






'''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


__SCREAMING_SNAKE_CASE :  List[Any]  =						logging.get_logger(__name__)  # pylint: disable=invalid-name

class 			lowerCamelCase_			(_UpperCAmelCase					):
      '''simple docstring'''

      __UpperCamelCase:						Tuple      			=		["safety_checker", "feature_extractor"]




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                        F"""The configuration file of this scheduler: {scheduler} is outdated.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

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




      @property
      def   _A			( self		:       Any ):
             return {k: getattr(self		,      A ) for k in self.config.keys() if not k.startswith("_" )}




      def   _A			( self		:       Dict		,      A		:       List[Any] = "auto" ):






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




      def   _A			( self		:       Any ):
             self.enable_attention_slicing(A )




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




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





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


'''simple docstring'''




import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"    ,			[
        (Version("1.0.0"					), True),
        ("1.0.0", True),
        (Version("2.0.0"					), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]    ,			)
def 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   List[Any]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Union[str, Any]							   =					Version("1.0.0"					)
       assert (version == other) is expected_equality
       assert (version != other) is not expected_equality
       assert (hash(_UpperCAmelCase					) == hash(_UpperCAmelCase					)) is expected_equality

