
"""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


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

class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: Optional[Any]					      =      ["""safety_checker""", """feature_extractor"""]
							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = True     ,						) ->       Any:




										'''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													    f'''The configuration file of this scheduler: {scheduler} is outdated.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      SCREAMING_SNAKE_CASE	(    self					) ->       Dict[str, Any]:




										'''simple docstring'''
										return {k: getattr(self     ,						_lowerCamelCase					) for k in self.config.keys() if not k.startswith("_"					)}
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase = "auto"					) ->       Tuple:




										'''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      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										self.enable_attention_slicing(_lowerCamelCase					)
							@torch.no_grad()
							def      SCREAMING_SNAKE_CASE	(    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     ,						) ->       Dict:




										'''simple docstring'''
										# 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=_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      SCREAMING_SNAKE_CASE	(    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     ,						) ->       int:




										'''simple docstring'''
										# 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=_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      SCREAMING_SNAKE_CASE	(    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'''
										# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
										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     ,						)







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowercase							 =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





