




"""simple docstring"""

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

a         =      TypeVar('''KEY''')
a         =      TypeVar('''VAL''')





@dataclass(frozen=__lowerCAmelCase      ,     slots=__lowerCAmelCase							)
class     lowercase_	(					Generic[KEY, VAL]							):



 '''simple docstring'''
 UpperCAmelCase   :					KEY
 UpperCAmelCase   :					VAL





class     lowercase_	(					_Item							):



 '''simple docstring'''

 def __init__(			self	:	Optional[int]		):
       super().__init__(_UpperCAmelCase   ,							_UpperCAmelCase		)



 def __bool__(			self	:	Optional[Any]		):
       return False



a         =      _DeletedItem()





class     lowercase_	(					MutableMapping[KEY, VAL]							):



 '''simple docstring'''

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	int = 8   ,							_UpperCAmelCase	:	float = 0.75		):
       _A        =       initial_block_size
       _A        =       [None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       _A        =       capacity_factor
       _A        =       0

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	KEY		):
       return hash(_UpperCAmelCase		) % len(self._buckets		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	int		):
       return (ind + 1) % len(self._buckets		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	KEY   ,							_UpperCAmelCase	:	VAL		):
       _A        =       self._buckets[ind]
       if not stored:
             _A        =       _Item(_UpperCAmelCase   ,							_UpperCAmelCase		)
             self._len += 1
             return True
       elif stored.key == key:
             _A        =       _Item(_UpperCAmelCase   ,							_UpperCAmelCase		)
             return True
       else:
             return False

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       len(self._buckets		) * self._capacity_factor
       return len(self		) >= int(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       if len(self._buckets		) <= self._initial_block_size:
             return False
       _A        =       len(self._buckets		) * self._capacity_factor / 2
       return len(self		) < limit

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	int		):
       _A        =       self._buckets
       _A        =       [None] * new_size
       _A        =       0
       for item in old_buckets:
             if item:
                   self._add_item(item.key   ,							item.val		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       self._resize(len(self._buckets		) * 2		)

 def       lowerCAmelCase_							(			self	:	Any		):
       self._resize(len(self._buckets		) // 2		)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	KEY		):
       _A        =       self._get_bucket_index(_UpperCAmelCase		)
       for _ in range(len(self._buckets		)		):
             yield ind
             _A        =       self._get_next_ind(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	KEY   ,							_UpperCAmelCase	:	VAL		):
       for ind in self._iterate_buckets(_UpperCAmelCase		):
             if self._try_set(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		):
                   break

 def __setitem__(			self	:	Dict   ,							_UpperCAmelCase	:	KEY   ,							_UpperCAmelCase	:	VAL		):
       if self._is_full():
             self._size_up()

       self._add_item(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def __delitem__(			self	:	int   ,							_UpperCAmelCase	:	KEY		):
       for ind in self._iterate_buckets(_UpperCAmelCase		):
             _A        =       self._buckets[ind]
             if item is None:
                   raise KeyError(_UpperCAmelCase		)
             if item is _deleted:
                   continue
             if item.key == key:
                   _A        =       _deleted
                   self._len -= 1
                   break
       if self._is_sparse():
             self._size_down()

 def __getitem__(			self	:	Dict   ,							_UpperCAmelCase	:	KEY		):
       for ind in self._iterate_buckets(_UpperCAmelCase		):
             _A        =       self._buckets[ind]
             if item is None:
                   break
             if item is _deleted:
                   continue
             if item.key == key:
                   return item.val
       raise KeyError(_UpperCAmelCase		)

 def __len__(			self	:	List[str]		):
       return self._len

 def __iter__(			self	:	Dict		):
       yield from (item.key for item in self._buckets if item)



 def __repr__(			self	:	Optional[int]		):
       _A        =       ' ,'.join(
           F'''{item.key}: {item.val}''' for item in self._buckets if item		)
       return F'''HashMap({val_string})'''







"""simple docstring"""

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,
)


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

a         =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: List[Any]    ,  _snake_case							: List[Any]=8							)   -> Optional[int]:






      '''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







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Dict   ,							_UpperCAmelCase	:	UNetaDConditionModel   ,							_UpperCAmelCase	:	DDPMScheduler   ,							_UpperCAmelCase	:	VQModel   ,							):
       super().__init__()

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

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if latents is None:
             _A        =       randn_tensor(_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		)
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
             _A        =       latents.to(_UpperCAmelCase		)

       _A        =       latents * scheduler.init_noise_sigma
       return latents

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Tuple=0		):
       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(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[str, Any]=0		):
       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=_UpperCAmelCase		)
             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(_UpperCAmelCase   ,							_UpperCAmelCase   ,							prev_module_hook=_UpperCAmelCase		)

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

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       lowerCAmelCase_							(			self	:	int		):
       if not hasattr(self.unet   ,							'_hf_hook'		):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase   ,							'_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(_UpperCAmelCase		)
 def __call__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 100   ,							_UpperCAmelCase	:	float = 4.0   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	Optional[torch.FloatTensor] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							):
       _A        =       self._execution_device

       _A        =       guidance_scale > 1.0

       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
             _A        =       torch.cat(_UpperCAmelCase   ,							dim=0		)
       _A        =       image_embeds.shape[0] * num_images_per_prompt
       if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
             _A        =       torch.cat(_UpperCAmelCase   ,							dim=0		)

       if do_classifier_free_guidance:
             _A        =       image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)
             _A        =       negative_image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)

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

       self.scheduler.set_timesteps(_UpperCAmelCase   ,							device=_UpperCAmelCase		)
       _A        =       self.scheduler.timesteps

       _A        =       self.unet.config.in_channels

       _A       ,   _A        =       downscale_height_and_width(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.movq_scale_factor		)

       # create initial latent
       _A        =       self.prepare_latents(
           (batch_size, num_channels_latents, height, width)   ,							image_embeds.dtype   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.scheduler   ,							)

       for i, t in enumerate(self.progress_bar(_UpperCAmelCase		)		):
             # 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=_UpperCAmelCase   ,							timestep=_UpperCAmelCase   ,							encoder_hidden_states=_UpperCAmelCase   ,							added_cond_kwargs=_UpperCAmelCase   ,							return_dict=_UpperCAmelCase   ,							)[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(
                 _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							)[0]
       # post-processing
       _A        =       self.movq.decode(_UpperCAmelCase   ,							force_not_quantize=_UpperCAmelCase		)['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(_UpperCAmelCase		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase		)

