






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


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





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				["safety_checker", "feature_extractor"]

	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase = True	,   ):
							"""simple docstring"""







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													    F"""The configuration file of this scheduler: {scheduler} is outdated.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

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

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase = "auto"   ):
							"""simple docstring"""







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

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							self.enable_attention_slicing(UpperCAmelCase   )

	@torch.no_grad()
	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase = 0.8	,   UpperCAmelCase = 50	,   UpperCAmelCase = 7.5	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   UpperCAmelCase = 0.0	,   UpperCAmelCase = None	,   UpperCAmelCase = "pil"	,   UpperCAmelCase = True	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   ):
							"""simple docstring"""







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

	@torch.no_grad()
	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase = 0.8	,   UpperCAmelCase = 50	,   UpperCAmelCase = 7.5	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   UpperCAmelCase = 0.0	,   UpperCAmelCase = None	,   UpperCAmelCase = "pil"	,   UpperCAmelCase = True	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







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

	@torch.no_grad()
	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = 512	,   UpperCAmelCase = 512	,   UpperCAmelCase = 50	,   UpperCAmelCase = 7.5	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   UpperCAmelCase = 0.0	,   UpperCAmelCase = None	,   UpperCAmelCase = None	,   UpperCAmelCase = "pil"	,   UpperCAmelCase = True	,   UpperCAmelCase = None	,   UpperCAmelCase = 1	,   ):
							"""simple docstring"""







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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				     =  logging.get_logger(__name__)

_a				     =  {
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"regnet"
	UpperCamelCase__								=				["x", "y"]

	def __init__(							self	,   UpperCAmelCase=3	,   UpperCAmelCase=32	,   UpperCAmelCase=[128, 192, 512, 1088]	,   UpperCAmelCase=[2, 6, 12, 2]	,   UpperCAmelCase=64	,   UpperCAmelCase="y"	,   UpperCAmelCase="relu"	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase   )
							if layer_type not in self.layer_types:
													raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types   )}"""   )
							_UpperCAmelCase     =      num_channels
							_UpperCAmelCase     =      embedding_size
							_UpperCAmelCase     =      hidden_sizes
							_UpperCAmelCase     =      depths
							_UpperCAmelCase     =      groups_width
							_UpperCAmelCase     =      layer_type
							_UpperCAmelCase     =      hidden_act
							# always downsample in the first stage
							_UpperCAmelCase     =      True
