code: 
  '''simple docstring'''
  
  
  
  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							(       __UpperCAmelCase				):
  			lowerCamelCase							:							Union[str, Any]       		=  ["""safety_checker""", """feature_extractor"""]
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__()
  									if hasattr(scheduler.config       ,     """steps_offset"""       ) and scheduler.config.steps_offset != 1:
  															lowercase__							 =       (
  															    F'''The configuration file of this scheduler: {scheduler} is outdated.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"""       ,     lowerCamelCase__       ,     standard_warn=lowerCamelCase__       )
  															lowercase__							 =       dict(scheduler.config       )
  															lowercase__							 =       1
  															lowercase__							 =       FrozenDict(lowerCamelCase__       )
  
  									self.register_modules(
  									    vae=lowerCamelCase__       ,     text_encoder=lowerCamelCase__       ,     tokenizer=lowerCamelCase__       ,     unet=lowerCamelCase__       ,     scheduler=lowerCamelCase__       ,     safety_checker=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       ,     )
  									self.register_to_config(requires_safety_checker=lowerCamelCase__       )
  
  
  
  			@property
  			def A__ (					self       )			->		Dict[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return {k: getattr(self       ,     lowerCamelCase__       ) for k in self.config.keys() if not k.startswith("""_"""       )}
  
  
  
  			def A__ (					self       ,     lowerCamelCase__ = "auto"       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if slice_size == "auto":
  															# half the attention head size is usually a good trade-off between
  															# speed and memory
  															lowercase__							 =       self.unet.config.attention_head_dim // 2
  									self.unet.set_attention_slice(lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.enable_attention_slicing(lowerCamelCase__       )
  
  
  
  			@torch.no_grad()
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = 0.8       ,     lowerCamelCase__ = 50       ,     lowerCamelCase__ = 7.5       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = 1       ,     lowerCamelCase__ = 0.0       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "pil"       ,     lowerCamelCase__ = True       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = 1       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return StableDiffusionInpaintPipelineLegacy(**self.components       )(
  									    prompt=lowerCamelCase__       ,     image=lowerCamelCase__       ,     mask_image=lowerCamelCase__       ,     strength=lowerCamelCase__       ,     num_inference_steps=lowerCamelCase__       ,     guidance_scale=lowerCamelCase__       ,     negative_prompt=lowerCamelCase__       ,     num_images_per_prompt=lowerCamelCase__       ,     eta=lowerCamelCase__       ,     generator=lowerCamelCase__       ,     output_type=lowerCamelCase__       ,     return_dict=lowerCamelCase__       ,     callback=lowerCamelCase__       ,     )
  
  
  
  			@torch.no_grad()
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = 0.8       ,     lowerCamelCase__ = 50       ,     lowerCamelCase__ = 7.5       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = 1       ,     lowerCamelCase__ = 0.0       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "pil"       ,     lowerCamelCase__ = True       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = 1       ,     **lowerCamelCase__       ,     )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return StableDiffusionImgaImgPipeline(**self.components       )(
  									    prompt=lowerCamelCase__       ,     image=lowerCamelCase__       ,     strength=lowerCamelCase__       ,     num_inference_steps=lowerCamelCase__       ,     guidance_scale=lowerCamelCase__       ,     negative_prompt=lowerCamelCase__       ,     num_images_per_prompt=lowerCamelCase__       ,     eta=lowerCamelCase__       ,     generator=lowerCamelCase__       ,     output_type=lowerCamelCase__       ,     return_dict=lowerCamelCase__       ,     callback=lowerCamelCase__       ,     callback_steps=lowerCamelCase__       ,     )
  
  
  
  			@torch.no_grad()
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = 512       ,     lowerCamelCase__ = 512       ,     lowerCamelCase__ = 50       ,     lowerCamelCase__ = 7.5       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = 1       ,     lowerCamelCase__ = 0.0       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = "pil"       ,     lowerCamelCase__ = True       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = 1       ,     )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return StableDiffusionPipeline(**self.components       )(
  									    prompt=lowerCamelCase__       ,     height=lowerCamelCase__       ,     width=lowerCamelCase__       ,     num_inference_steps=lowerCamelCase__       ,     guidance_scale=lowerCamelCase__       ,     negative_prompt=lowerCamelCase__       ,     num_images_per_prompt=lowerCamelCase__       ,     eta=lowerCamelCase__       ,     generator=lowerCamelCase__       ,     latents=lowerCamelCase__       ,     output_type=lowerCamelCase__       ,     return_dict=lowerCamelCase__       ,     callback=lowerCamelCase__       ,     callback_steps=lowerCamelCase__       ,     )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       copy.deepcopy(lowercase__	)
  						lowercase__							 =       sorted(lowercase__	)
  						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
  						return quantized
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
  						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
  						# count number of elements per group
  						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
  						lowercase__							 =       [0] + bins + [np.inf]
  						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
  						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
  						return groups
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															raise ValueError(
  															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
  									lowercase__							 =       sampler
  									lowercase__							 =       group_ids
  									lowercase__							 =       batch_size
  
  
  
  			def __iter__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  
  									lowercase__							 =       0
  									for idx in self.sampler:
  															lowercase__							 =       self.group_ids[idx]
  															buffer_per_group[group_id].append(lowerCamelCase__       )
  															samples_per_group[group_id].append(lowerCamelCase__       )
  															if len(buffer_per_group[group_id]       ) == self.batch_size:
  																					yield buffer_per_group[group_id]  # TODO
  																					num_batches += 1
  																					del buffer_per_group[group_id]
  															assert len(buffer_per_group[group_id]       ) < self.batch_size
  
  									# now we have run out of elements that satisfy
  									# the group criteria, let's return the remaining
  									# elements so that the size of the sampler is
  									# deterministic
  									lowercase__							 =       len(self       )
  									lowercase__							 =       expected_num_batches - num_batches
  									if num_remaining > 0:
  															# for the remaining batches, group the batches by similar lengths
  															lowercase__							 =       []
  															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
  																					batch_idx.extend(lowerCamelCase__       )
  																					if len(lowerCamelCase__       ) >= self.batch_size:
  																											yield batch_idx[: self.batch_size]
  																											lowercase__							 =       batch_idx[self.batch_size :]
  																											num_remaining -= 1
  															if len(lowerCamelCase__       ) > 0:
  																					yield batch_idx
  																					num_remaining -= 1
  									assert num_remaining == 0
  
  
  
  			def __len__(					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
  