase	:     List[str]						 =			image_embeds.shape[0]
      if isinstance(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     str						 =			torch.cat(lowercase_					,      dim=0	)

      if do_classifier_free_guidance:
       UpperCAmelCase	:     Tuple						 =			image_embeds.repeat_interleave(lowercase_					,      dim=0	)
       UpperCAmelCase	:     Dict						 =			negative_image_embeds.repeat_interleave(lowercase_					,      dim=0	)

      UpperCAmelCase	:     Tuple						 =			torch.cat([negative_image_embeds, image_embeds]					,      dim=0	).to(dtype=self.unet.dtype					,      device=lowercase_	)

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

      UpperCAmelCase	:     Dict						 =			torch.cat([prepare_image(lowercase_					,      lowercase_					,      lowercase_	) for i in image]					,      dim=0	)
      UpperCAmelCase	:     Optional[int]						 =			image.to(dtype=image_embeds.dtype					,      device=lowercase_	)

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

       UpperCAmelCase	:     int						 =			{'image_embeds': image_embeds}
       UpperCAmelCase	:     Optional[int]						 =			self.unet(
           sample=lowercase_					,      timestep=lowercase_					,      encoder_hidden_states=lowercase_					,      added_cond_kwargs=lowercase_					,      return_dict=lowercase_					,      )[0]

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

       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Optional[Any]						 =			self.scheduler.step(
           lowercase_					,      lowercase_					,      lowercase_					,      generator=lowercase_					,      )[0]

      # post-processing
      UpperCAmelCase	:     List[Any]						 =			self.movq.decode(lowercase_					,      force_not_quantize=lowercase_	)['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	:     int						 =			image * 0.5 + 0.5
       UpperCAmelCase	:     List[Any]						 =			image.clamp(0					,      1	)
       UpperCAmelCase	:     Optional[int]						 =			image.cpu().permute(0					,      2					,      3					,      1	).float().numpy()

      if output_type == "pil":
       UpperCAmelCase	:     str						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)







'''simple docstring'''

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowercase__         =      TypeVar("KEY")
lowercase__         =      TypeVar("VAL")




@dataclass(frozen=_snake_case	,       slots=_snake_case							)
class 	A_			(    Generic[KEY, VAL]							):






     '''simple docstring'''



     UpperCAmelCase_						:     KEY
     UpperCAmelCase_						:     VAL




class 	A_			(    _Item							):






     '''simple docstring'''



     def __init__(		self			:					Any	)						->     None:
      super().__init__(lowercase_					,      lowercase_	)




     def __bool__(		self			:					List[str]	)						->     bool:
      return False



lowercase__         =      _DeletedItem()




class 	A_			(    MutableMapping[KEY, VAL]							):






     '''simple docstring'''



     def __init__(		self			:					Optional[int]					,      lowercase_			:					int = 8					,      lowercase_			:					float = 0.75	)						->     None:
      UpperCAmelCase	:     Dict						 =			initial_block_size
      UpperCAmelCase	:     list[_Item | None]						 =			[None] * initial_block_size
      assert 0.0 < capacity_factor < 1.0
      UpperCAmelCase	:     Any						 =			capacity_factor
      UpperCAmelCase	:     Union[str, Any]						 =			0


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					KEY	)						->     int:
      return hash(lowercase_	) % len(self._buckets	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int	)						->     int:
      return (ind + 1) % len(self._buckets	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     bool:
      UpperCAmelCase	:     List[Any]						 =			self._buckets[ind]
      if not stored:
       UpperCAmelCase	:     Dict						 =			_Item(lowercase_					,      lowercase_	)
       self._len += 1
       return True
      elif stored.key == key:
       UpperCAmelCase	:     Dict						 =			_Item(lowercase_					,      lowercase_	)
       return True
      else:
       return False


     def        UpperCAmelCase_	(		self			:					Any	)						->     bool:
      UpperCAmelCase	:     List[str]						 =			len(self._buckets	) * self._capacity_factor
      return len(self	) >= int(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     bool:
      if len(self._buckets	) <= self._initial_block_size:
       return False
      UpperCAmelCase	:     Optional[int]						 =			len(self._buckets	) * self._capacity_factor / 2
      return len(self	) < limit


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int	)						->     None:
      UpperCAmelCase	:     int						 =			self._buckets
      UpperCAmelCase	:     List[str]						 =			[None] * new_size
      UpperCAmelCase	:     Dict						 =			0
      for item in old_buckets:
       if item:
        self._add_item(item.key					,      item.val	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     None:
      self._resize(len(self._buckets	) * 2	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     None:
      self._resize(len(self._buckets	) // 2	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					KEY	)						->     Iterator[int]:
      UpperCAmelCase	:     Dict						 =			self._get_bucket_index(lowercase_	)
      for _ in range(len(self._buckets	)	):
       yield ind
       UpperCAmelCase	:     Union[str, Any]						 =			self._get_next_ind(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     None:
      for ind in self._iterate_buckets(lowercase_	):
       if self._try_set(lowercase_					,      lowercase_					,      lowercase_	):
        break


     def __setitem__(		self			:					Union[str, Any]					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     None:
      if self._is_full():
       self._size_up()

      self._add_item(lowercase_					,      lowercase_	)


     def __delitem__(		self			:					Tuple					,      lowercase_			:					KEY	)						->     None:
      for ind in self._iterate_buckets(lowercase_	):
       UpperCAmelCase	:     int						 =			self._buckets[ind]
       if item is None:
        raise KeyError(lowercase_	)
       if item is _deleted:
        continue
       if item.key == key:
        UpperCAmelCase	:     Union[str, Any]						 =			_deleted
        self._len -= 1
        break
      if self._is_sparse():
       self._size_down()


     def __getitem__(		self			:					Dict					,      lowercase_			:					KEY	)						->     VAL:
      for ind in self._iterate_buckets(lowercase_	):
       UpperCAmelCase	:     List[str]						 =			self._buckets[ind]
       if item is None:
        break
       if item is _deleted:
        continue
       if item.key == key:
        return item.val
      raise KeyError(lowercase_	)


     def __len__(		self			:					Optional[Any]	)						->     int:
      return self._len


     def __iter__(		self			:					int	)						->     Iterator[KEY]:
      yield from (item.key for item in self._buckets if item)




     def __repr__(		self			:					Optional[Any]	)						->     str:
      UpperCAmelCase	:     int						 =			' ,'.join(
          f"""{item.key}: {item.val}""" for item in self._buckets if item	)
      return f"""HashMap({val_string})"""




