






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 							a_				(		snake_case_	):


       '''simple docstring'''






       __a:			Any									=					['''safety_checker''', '''feature_extractor''']

       def __init__(	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = True    ,    )      ->      List[Any]:
              '''simple docstring'''

              super().__init__()
              if hasattr(scheduler.config    ,    'steps_offset'     ) and scheduler.config.steps_offset != 1:
                     lowerCAmelCase_       		=  (
                         f'''The configuration file of this scheduler: {scheduler} is outdated.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'    ,    lowercase_    ,    standard_warn=lowercase_     )
                     lowerCAmelCase_       		=  dict(scheduler.config     )
                     lowerCAmelCase_       		=  1
                     lowerCAmelCase_       		=  FrozenDict(lowercase_     )

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

       @property
       def   _lowercase (	self     )      ->      Dict[str, Any]:
              '''simple docstring'''

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

       def   _lowercase (	self    ,    lowercase_ = "auto"     )      ->      Union[str, Any]:
              '''simple docstring'''

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

       def   _lowercase (	self     )      ->      Any:
              '''simple docstring'''

              self.enable_attention_slicing(lowercase_     )

       @torch.no_grad()
       def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 0.8    ,    lowercase_ = 5_0    ,    lowercase_ = 7.5    ,    lowercase_ = None    ,    lowercase_ = 1    ,    lowercase_ = 0.0    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    lowercase_ = None    ,    lowercase_ = 1    ,    )      ->      Union[str, Any]:
              '''simple docstring'''

              return StableDiffusionInpaintPipelineLegacy(**self.components     )(
                  prompt=lowercase_    ,    image=lowercase_    ,    mask_image=lowercase_    ,    strength=lowercase_    ,    num_inference_steps=lowercase_    ,    guidance_scale=lowercase_    ,    negative_prompt=lowercase_    ,    num_images_per_prompt=lowercase_    ,    eta=lowercase_    ,    generator=lowercase_    ,    output_type=lowercase_    ,    return_dict=lowercase_    ,    callback=lowercase_    ,    )

       @torch.no_grad()
       def   _lowercase (	self    ,    lowercase_    ,    lowercase_    ,    lowercase_ = 0.8    ,    lowercase_ = 5_0    ,    lowercase_ = 7.5    ,    lowercase_ = None    ,    lowercase_ = 1    ,    lowercase_ = 0.0    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    lowercase_ = None    ,    lowercase_ = 1    ,    **lowercase_    ,    )      ->      Optional[Any]:
              '''simple docstring'''

              return StableDiffusionImgaImgPipeline(**self.components     )(
                  prompt=lowercase_    ,    image=lowercase_    ,    strength=lowercase_    ,    num_inference_steps=lowercase_    ,    guidance_scale=lowercase_    ,    negative_prompt=lowercase_    ,    num_images_per_prompt=lowercase_    ,    eta=lowercase_    ,    generator=lowercase_    ,    output_type=lowercase_    ,    return_dict=lowercase_    ,    callback=lowercase_    ,    callback_steps=lowercase_    ,    )






       @torch.no_grad()
       def   _lowercase (	self    ,    lowercase_    ,    lowercase_ = 5_1_2    ,    lowercase_ = 5_1_2    ,    lowercase_ = 5_0    ,    lowercase_ = 7.5    ,    lowercase_ = None    ,    lowercase_ = 1    ,    lowercase_ = 0.0    ,    lowercase_ = None    ,    lowercase_ = None    ,    lowercase_ = "pil"    ,    lowercase_ = True    ,    lowercase_ = None    ,    lowercase_ = 1    ,    )      ->      Any:
              '''simple docstring'''

              return StableDiffusionPipeline(**self.components     )(
                  prompt=lowercase_    ,    height=lowercase_    ,    width=lowercase_    ,    num_inference_steps=lowercase_    ,    guidance_scale=lowercase_    ,    negative_prompt=lowercase_    ,    num_images_per_prompt=lowercase_    ,    eta=lowercase_    ,    generator=lowercase_    ,    latents=lowercase_    ,    output_type=lowercase_    ,    return_dict=lowercase_    ,    callback=lowercase_    ,    callback_steps=lowercase_    ,    )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_		  =						{
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase_		  =						[
		    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """ProphetNetDecoder""",
		    """ProphetNetEncoder""",
		    """ProphetNetForCausalLM""",
		    """ProphetNetForConditionalGeneration""",
		    """ProphetNetModel""",
		    """ProphetNetPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
		from .tokenization_prophetnet import ProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_prophetnet import (
				    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ProphetNetDecoder,
				    ProphetNetEncoder,
				    ProphetNetForCausalLM,
				    ProphetNetForConditionalGeneration,
				    ProphetNetModel,
				    ProphetNetPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase_		  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



