



'''simple docstring'''






#
# Sort large text files in a minimum amount of memory
#
import argparse
import os



class 							lowercase :


 """simple docstring"""


 UpperCAmelCase					=						"""block_{0}.dat"""







 def __init__(							self						,a_							)	->     Optional[Any]:
        _UpperCAmelCase						:		int      =				filename
        _UpperCAmelCase						:		List[str]      =				[]







 def _snake_case				(							self						,a_						,a_							)	->     Tuple:
        _UpperCAmelCase						:		Optional[Any]      =				self.BLOCK_FILENAME_FORMAT.format(a_							)
        with open(a_						,"""w"""							) as file:
               file.write(a_							)
        self.block_filenames.append(a_							)







 def _snake_case				(							self							)	->     Any:
        return self.block_filenames







 def _snake_case				(							self						,a_						,a_=None							)	->     List[str]:
        _UpperCAmelCase						:		Optional[int]      =				0
        with open(self.filename							) as file:
               while True:
                      _UpperCAmelCase						:		List[str]      =				file.readlines(a_							)

                      if lines == []:
                             break

                      if sort_key is None:
                             lines.sort()
                      else:
                             lines.sort(key=a_							)

                      self.write_block("""""".join(a_							)						,a_							)
                      i += 1







 def _snake_case				(							self							)	->     Union[str, Any]:
        map(os.remove						,self.block_filenames							)







class 							lowercase :


 """simple docstring"""


 def _snake_case				(							self						,a_							)	->     Union[str, Any]:
        _UpperCAmelCase						:		int      =				-1
        _UpperCAmelCase						:		Optional[int]      =				None

        for i in range(len(a_							)							):
               if min_str is None or choices[i] < min_str:
                      _UpperCAmelCase						:		Optional[int]      =				i

        return min_index







class 							lowercase :


 """simple docstring"""


 def __init__(							self						,a_							)	->     Tuple:
        _UpperCAmelCase						:		Tuple      =				files
        _UpperCAmelCase						:		Optional[int]      =				set()
        _UpperCAmelCase						:		Any      =				len(a_							)
        _UpperCAmelCase						:		Any      =				{i: None for i in range(self.num_buffers							)}







 def _snake_case				(							self							)	->     Dict:
        return {
            i: self.buffers[i] for i in range(self.num_buffers							) if i not in self.empty
        }







 def _snake_case				(							self							)	->     Union[str, Any]:
        for i in range(self.num_buffers							):
               if self.buffers[i] is None and i not in self.empty:
                      _UpperCAmelCase						:		Dict      =				self.files[i].readline()

                      if self.buffers[i] == "":
                             self.empty.add(a_							)
                             self.files[i].close()

        if len(self.empty							) == self.num_buffers:
               return False

        return True







 def _snake_case				(							self						,a_							)	->     str:
        _UpperCAmelCase						:		List[str]      =				self.buffers[index]
        _UpperCAmelCase						:		Tuple      =				None

        return value







class 							lowercase :


 """simple docstring"""


 def __init__(							self						,a_							)	->     int:
        _UpperCAmelCase						:		str      =				merge_strategy







 def _snake_case				(							self						,a_						,a_						,a_							)	->     List[Any]:
        _UpperCAmelCase						:		Dict      =				FilesArray(self.get_file_handles(a_						,a_							)							)
        with open(a_						,"""w"""						,a_							) as outfile:
               while buffers.refresh():
                      _UpperCAmelCase						:		List[str]      =				self.merge_strategy.select(buffers.get_dict()							)
                      outfile.write(buffers.unshift(a_							)							)







 def _snake_case				(							self						,a_						,a_							)	->     str:
        _UpperCAmelCase						:		Optional[int]      =				{}

        for i in range(len(a_							)							):
               _UpperCAmelCase						:		Optional[int]      =				open(filenames[i]						,"""r"""						,a_							)  # noqa: UP015

        return files







class 							lowercase :


 """simple docstring"""


 def __init__(							self						,a_							)	->     int:
        _UpperCAmelCase						:		List[str]      =				block_size







 def _snake_case				(							self						,a_						,a_=None							)	->     List[str]:
        _UpperCAmelCase						:		List[str]      =				self.get_number_blocks(a_						,self.block_size							)
        _UpperCAmelCase						:		List[Any]      =				FileSplitter(a_							)
        splitter.split(self.block_size						,a_							)

        _UpperCAmelCase						:		str      =				FileMerger(NWayMerge()							)
        _UpperCAmelCase						:		int      =				self.block_size / (num_blocks + 1)
        merger.merge(splitter.get_block_filenames()						,filename + """.out"""						,a_							)

        splitter.cleanup()







 def _snake_case				(							self						,a_						,a_							)	->     Optional[int]:
        return (os.stat(a_							).st_size / block_size) + 1







def 					snake_case_      (  lowerCAmelCase_	)->	Tuple:





       '''simple docstring'''


       if string[-1].lower() == "k":
              return int(string[:-1]	) * 1024
       elif string[-1].lower() == "m":
              return int(string[:-1]	) * 1024 * 1024
       elif string[-1].lower() == "g":
              return int(string[:-1]	) * 1024 * 1024 * 1024
       else:
              return int(lowerCAmelCase_	)




def 					snake_case_      (  )->	str:





       '''simple docstring'''


       _UpperCAmelCase						:		List[Any]      =				argparse.ArgumentParser()
       parser.add_argument(
           """-m""" ,     """--mem""" ,     help="""amount of memory to use for sorting""" ,     default="""100M"""	)
       parser.add_argument(
           """filename""" ,     metavar="""<filename>""" ,     nargs=1 ,     help="""name of file to sort"""	)
       _UpperCAmelCase						:		List[Any]      =				parser.parse_args()

       _UpperCAmelCase						:		Optional[Any]      =				ExternalSort(parse_memory(args.mem	)	)
       sorter.sort(args.filename[0]	)


if __name__ == "__main__":
   main()








'''simple docstring'''






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



class 							lowercase (						_lowerCamelCase       ):


 """simple docstring"""


 UpperCAmelCase					=						["""safety_checker""", """feature_extractor"""]







 def __init__(							self						,a_						,a_						,a_						,a_						,a_						,a_						,a_						,a_ = True						,)	->     int:
        super().__init__()
        if hasattr(scheduler.config						,"""steps_offset"""							) and scheduler.config.steps_offset != 1:
               _UpperCAmelCase						:		Tuple      =				(
                   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"""						,a_						,standard_warn=a_							)
               _UpperCAmelCase						:		Any      =				dict(scheduler.config							)
               _UpperCAmelCase						:		str      =				1
               _UpperCAmelCase						:		Any      =				FrozenDict(a_							)

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







 @property
 def _snake_case				(							self							)	->     Dict[str, Any]:
        return {k: getattr(self						,a_							) for k in self.config.keys() if not k.startswith("""_"""							)}







 def _snake_case				(							self						,a_ = "auto"							)	->     Union[str, Any]:
        if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _UpperCAmelCase						:		Optional[int]      =				self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(a_							)







 def _snake_case				(							self							)	->     Tuple:
        self.enable_attention_slicing(a_							)







 @torch.no_grad()
 def _snake_case				(							self						,a_						,a_						,a_						,a_ = 0.8						,a_ = 50						,a_ = 7.5						,a_ = None						,a_ = 1						,a_ = 0.0						,a_ = None						,a_ = "pil"						,a_ = True						,a_ = None						,a_ = 1						,)	->     List[Any]:
        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
        return StableDiffusionInpaintPipelineLegacy(**self.components							)(
            prompt=a_						,image=a_						,mask_image=a_						,strength=a_						,num_inference_steps=a_						,guidance_scale=a_						,negative_prompt=a_						,num_images_per_prompt=a_						,eta=a_						,generator=a_						,output_type=a_						,return_dict=a_						,callback=a_						,)







 @torch.no_grad()
 def _snake_case				(							self						,a_						,a_						,a_ = 0.8						,a_ = 50						,a_ = 7.5						,a_ = None						,a_ = 1						,a_ = 0.0						,a_ = None						,a_ = "pil"						,a_ = True						,a_ = None						,a_ = 1						,**a_						,)	->     Tuple:
        # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
        return StableDiffusionImgaImgPipeline(**self.components							)(
            prompt=a_						,image=a_						,strength=a_						,num_inference_steps=a_						,guidance_scale=a_						,negative_prompt=a_						,num_images_per_prompt=a_						,eta=a_						,generator=a_						,output_type=a_						,return_dict=a_						,callback=a_						,callback_steps=a_						,)







 @torch.no_grad()
 def _snake_case				(							self						,a_						,a_ = 512						,a_ = 512						,a_ = 50						,a_ = 7.5						,a_ = None						,a_ = 1						,a_ = 0.0						,a_ = None						,a_ = None						,a_ = "pil"						,a_ = True						,a_ = None						,a_ = 1						,)	->     List[str]:
        # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
        return StableDiffusionPipeline(**self.components							)(
            prompt=a_						,height=a_						,width=a_						,num_inference_steps=a_						,guidance_scale=a_						,negative_prompt=a_						,num_images_per_prompt=a_						,eta=a_						,generator=a_						,latents=a_						,output_type=a_						,return_dict=a_						,callback=a_						,callback_steps=a_						,)



