




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





class  lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       					=					"block_{0}.dat"



 def __init__(							self						:	Dict       ,       _lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    filename
       SCREAMING_SNAKE_CASE_					=    []



 def    lowerCAmelCase_						(							self						:	Dict       ,       _lowerCAmelCase						:	Union[str, Any]       ,       _lowerCAmelCase						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    self.BLOCK_FILENAME_FORMAT.format(_lowerCAmelCase		)
       with open(_lowerCAmelCase       ,       'w'		) as file:
             file.write(_lowerCAmelCase		)
       self.block_filenames.append(_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       return self.block_filenames



 def    lowerCAmelCase_						(							self						:	Union[str, Any]       ,       _lowerCAmelCase						:	List[Any]       ,       _lowerCAmelCase						:	Any=None		):
       SCREAMING_SNAKE_CASE_					=    0
       with open(self.filename		) as file:
             while True:
                   SCREAMING_SNAKE_CASE_					=    file.readlines(_lowerCAmelCase		)

                   if lines == []:
                         break

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

                   self.write_block(''.join(_lowerCAmelCase		)       ,       _lowerCAmelCase		)
                   i += 1




 def    lowerCAmelCase_						(							self						:	Dict		):
       map(os.remove       ,       self.block_filenames		)





class  lowerCamelCase_							:


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	int       ,       _lowerCAmelCase						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    -1
       SCREAMING_SNAKE_CASE_					=    None

       for i in range(len(_lowerCAmelCase		)		):
             if min_str is None or choices[i] < min_str:
                   SCREAMING_SNAKE_CASE_					=    i

       return min_index





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	int       ,       _lowerCAmelCase						:	str		):
       SCREAMING_SNAKE_CASE_					=    files
       SCREAMING_SNAKE_CASE_					=    set()
       SCREAMING_SNAKE_CASE_					=    len(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    {i: None for i in range(self.num_buffers		)}



 def    lowerCAmelCase_						(							self						:	Any		):
       return {
           i: self.buffers[i] for i in range(self.num_buffers		) if i not in self.empty
       }



 def    lowerCAmelCase_						(							self						:	str		):
       for i in range(self.num_buffers		):
             if self.buffers[i] is None and i not in self.empty:
                   SCREAMING_SNAKE_CASE_					=    self.files[i].readline()

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

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

       return True




 def    lowerCAmelCase_						(							self						:	Union[str, Any]       ,       _lowerCAmelCase						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    self.buffers[index]
       SCREAMING_SNAKE_CASE_					=    None

       return value





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	int       ,       _lowerCAmelCase						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    merge_strategy



 def    lowerCAmelCase_						(							self						:	int       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	Dict       ,       _lowerCAmelCase						:	str		):
       SCREAMING_SNAKE_CASE_					=    FilesArray(self.get_file_handles(_lowerCAmelCase       ,       _lowerCAmelCase		)		)
       with open(_lowerCAmelCase       ,       'w'       ,       _lowerCAmelCase		) as outfile:
             while buffers.refresh():
                   SCREAMING_SNAKE_CASE_					=    self.merge_strategy.select(buffers.get_dict()		)
                   outfile.write(buffers.unshift(_lowerCAmelCase		)		)




 def    lowerCAmelCase_						(							self						:	int       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    {}

       for i in range(len(_lowerCAmelCase		)		):
             SCREAMING_SNAKE_CASE_					=    open(filenames[i]       ,       'r'       ,       _lowerCAmelCase		)  # noqa: UP015

       return files





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	List[str]       ,       _lowerCAmelCase						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    block_size



 def    lowerCAmelCase_						(							self						:	Dict       ,       _lowerCAmelCase						:	Optional[int]       ,       _lowerCAmelCase						:	Optional[int]=None		):
       SCREAMING_SNAKE_CASE_					=    self.get_number_blocks(_lowerCAmelCase       ,       self.block_size		)
       SCREAMING_SNAKE_CASE_					=    FileSplitter(_lowerCAmelCase		)
       splitter.split(self.block_size       ,       _lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    FileMerger(NWayMerge()		)
       SCREAMING_SNAKE_CASE_					=    self.block_size / (num_blocks + 1)
       merger.merge(splitter.get_block_filenames()       ,       filename + '.out'       ,       _lowerCAmelCase		)

       splitter.cleanup()




 def    lowerCAmelCase_						(							self						:	Optional[Any]       ,       _lowerCAmelCase						:	List[str]       ,       _lowerCAmelCase						:	List[str]		):
       return (os.stat(_lowerCAmelCase		).st_size / block_size) + 1


def       UpperCAmelCase_							(   __UpperCAmelCase		:			int							)      ->				Optional[int]:
      if string[-1].lower() == "k":
            return int(string[:-1]							) * 10_24
      elif string[-1].lower() == "m":
            return int(string[:-1]							) * 10_24 * 10_24
      elif string[-1].lower() == "g":
            return int(string[:-1]							) * 10_24 * 10_24 * 10_24
      else:
            return int(__UpperCAmelCase							)

def       UpperCAmelCase_							(   )      ->				Optional[int]:
      SCREAMING_SNAKE_CASE_					=    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'							)
      SCREAMING_SNAKE_CASE_					=    parser.parse_args()

      SCREAMING_SNAKE_CASE_					=    ExternalSort(parse_memory(args.mem							)							)
      sorter.sort(args.filename[0]							)


if __name__ == "__main__":
   main()





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					RePaintPipeline
 lowercase_       					=					IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
 lowercase_       					=					PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_       					=					IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	int		):
       torch.manual_seed(0		)
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    UNetaDModel(
           block_out_channels=(32, 64)       ,       layers_per_block=2       ,       sample_size=32       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=('DownBlock2D', 'AttnDownBlock2D')       ,       up_block_types=('AttnUpBlock2D', 'UpBlock2D')       ,       )
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler()
       SCREAMING_SNAKE_CASE_					=    {'unet': unet, 'scheduler': scheduler}
       return components



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str=0		):
       if str(_lowerCAmelCase		).startswith('mps'		):
             SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_					=    torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    np.random.RandomState(_lowerCAmelCase		).standard_normal((1, 3, 32, 32)		)
       SCREAMING_SNAKE_CASE_					=    torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    (image > 0).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs



 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_local()



 @unittest.skip('non-deterministic pipeline'		)
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_inference_batch_single_identical()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_optional_components()




 @skip_mps
 def    lowerCAmelCase_						(							self						:	Tuple		):
       return super().test_attention_slicing_forward_pass()





@nightly
@require_torch_gpu
class  lowerCamelCase_							(   unittest.TestCase      ):


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	List[str]		):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def    lowerCAmelCase_						(							self						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'		)

       SCREAMING_SNAKE_CASE_					=    'google/ddpm-ema-celebahq-256'
       SCREAMING_SNAKE_CASE_					=    UNetaDModel.from_pretrained(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler.from_pretrained(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(unet=_lowerCAmelCase       ,       scheduler=_lowerCAmelCase		).to(_lowerCAmelCase		)
       repaint.set_progress_bar_config(disable=_lowerCAmelCase		)
       repaint.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    repaint(
           _lowerCAmelCase       ,       _lowerCAmelCase       ,       num_inference_steps=250       ,       eta=0.0       ,       jump_length=10       ,       jump_n_sample=10       ,       generator=_lowerCAmelCase       ,       output_type='np'       ,       )
       SCREAMING_SNAKE_CASE_					=    output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image		).mean() < 1E-2