
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


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





class   __UpperCamelCase				(    SCREAMING_SNAKE_CASE__   ):






     """simple docstring"""







     lowerCAmelCase_     =	['''safety_checker''', '''feature_extractor''']



     def __init__(							self    :    Tuple      ,		_A    :    List[Any]      ,		_A    :    Union[str, Any]      ,		_A    :    Tuple      ,		_A    :    Any      ,		_A    :    List[str]      ,		_A    :    str      ,		_A    :    List[Any]      ,		_A    :    Optional[int] = True      ,		):







       """simple docstring"""




       super().__init__()
       if hasattr(scheduler.config      ,		'''steps_offset'''				) and scheduler.config.steps_offset != 1:
         __SCREAMING_SNAKE_CASE     :					List[str]			=						(
             F'''The configuration file of this scheduler: {scheduler} is outdated.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				)
         __SCREAMING_SNAKE_CASE     :					str			=						dict(scheduler.config				)
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						1
         __SCREAMING_SNAKE_CASE     :					Dict			=						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    :    List[Any]				):







       """simple docstring"""




       return {k: getattr(self      ,		_UpperCAmelCase				) for k in self.config.keys() if not k.startswith('''_'''				)}



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    Tuple = "auto"				):







       """simple docstring"""




       if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(_UpperCAmelCase				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       self.enable_attention_slicing(_UpperCAmelCase				)



     @torch.no_grad()
     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    int      ,		_A    :    str      ,		_A    :    int      ,		_A    :    str = 0.8      ,		_A    :    List[str] = 50      ,		_A    :    Dict = 7.5      ,		_A    :    Dict = None      ,		_A    :    Any = 1      ,		_A    :    int = 0.0      ,		_A    :    List[str] = None      ,		_A    :    Optional[int] = "pil"      ,		_A    :    List[Any] = True      ,		_A    :    Dict = None      ,		_A    :    Optional[int] = 1      ,		):







       """simple docstring"""




       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    :    Tuple      ,		_A    :    Any      ,		_A    :    Dict      ,		_A    :    Optional[int] = 0.8      ,		_A    :    Optional[int] = 50      ,		_A    :    Tuple = 7.5      ,		_A    :    Union[str, Any] = None      ,		_A    :    Tuple = 1      ,		_A    :    List[Any] = 0.0      ,		_A    :    Optional[int] = None      ,		_A    :    Any = "pil"      ,		_A    :    str = True      ,		_A    :    int = None      ,		_A    :    str = 1      ,		**_A    :    List[Any]      ,		):







       """simple docstring"""




       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    :    Dict      ,		_A    :    str      ,		_A    :    Dict = 512      ,		_A    :    int = 512      ,		_A    :    str = 50      ,		_A    :    Tuple = 7.5      ,		_A    :    int = None      ,		_A    :    Union[str, Any] = 1      ,		_A    :    Union[str, Any] = 0.0      ,		_A    :    Dict = None      ,		_A    :    Dict = None      ,		_A    :    List[str] = "pil"      ,		_A    :    List[Any] = True      ,		_A    :    Optional[int] = None      ,		_A    :    List[str] = 1      ,		):







       """simple docstring"""




       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 collections import defaultdict


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					defaultdict[str, int]			=						defaultdict(snake_case    )
  # Creating a dictionary containing count of each word
  for word in sentence.split():
    occurrence[word] += 1
  return occurrence


if __name__ == "__main__":
   for word, count in word_occurrence("""INPUT STRING""").items():
      print(f'''{word}: {count}''')
