






'''simple docstring'''

import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE__											=     get_logger(__name__)






class        a_		(    enum.Enum       ):
  lowercase  	=   """all_checks"""
  lowercase  	=   """basic_checks"""
  lowercase  	=   """no_checks"""






class        a_		(    lowerCamelCase       ):
  pass






class        a_		(    lowerCamelCase       ):
  pass






class        a_		(    lowerCamelCase       ):
  pass






class        a_		(    lowerCamelCase       ):
  pass







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None					)->				int:
      if expected_checksums is None:
            logger.info("""Unable to verify checksums."""					)
            return
      if len(set(__UpperCamelCase					) - set(__UpperCamelCase					)					) > 0:
            raise ExpectedMoreDownloadedFiles(str(set(__UpperCamelCase					) - set(__UpperCamelCase					)					)					)
      if len(set(__UpperCamelCase					) - set(__UpperCamelCase					)					) > 0:
            raise UnexpectedDownloadedFile(str(set(__UpperCamelCase					) - set(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
      UpperCamelCase       						=      """ for """ + verification_name if verification_name is not None else """"""
      if len(__UpperCamelCase					) > 0:
            raise NonMatchingChecksumError(
                F"Checksums didn't match{for_verification_name}:\n"
                F"{bad_urls}\n"
                """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""					)
      logger.info("""All the checksums matched successfully""" + for_verification_name					)






class        a_		(    lowerCamelCase       ):
  pass






class        a_		(    lowerCamelCase       ):
  pass






class        a_		(    lowerCamelCase       ):
  pass






class        a_		(    lowerCamelCase       ):
  pass







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[str]:
      if expected_splits is None:
            logger.info("""Unable to verify splits sizes."""					)
            return
      if len(set(__UpperCamelCase					) - set(__UpperCamelCase					)					) > 0:
            raise ExpectedMoreSplits(str(set(__UpperCamelCase					) - set(__UpperCamelCase					)					)					)
      if len(set(__UpperCamelCase					) - set(__UpperCamelCase					)					) > 0:
            raise UnexpectedSplits(str(set(__UpperCamelCase					) - set(__UpperCamelCase					)					)					)
      UpperCamelCase       						=      [
          {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
          for name in expected_splits
          if expected_splits[name].num_examples != recorded_splits[name].num_examples
      ]
      if len(__UpperCamelCase					) > 0:
            raise NonMatchingSplitsSizesError(str(__UpperCamelCase					)					)
      logger.info("""All the splits matched successfully."""					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = True					)->				dict:
      if record_checksum:
            UpperCamelCase       						=      shaaaa()
            with open(__UpperCamelCase	,		"""rb"""					) as f:
                  for chunk in iter(lambda: f.read(1 << 20					)	,		b""""""					):
                        m.update(__UpperCamelCase					)
                  UpperCamelCase       						=      m.hexdigest()
      else:
            UpperCamelCase       						=      None
      return {"num_bytes": os.path.getsize(__UpperCamelCase					), "checksum": checksum}







def 					lowercase__				(   __UpperCamelCase					)->				List[str]:
      if dataset_size and config.IN_MEMORY_MAX_SIZE:
            return dataset_size < config.IN_MEMORY_MAX_SIZE
      else:
            return False








'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


SCREAMING_SNAKE_CASE__											=     'CompVis/stable-diffusion-v1-1'
SCREAMING_SNAKE_CASE__											=     'CompVis/stable-diffusion-v1-2'
SCREAMING_SNAKE_CASE__											=     'CompVis/stable-diffusion-v1-3'
SCREAMING_SNAKE_CASE__											=     'CompVis/stable-diffusion-v1-4'






class        a_		(    lowerCamelCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = True    ,	)						->   Any:



        """simple docstring"""






        super()._init_()

        UpperCamelCase       						=      StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      StableDiffusionPipeline(
            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    ,	requires_safety_checker=_SCREAMING_SNAKE_CASE    ,	)

        self.register_modules(pipelinea=self.pipea    ,	pipelinea=self.pipea    ,	pipelinea=self.pipea    ,	pipelinea=self.pipea   )

  @property
  def 						A__		(   self   )						->   Dict[str, Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE = "auto"   )						->   Optional[Any]:



        """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(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        self.enable_attention_slicing(_SCREAMING_SNAKE_CASE   )

  @torch.no_grad()
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 50    ,	_SCREAMING_SNAKE_CASE = 7.5    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "pil"    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[Any]:



        """simple docstring"""






        return self.pipea(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)

  @torch.no_grad()
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 50    ,	_SCREAMING_SNAKE_CASE = 7.5    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "pil"    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[Any]:



        """simple docstring"""






        return self.pipea(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)

  @torch.no_grad()
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 50    ,	_SCREAMING_SNAKE_CASE = 7.5    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "pil"    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Dict:



        """simple docstring"""






        return self.pipea(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)

  @torch.no_grad()
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 50    ,	_SCREAMING_SNAKE_CASE = 7.5    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "pil"    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   str:



        """simple docstring"""






        return self.pipea(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)


  @torch.no_grad()
  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 50    ,	_SCREAMING_SNAKE_CASE = 7.5    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "pil"    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      """cuda""" if torch.cuda.is_available() else """cpu"""
        self.to(_SCREAMING_SNAKE_CASE   )

        # Checks if the height and width are divisible by 8 or not
        if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F"`height` and `width` must be divisible by 8 but are {height} and {width}."   )

        # Get first result from Stable Diffusion Checkpoint v1.1
        UpperCamelCase       						=      self.textaimg_sda_a(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)

        # Get first result from Stable Diffusion Checkpoint v1.2
        UpperCamelCase       						=      self.textaimg_sda_a(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)

        # Get first result from Stable Diffusion Checkpoint v1.3
        UpperCamelCase       						=      self.textaimg_sda_a(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)

        # Get first result from Stable Diffusion Checkpoint v1.4
        UpperCamelCase       						=      self.textaimg_sda_a(
            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    ,	**_SCREAMING_SNAKE_CASE    ,	)

        # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
        return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]   )
