






"""simple docstring"""



from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)




class  lowerCAmelCase__ (						__magic_name__      ):
   def __init__(		self			:  Dict       , snake_case__			:  AutoencoderKL       , snake_case__			:  CLIPTextModel       , snake_case__			:  CLIPTokenizer       , snake_case__			:  UNetaDConditionModel       , snake_case__			:  Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]       , ):


        '''simple docstring'''
        super().__init__()

        self.register_modules(vae=snake_case__       , text_encoder=snake_case__       , tokenizer=snake_case__       , unet=snake_case__       , scheduler=snake_case__       )
   def 			__a       (		self			:  List[Any]       , snake_case__			:  str       ):


        '''simple docstring'''
        with torch.no_grad():
             UpperCAmelCase__	:						Any       =       self.vae.encode(tfms.ToTensor()(snake_case__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
             UpperCAmelCase__	:						List[Any]       =       0.1_8215 * latent.latent_dist.sample()
        return latent
   def 			__a       (		self			:  str       , snake_case__			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       (1 / 0.1_8215) * latent
        with torch.no_grad():
             UpperCAmelCase__	:						Optional[Any]       =       self.vae.decode(snake_case__       ).sample
        UpperCAmelCase__	:						List[str]       =       (img / 2 + 0.5).clamp(0       , 1       )
        UpperCAmelCase__	:						List[Any]       =       img.detach().cpu().permute(0       , 2       , 3       , 1       ).numpy()
        UpperCAmelCase__	:						str       =       (img * 2_5_5).round().astype("uint8"       )
        return Image.fromarray(img[0]       )
   def 			__a       (		self			:  Tuple       , snake_case__			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       self.tokenizer(
            snake_case__       , padding="max_length"       , max_length=self.tokenizer.model_max_length       , truncation=snake_case__       , return_tensors="pt"       , )

        UpperCAmelCase__	:						Tuple       =       self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

        UpperCAmelCase__	:						List[Any]       =       self.tokenizer(
            ""       , padding="max_length"       , max_length=self.tokenizer.model_max_length       , truncation=snake_case__       , return_tensors="pt"       , )

        UpperCAmelCase__	:						Optional[Any]       =       self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        return torch.cat([uncond_embedding, text_embedding]       )




   def __call__(		self			:  Any       , snake_case__			:  Image.Image       , snake_case__			:  str       , snake_case__			:  float = 0.3       , snake_case__			:  float = 0.6       , snake_case__			:  float = 0.5       , snake_case__			:  int = 4_2       , snake_case__			:  int = 5_0       , snake_case__			:  float = 7.5       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       steps - int(kmin * steps       )
        UpperCAmelCase__	:						Dict       =       steps - int(kmax * steps       )

        UpperCAmelCase__	:						int       =       self.prep_text(snake_case__       )

        self.scheduler.set_timesteps(snake_case__       )

        UpperCAmelCase__       ,							UpperCAmelCase__	:						Any       =       img.size
        UpperCAmelCase__	:						Any       =       self.encode(snake_case__       )

        torch.manual_seed(snake_case__       )
        UpperCAmelCase__	:						str       =       torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       , ).to(self.device       )

        UpperCAmelCase__	:						Dict       =       self.scheduler.add_noise(
            snake_case__       , snake_case__       , timesteps=self.scheduler.timesteps[tmax]       , )

        UpperCAmelCase__	:						List[str]       =       torch.cat([latents] * 2       )

        UpperCAmelCase__	:						Any       =       self.scheduler.scale_model_input(snake_case__       , self.scheduler.timesteps[tmax]       )

        with torch.no_grad():
             UpperCAmelCase__	:						str       =       self.unet(
                 snake_case__       , self.scheduler.timesteps[tmax]       , encoder_hidden_states=snake_case__       , ).sample

        UpperCAmelCase__       ,							UpperCAmelCase__	:						Tuple       =       pred.chunk(2       )
        UpperCAmelCase__	:						Optional[int]       =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

        UpperCAmelCase__	:						str       =       self.scheduler.step(snake_case__       , self.scheduler.timesteps[tmax]       , snake_case__       ).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
             if i > tmax:
                  if i < tmin:  # layout generation phase
                       UpperCAmelCase__	:						Union[str, Any]       =       self.scheduler.add_noise(
                           snake_case__       , snake_case__       , timesteps=snake_case__       , )

                       UpperCAmelCase__	:						List[str]       =       (mix_factor * latents) + (
                           1 - mix_factor
                       ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                       UpperCAmelCase__	:						Dict       =       torch.cat([input] * 2       )

                  else:  # content generation phase
                       UpperCAmelCase__	:						Dict       =       torch.cat([latents] * 2       )

                  UpperCAmelCase__	:						Union[str, Any]       =       self.scheduler.scale_model_input(snake_case__       , snake_case__       )

                  with torch.no_grad():
                       UpperCAmelCase__	:						Dict       =       self.unet(
                           snake_case__       , snake_case__       , encoder_hidden_states=snake_case__       , ).sample

                  UpperCAmelCase__       ,							UpperCAmelCase__	:						Tuple       =       pred.chunk(2       )
                  UpperCAmelCase__	:						Tuple       =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

                  UpperCAmelCase__	:						str       =       self.scheduler.step(snake_case__       , snake_case__       , snake_case__       ).prev_sample

        return self.decode(snake_case__       )










"""simple docstring"""



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




class  lowerCAmelCase__ :
   SCREAMING_SNAKE_CASE_         ='''block_{0}.dat'''
   def __init__(		self			:  Tuple       , snake_case__			:  List[str]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       filename
        UpperCAmelCase__	:						List[Any]       =       []
   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  Union[str, Any]       , snake_case__			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       self.BLOCK_FILENAME_FORMAT.format(snake_case__       )
        with open(snake_case__       , "w"       ) as file:
             file.write(snake_case__       )
        self.block_filenames.append(snake_case__       )
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        return self.block_filenames
   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  List[str]       , snake_case__			:  str=None       ):


        '''simple docstring'''
        UpperCAmelCase__	:						int       =       0
        with open(self.filename       ) as file:
             while True:
                  UpperCAmelCase__	:						Union[str, Any]       =       file.readlines(snake_case__       )

                  if lines == []:
                       break

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

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




   def 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        map(os.remove       , self.block_filenames       )




class  lowerCAmelCase__ :
   def 			__a       (		self			:  Union[str, Any]       , snake_case__			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       -1
        UpperCAmelCase__	:						Tuple       =       None

        for i in range(len(snake_case__       )       ):
             if min_str is None or choices[i] < min_str:
                  UpperCAmelCase__	:						List[str]       =       i

        return min_index




class  lowerCAmelCase__ :
   def __init__(		self			:  Dict       , snake_case__			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[Any]       =       files
        UpperCAmelCase__	:						str       =       set()
        UpperCAmelCase__	:						Any       =       len(snake_case__       )
        UpperCAmelCase__	:						Tuple       =       {i: None for i in range(self.num_buffers       )}
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        return {
            i: self.buffers[i] for i in range(self.num_buffers       ) if i not in self.empty
        }
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        for i in range(self.num_buffers       ):
             if self.buffers[i] is None and i not in self.empty:
                  UpperCAmelCase__	:						Tuple       =       self.files[i].readline()

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

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

        return True




   def 			__a       (		self			:  List[str]       , snake_case__			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       self.buffers[index]
        UpperCAmelCase__	:						Union[str, Any]       =       None

        return value




class  lowerCAmelCase__ :
   def __init__(		self			:  Optional[Any]       , snake_case__			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       merge_strategy
   def 			__a       (		self			:  Dict       , snake_case__			:  Dict       , snake_case__			:  Optional[int]       , snake_case__			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       FilesArray(self.get_file_handles(snake_case__       , snake_case__       )       )
        with open(snake_case__       , "w"       , snake_case__       ) as outfile:
             while buffers.refresh():
                  UpperCAmelCase__	:						Any       =       self.merge_strategy.select(buffers.get_dict()       )
                  outfile.write(buffers.unshift(snake_case__       )       )




   def 			__a       (		self			:  Tuple       , snake_case__			:  Dict       , snake_case__			:  List[str]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       {}

        for i in range(len(snake_case__       )       ):
             UpperCAmelCase__	:						List[Any]       =       open(filenames[i]       , "r"       , snake_case__       )  # noqa: UP015

        return files




class  lowerCAmelCase__ :
   def __init__(		self			:  str       , snake_case__			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       block_size
   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  Optional[int]       , snake_case__			:  Any=None       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       self.get_number_blocks(snake_case__       , self.block_size       )
        UpperCAmelCase__	:						Union[str, Any]       =       FileSplitter(snake_case__       )
        splitter.split(self.block_size       , snake_case__       )

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

        splitter.cleanup()




   def 			__a       (		self			:  Union[str, Any]       , snake_case__			:  Optional[int]       , snake_case__			:  Optional[Any]       ):


        '''simple docstring'''
        return (os.stat(snake_case__       ).st_size / block_size) + 1




def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Tuple							)->		Union[str, Any]:






     '''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(snake_case							)


def   SCREAMING_SNAKE_CASE__					(     )->		Any:






     '''simple docstring'''



     UpperCAmelCase__	:						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__	:						int       =       parser.parse_args()

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


if __name__ == "__main__":
 main()


