




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


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

class      UpperCAmelCase						(			__UpperCAmelCase   ):
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					["""safety_checker""", """feature_extractor"""]






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       Tuple = True						,					)->			Tuple:
     super().__init__()
     if hasattr(scheduler.config						,					'steps_offset'     ) and scheduler.config.steps_offset != 1:
       _lowercase: List[Any]						     =    (
           f'The configuration file of this scheduler: {scheduler} is outdated.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: Tuple						     =    dict(scheduler.config     )
       _lowercase: List[Any]						     =    1
       _lowercase: Union[str, Any]						     =    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   :       int     )->			Dict[str, Any]:
     return {k: getattr(self						,					lowerCamelCase__     ) for k in self.config.keys() if not k.startswith('_'     )}






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       Dict = "auto"     )->			Union[str, Any]:



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






   def  _a						(	self   :       List[Any]     )->			str:
     self.enable_attention_slicing(lowerCamelCase__     )






   @torch.no_grad()
   def  _a						(	self   :       int						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Any = 0.8						,					UpperCamelCase_   :       str = 50						,					UpperCamelCase_   :       Optional[int] = 7.5						,					UpperCamelCase_   :       Dict = None						,					UpperCamelCase_   :       Any = 1						,					UpperCamelCase_   :       Optional[int] = 0.0						,					UpperCamelCase_   :       Tuple = None						,					UpperCamelCase_   :       Optional[int] = "pil"						,					UpperCamelCase_   :       List[str] = True						,					UpperCamelCase_   :       List[str] = None						,					UpperCamelCase_   :       List[str] = 1						,					)->			str:
     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   :       List[str]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       Dict = 0.8						,					UpperCamelCase_   :       Optional[Any] = 50						,					UpperCamelCase_   :       Tuple = 7.5						,					UpperCamelCase_   :       List[Any] = None						,					UpperCamelCase_   :       List[Any] = 1						,					UpperCamelCase_   :       Tuple = 0.0						,					UpperCamelCase_   :       Optional[Any] = None						,					UpperCamelCase_   :       str = "pil"						,					UpperCamelCase_   :       Tuple = True						,					UpperCamelCase_   :       Tuple = None						,					UpperCamelCase_   :       Tuple = 1						,					**UpperCamelCase_   :       Dict						,					)->			Any:
     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   :       List[Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       List[Any] = 5_12						,					UpperCamelCase_   :       Optional[Any] = 5_12						,					UpperCamelCase_   :       Optional[int] = 50						,					UpperCamelCase_   :       List[str] = 7.5						,					UpperCamelCase_   :       Optional[Any] = None						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       List[Any] = 0.0						,					UpperCamelCase_   :       int = None						,					UpperCamelCase_   :       str = None						,					UpperCamelCase_   :       Dict = "pil"						,					UpperCamelCase_   :       Dict = True						,					UpperCamelCase_   :       Any = None						,					UpperCamelCase_   :       Optional[Any] = 1						,					)->			Dict:
     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__						,					)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__						:       Union[str, Any]							=    {'configuration_bert_generation': ['BertGenerationConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       str							=    ['BertGenerationTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       'BertGenerationDecoder',
       'BertGenerationEncoder',
       'BertGenerationPreTrainedModel',
       'load_tf_weights_in_bert_generation',
   ]


if TYPE_CHECKING:
   from .configuration_bert_generation import BertGenerationConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_generation import BertGenerationTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert_generation import (
          BertGenerationDecoder,
          BertGenerationEncoder,
          BertGenerationPreTrainedModel,
          load_tf_weights_in_bert_generation,
      )

else:
   import sys

   UpperCAmelCase__						:       List[Any]							=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
