

'''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


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






class   _SCREAMING_SNAKE_CASE    (    lowerCAmelCase_	):
 __SCREAMING_SNAKE_CASE     :List[Any]			       =     ["""safety_checker""", """feature_extractor"""]



 def __init__(    self		:    Union[str, Any] ,      a__		:    Tuple ,      a__		:    Any ,      a__		:    Optional[int] ,      a__		:    Tuple ,      a__		:    List[str] ,      a__		:    int ,      a__		:    Any ,      a__		:    List[Any] = True ,      ):
      super().__init__()
      if hasattr(scheduler.config ,      '''steps_offset'''			) and scheduler.config.steps_offset != 1:
           __magic_name__						   =				(
               F'''The configuration file of this scheduler: {scheduler} is outdated.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''' ,      __SCREAMING_SNAKE_CASE ,      standard_warn=__SCREAMING_SNAKE_CASE			)
           __magic_name__						   =				dict(scheduler.config			)
           __magic_name__						   =				1
           __magic_name__						   =				FrozenDict(__SCREAMING_SNAKE_CASE			)

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



 @property
 def  snake_case__  (    self		:    Optional[Any]			):
      return {k: getattr(self ,      __SCREAMING_SNAKE_CASE			) for k in self.config.keys() if not k.startswith('''_'''			)}



 def  snake_case__  (    self		:    int ,      a__		:    Any = "auto"			):

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



 def  snake_case__  (    self		:    Union[str, Any]			):
      self.enable_attention_slicing(__SCREAMING_SNAKE_CASE			)



 @torch.no_grad()
 def  snake_case__  (    self		:    Dict ,      a__		:    Union[str, Any] ,      a__		:    int ,      a__		:    Dict ,      a__		:    Any = 0.8 ,      a__		:    List[Any] = 50 ,      a__		:    List[str] = 7.5 ,      a__		:    Any = None ,      a__		:    Optional[int] = 1 ,      a__		:    Union[str, Any] = 0.0 ,      a__		:    str = None ,      a__		:    Tuple = "pil" ,      a__		:    List[Any] = True ,      a__		:    Dict = None ,      a__		:    Optional[int] = 1 ,      ):
      return StableDiffusionInpaintPipelineLegacy(**self.components			)(
          prompt=__SCREAMING_SNAKE_CASE ,      image=__SCREAMING_SNAKE_CASE ,      mask_image=__SCREAMING_SNAKE_CASE ,      strength=__SCREAMING_SNAKE_CASE ,      num_inference_steps=__SCREAMING_SNAKE_CASE ,      guidance_scale=__SCREAMING_SNAKE_CASE ,      negative_prompt=__SCREAMING_SNAKE_CASE ,      num_images_per_prompt=__SCREAMING_SNAKE_CASE ,      eta=__SCREAMING_SNAKE_CASE ,      generator=__SCREAMING_SNAKE_CASE ,      output_type=__SCREAMING_SNAKE_CASE ,      return_dict=__SCREAMING_SNAKE_CASE ,      callback=__SCREAMING_SNAKE_CASE ,      )



 @torch.no_grad()
 def  snake_case__  (    self		:    List[str] ,      a__		:    List[str] ,      a__		:    Dict ,      a__		:    int = 0.8 ,      a__		:    Tuple = 50 ,      a__		:    Dict = 7.5 ,      a__		:    List[str] = None ,      a__		:    Any = 1 ,      a__		:    Tuple = 0.0 ,      a__		:    Union[str, Any] = None ,      a__		:    Optional[int] = "pil" ,      a__		:    Tuple = True ,      a__		:    Union[str, Any] = None ,      a__		:    Union[str, Any] = 1 ,      **a__		:    Tuple ,      ):
      return StableDiffusionImgaImgPipeline(**self.components			)(
          prompt=__SCREAMING_SNAKE_CASE ,      image=__SCREAMING_SNAKE_CASE ,      strength=__SCREAMING_SNAKE_CASE ,      num_inference_steps=__SCREAMING_SNAKE_CASE ,      guidance_scale=__SCREAMING_SNAKE_CASE ,      negative_prompt=__SCREAMING_SNAKE_CASE ,      num_images_per_prompt=__SCREAMING_SNAKE_CASE ,      eta=__SCREAMING_SNAKE_CASE ,      generator=__SCREAMING_SNAKE_CASE ,      output_type=__SCREAMING_SNAKE_CASE ,      return_dict=__SCREAMING_SNAKE_CASE ,      callback=__SCREAMING_SNAKE_CASE ,      callback_steps=__SCREAMING_SNAKE_CASE ,      )



 @torch.no_grad()
 def  snake_case__  (    self		:    str ,      a__		:    Any ,      a__		:    str = 512 ,      a__		:    List[Any] = 512 ,      a__		:    int = 50 ,      a__		:    Optional[Any] = 7.5 ,      a__		:    Union[str, Any] = None ,      a__		:    List[str] = 1 ,      a__		:    Union[str, Any] = 0.0 ,      a__		:    int = None ,      a__		:    Optional[int] = None ,      a__		:    Union[str, Any] = "pil" ,      a__		:    Optional[Any] = True ,      a__		:    List[str] = None ,      a__		:    Optional[int] = 1 ,      ):
      return StableDiffusionPipeline(**self.components			)(
          prompt=__SCREAMING_SNAKE_CASE ,      height=__SCREAMING_SNAKE_CASE ,      width=__SCREAMING_SNAKE_CASE ,      num_inference_steps=__SCREAMING_SNAKE_CASE ,      guidance_scale=__SCREAMING_SNAKE_CASE ,      negative_prompt=__SCREAMING_SNAKE_CASE ,      num_images_per_prompt=__SCREAMING_SNAKE_CASE ,      eta=__SCREAMING_SNAKE_CASE ,      generator=__SCREAMING_SNAKE_CASE ,      latents=__SCREAMING_SNAKE_CASE ,      output_type=__SCREAMING_SNAKE_CASE ,      return_dict=__SCREAMING_SNAKE_CASE ,      callback=__SCREAMING_SNAKE_CASE ,      callback_steps=__SCREAMING_SNAKE_CASE ,      )













'''simple docstring'''







from collections import namedtuple

_a					: Any      =    namedtuple("""from_to""", """from_ to""")

_a					: Dict      =    {
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

_a					: Optional[Any]      =    {
    """mm""": from_to(0.001, 1_0_0_0),
    """cm""": from_to(0.01, 1_0_0),
    """m""": from_to(1, 1),
    """km""": from_to(1_0_0_0, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.28084),
    """yd""": from_to(0.9144, 1.09361),
    """mi""": from_to(1609.34, 0.000621371),
}





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				from_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			__lowerCAmelCase											=				to_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			if new_from not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'from_type\' value: {from_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			if new_to not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'to_type\' value: {to_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()

