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




class  __lowerCAmelCase	(							_a    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			["safety_checker", "feature_extractor"]

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		Dict = True							,      )			->				Union[str, Any]:
        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                   f"The configuration file of this scheduler: {scheduler} is outdated.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'							,      __SCREAMING_SNAKE_CASE							,      standard_warn=__SCREAMING_SNAKE_CASE	)
               _UpperCamelCase  :							Any	    =	dict(scheduler.config	)
               _UpperCamelCase  :							List[Any]	    =	1
               _UpperCamelCase  :							Dict	    =	FrozenDict(__SCREAMING_SNAKE_CASE	)

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

 @property
 def        __snake_case       (      self							:		Union[str, Any]	)			->				Union[str, Any]:
        return {k: getattr(self							,      __SCREAMING_SNAKE_CASE	) for k in self.config.keys() if not k.startswith('_'	)}

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Dict = "auto"	)			->				Optional[int]:




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

 def        __snake_case       (      self							:		Dict	)			->				Tuple:
        self.enable_attention_slicing(__SCREAMING_SNAKE_CASE	)

 @torch.no_grad()
 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		List[str] = 0.8							,      lowerCamelCase							:		Optional[Any] = 5_0							,      lowerCamelCase							:		int = 7.5							,      lowerCamelCase							:		List[Any] = None							,      lowerCamelCase							:		List[Any] = 1							,      lowerCamelCase							:		Dict = 0.0							,      lowerCamelCase							:		Union[str, Any] = None							,      lowerCamelCase							:		Tuple = "pil"							,      lowerCamelCase							:		Tuple = True							,      lowerCamelCase							:		Optional[Any] = None							,      lowerCamelCase							:		List[Any] = 1							,      )			->				Tuple:
        return StableDiffusionInpaintPipelineLegacy(**self.components	)(
            prompt=__SCREAMING_SNAKE_CASE							,      image=__SCREAMING_SNAKE_CASE							,      mask_image=__SCREAMING_SNAKE_CASE							,      strength=__SCREAMING_SNAKE_CASE							,      num_inference_steps=__SCREAMING_SNAKE_CASE							,      guidance_scale=__SCREAMING_SNAKE_CASE							,      negative_prompt=__SCREAMING_SNAKE_CASE							,      num_images_per_prompt=__SCREAMING_SNAKE_CASE							,      eta=__SCREAMING_SNAKE_CASE							,      generator=__SCREAMING_SNAKE_CASE							,      output_type=__SCREAMING_SNAKE_CASE							,      return_dict=__SCREAMING_SNAKE_CASE							,      callback=__SCREAMING_SNAKE_CASE							,      )

 @torch.no_grad()
 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Any = 0.8							,      lowerCamelCase							:		Optional[Any] = 5_0							,      lowerCamelCase							:		Optional[int] = 7.5							,      lowerCamelCase							:		List[str] = None							,      lowerCamelCase							:		int = 1							,      lowerCamelCase							:		str = 0.0							,      lowerCamelCase							:		Optional[Any] = None							,      lowerCamelCase							:		Any = "pil"							,      lowerCamelCase							:		Tuple = True							,      lowerCamelCase							:		Union[str, Any] = None							,      lowerCamelCase							:		Any = 1							,      **lowerCamelCase							:		Tuple							,      )			->				Tuple:
        return StableDiffusionImgaImgPipeline(**self.components	)(
            prompt=__SCREAMING_SNAKE_CASE							,      image=__SCREAMING_SNAKE_CASE							,      strength=__SCREAMING_SNAKE_CASE							,      num_inference_steps=__SCREAMING_SNAKE_CASE							,      guidance_scale=__SCREAMING_SNAKE_CASE							,      negative_prompt=__SCREAMING_SNAKE_CASE							,      num_images_per_prompt=__SCREAMING_SNAKE_CASE							,      eta=__SCREAMING_SNAKE_CASE							,      generator=__SCREAMING_SNAKE_CASE							,      output_type=__SCREAMING_SNAKE_CASE							,      return_dict=__SCREAMING_SNAKE_CASE							,      callback=__SCREAMING_SNAKE_CASE							,      callback_steps=__SCREAMING_SNAKE_CASE							,      )

 @torch.no_grad()
 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		Optional[Any]							,      lowerCamelCase							:		List[Any] = 5_1_2							,      lowerCamelCase							:		Union[str, Any] = 5_1_2							,      lowerCamelCase							:		Dict = 5_0							,      lowerCamelCase							:		Any = 7.5							,      lowerCamelCase							:		Optional[Any] = None							,      lowerCamelCase							:		Any = 1							,      lowerCamelCase							:		Any = 0.0							,      lowerCamelCase							:		str = None							,      lowerCamelCase							:		List[Any] = None							,      lowerCamelCase							:		Optional[Any] = "pil"							,      lowerCamelCase							:		Union[str, Any] = True							,      lowerCamelCase							:		List[str] = None							,      lowerCamelCase							:		int = 1							,      )			->				Any:
        return StableDiffusionPipeline(**self.components	)(
            prompt=__SCREAMING_SNAKE_CASE							,      height=__SCREAMING_SNAKE_CASE							,      width=__SCREAMING_SNAKE_CASE							,      num_inference_steps=__SCREAMING_SNAKE_CASE							,      guidance_scale=__SCREAMING_SNAKE_CASE							,      negative_prompt=__SCREAMING_SNAKE_CASE							,      num_images_per_prompt=__SCREAMING_SNAKE_CASE							,      eta=__SCREAMING_SNAKE_CASE							,      generator=__SCREAMING_SNAKE_CASE							,      latents=__SCREAMING_SNAKE_CASE							,      output_type=__SCREAMING_SNAKE_CASE							,      return_dict=__SCREAMING_SNAKE_CASE							,      callback=__SCREAMING_SNAKE_CASE							,      callback_steps=__SCREAMING_SNAKE_CASE							,      )







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__UpperCAmelCase			:			Optional[Any]			      =					2 << 20  # 2mb
__UpperCAmelCase			:			List[Any]			      =					get_logger(__name__)



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self._options.get_all_options().get('''runner'''    )
              return runner in [None, "DirectRunner", "PortableRunner"]




def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		FileSystems
       if fs.exists(SCREAMING_SNAKE_CASE_							):
              if force_upload:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.create(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''rb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		local_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            remote_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						Any 						=		local_file.read(SCREAMING_SNAKE_CASE_							)


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]=False							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		FileSystems
       if os.path.exists(SCREAMING_SNAKE_CASE_							):
              if force_download:
                     logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.open(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''wb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            local_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						str 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)

