

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


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






class 							A_			(		snake_case__ ):
     _lowercase      :					Optional[Any]       =     ['safety_checker', 'feature_extractor']


     def __init__(							self      :		Optional[Any]      ,  UpperCAmelCase      :		AutoencoderKL      ,  UpperCAmelCase      :		CLIPTextModel      ,  UpperCAmelCase      :		CLIPTokenizer      ,  UpperCAmelCase      :		UNetaDConditionModel      ,  UpperCAmelCase      :		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,  UpperCAmelCase      :		StableDiffusionSafetyChecker      ,  UpperCAmelCase      :		CLIPImageProcessor      ,  UpperCAmelCase      :		bool = True      ,  )							->     int:
      super().__init__()
      if hasattr(scheduler.config      ,  'steps_offset' ) and scheduler.config.steps_offset != 1:
       __lowerCAmelCase:       Dict            =				(
           F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
           F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
           'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
           ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
           ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
           ' file'
       )
       deprecate('steps_offset!=1'      ,  '1.0.0'      ,  UpperCAmelCase      ,  standard_warn=UpperCAmelCase )
       __lowerCAmelCase:       List[Any]            =				dict(scheduler.config )
       __lowerCAmelCase:       Dict            =				1
       __lowerCAmelCase:       Union[str, Any]            =				FrozenDict(UpperCAmelCase )

      self.register_modules(
          vae=UpperCAmelCase      ,  text_encoder=UpperCAmelCase      ,  tokenizer=UpperCAmelCase      ,  unet=UpperCAmelCase      ,  scheduler=UpperCAmelCase      ,  safety_checker=UpperCAmelCase      ,  feature_extractor=UpperCAmelCase      ,  )
      self.register_to_config(requires_safety_checker=UpperCAmelCase )


     @property
     def   UpperCAmelCase  (							self      :		int )							->     Dict[str, Any]:
      return {k: getattr(self      ,  UpperCAmelCase ) for k in self.config.keys() if not k.startswith('_' )}


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Optional[Union[str, int]] = "auto" )							->     List[str]:
      if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       __lowerCAmelCase:       Optional[Any]            =				self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Tuple )							->     List[str]:
      self.enable_attention_slicing(UpperCAmelCase )


     @torch.no_grad()
     def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, List[str]]      ,  UpperCAmelCase      :		Union[torch.FloatTensor, PIL.Image.Image]      ,  UpperCAmelCase      :		Union[torch.FloatTensor, PIL.Image.Image]      ,  UpperCAmelCase      :		float = 0.8      ,  UpperCAmelCase      :		Optional[int] = 5_0      ,  UpperCAmelCase      :		Optional[float] = 7.5      ,  UpperCAmelCase      :		Optional[Union[str, List[str]]] = None      ,  UpperCAmelCase      :		Optional[int] = 1      ,  UpperCAmelCase      :		Optional[float] = 0.0      ,  UpperCAmelCase      :		Optional[torch.Generator] = None      ,  UpperCAmelCase      :		Optional[str] = "pil"      ,  UpperCAmelCase      :		bool = True      ,  UpperCAmelCase      :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,  UpperCAmelCase      :		int = 1      ,  )							->     str:
      # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
      return StableDiffusionInpaintPipelineLegacy(**self.components )(
          prompt=UpperCAmelCase      ,  image=UpperCAmelCase      ,  mask_image=UpperCAmelCase      ,  strength=UpperCAmelCase      ,  num_inference_steps=UpperCAmelCase      ,  guidance_scale=UpperCAmelCase      ,  negative_prompt=UpperCAmelCase      ,  num_images_per_prompt=UpperCAmelCase      ,  eta=UpperCAmelCase      ,  generator=UpperCAmelCase      ,  output_type=UpperCAmelCase      ,  return_dict=UpperCAmelCase      ,  callback=UpperCAmelCase      ,  )


     @torch.no_grad()
     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Union[str, List[str]]      ,  UpperCAmelCase      :		Union[torch.FloatTensor, PIL.Image.Image]      ,  UpperCAmelCase      :		float = 0.8      ,  UpperCAmelCase      :		Optional[int] = 5_0      ,  UpperCAmelCase      :		Optional[float] = 7.5      ,  UpperCAmelCase      :		Optional[Union[str, List[str]]] = None      ,  UpperCAmelCase      :		Optional[int] = 1      ,  UpperCAmelCase      :		Optional[float] = 0.0      ,  UpperCAmelCase      :		Optional[torch.Generator] = None      ,  UpperCAmelCase      :		Optional[str] = "pil"      ,  UpperCAmelCase      :		bool = True      ,  UpperCAmelCase      :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,  UpperCAmelCase      :		int = 1      ,  **UpperCAmelCase      :		List[str]      ,  )							->     List[str]:
      # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
      return StableDiffusionImgaImgPipeline(**self.components )(
          prompt=UpperCAmelCase      ,  image=UpperCAmelCase      ,  strength=UpperCAmelCase      ,  num_inference_steps=UpperCAmelCase      ,  guidance_scale=UpperCAmelCase      ,  negative_prompt=UpperCAmelCase      ,  num_images_per_prompt=UpperCAmelCase      ,  eta=UpperCAmelCase      ,  generator=UpperCAmelCase      ,  output_type=UpperCAmelCase      ,  return_dict=UpperCAmelCase      ,  callback=UpperCAmelCase      ,  callback_steps=UpperCAmelCase      ,  )


     @torch.no_grad()
     def   UpperCAmelCase  (							self      :		List[str]      ,  UpperCAmelCase      :		Union[str, List[str]]      ,  UpperCAmelCase      :		int = 5_1_2      ,  UpperCAmelCase      :		int = 5_1_2      ,  UpperCAmelCase      :		int = 5_0      ,  UpperCAmelCase      :		float = 7.5      ,  UpperCAmelCase      :		Optional[Union[str, List[str]]] = None      ,  UpperCAmelCase      :		Optional[int] = 1      ,  UpperCAmelCase      :		float = 0.0      ,  UpperCAmelCase      :		Optional[torch.Generator] = None      ,  UpperCAmelCase      :		Optional[torch.FloatTensor] = None      ,  UpperCAmelCase      :		Optional[str] = "pil"      ,  UpperCAmelCase      :		bool = True      ,  UpperCAmelCase      :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,  UpperCAmelCase      :		int = 1      ,  )							->     int:
      # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
      return StableDiffusionPipeline(**self.components )(
          prompt=UpperCAmelCase      ,  height=UpperCAmelCase      ,  width=UpperCAmelCase      ,  num_inference_steps=UpperCAmelCase      ,  guidance_scale=UpperCAmelCase      ,  negative_prompt=UpperCAmelCase      ,  num_images_per_prompt=UpperCAmelCase      ,  eta=UpperCAmelCase      ,  generator=UpperCAmelCase      ,  latents=UpperCAmelCase      ,  output_type=UpperCAmelCase      ,  return_dict=UpperCAmelCase      ,  callback=UpperCAmelCase      ,  callback_steps=UpperCAmelCase      ,  )








from __future__ import annotations







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   list[int]:

 """simple docstring"""






 if not list_of_ints:
  return []

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Tuple            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int		)   ->   list[int]:

 """simple docstring"""






 if bit_position == 0 or len(SCREAMING_SNAKE_CASE		) in [0, 1]:
  return list_of_ints

 __lowerCAmelCase:       List[str]            =				[]
 __lowerCAmelCase:       Any            =				[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(SCREAMING_SNAKE_CASE		)
  else:
   # number has a zero at bit bit_position
   zeros.append(SCREAMING_SNAKE_CASE		)

    # recursively split both lists further
 __lowerCAmelCase:       Dict            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)
 __lowerCAmelCase:       Union[str, Any]            =				_msd_radix_sort(SCREAMING_SNAKE_CASE   , bit_position - 1		)

 # recombine lists
 __lowerCAmelCase:       List[str]            =				zeros
 res.extend(SCREAMING_SNAKE_CASE		)

 return res







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]		)   ->   List[str]:

 """simple docstring"""






 __lowerCAmelCase:       Optional[int]            =				len(SCREAMING_SNAKE_CASE		)
 if not list_of_ints or length == 1:
  return

 if min(SCREAMING_SNAKE_CASE		) < 0:
  raise ValueError('All numbers must be positive'		)

 __lowerCAmelCase:       Optional[int]            =				max(len(bin(SCREAMING_SNAKE_CASE		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , 0   , SCREAMING_SNAKE_CASE		)







def _a			(	SCREAMING_SNAKE_CASE		:      list[int]   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int   , SCREAMING_SNAKE_CASE		:      int		)   ->   str:

 """simple docstring"""






 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 __lowerCAmelCase:       Optional[int]            =				begin_index
 __lowerCAmelCase:       Optional[Any]            =				end_index - 1
 while i <= j:
  __lowerCAmelCase:       Any            =				False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   __lowerCAmelCase:       Optional[Any]            =				True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   __lowerCAmelCase:       List[str]            =				True

  if changed:
   continue

  __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 _msd_radix_sort_inplace(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





