


'''simple docstring'''

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





class     __SCREAMING_SNAKE_CASE       :
     UpperCAmelCase			       =					'''block_{0}.dat'''






     def __init__(      self  ,				__UpperCamelCase		)							->     Optional[Any]:
          _a            =		filename
          _a            =		[]






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Any:
          _a            =		self.BLOCK_FILENAME_FORMAT.format(__UpperCamelCase		)
          with open(__UpperCamelCase  ,				"w"		) as file:
               file.write(__UpperCamelCase		)
          self.block_filenames.append(__UpperCamelCase		)






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






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=None		)							->     Tuple:
          _a            =		0
          with open(self.filename		) as file:
               while True:
                    _a            =		file.readlines(__UpperCamelCase		)

                    if lines == []:
                         break

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

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






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







class     __SCREAMING_SNAKE_CASE       :






     def 			a_       (      self  ,				__UpperCamelCase		)							->     List[str]:
          _a            =		-1
          _a            =		None

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

          return min_index







class     __SCREAMING_SNAKE_CASE       :






     def __init__(      self  ,				__UpperCamelCase		)							->     int:
          _a            =		files
          _a            =		set()
          _a            =		len(__UpperCamelCase		)
          _a            =		{i: None for i in range(self.num_buffers		)}






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






     def 			a_       (      self		)							->     Any:
          for i in range(self.num_buffers		):
               if self.buffers[i] is None and i not in self.empty:
                    _a            =		self.files[i].readline()

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

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

          return True






     def 			a_       (      self  ,				__UpperCamelCase		)							->     List[str]:
          _a            =		self.buffers[index]
          _a            =		None

          return value







class     __SCREAMING_SNAKE_CASE       :






     def __init__(      self  ,				__UpperCamelCase		)							->     List[Any]:
          _a            =		merge_strategy






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Tuple:
          _a            =		FilesArray(self.get_file_handles(__UpperCamelCase  ,				__UpperCamelCase		)		)
          with open(__UpperCamelCase  ,				"w"  ,				__UpperCamelCase		) as outfile:
               while buffers.refresh():
                    _a            =		self.merge_strategy.select(buffers.get_dict()		)
                    outfile.write(buffers.unshift(__UpperCamelCase		)		)






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     Union[str, Any]:
          _a            =		{}

          for i in range(len(__UpperCamelCase		)		):
               _a            =		open(filenames[i]  ,				"r"  ,				__UpperCamelCase		)  # noqa: UP015

          return files







class     __SCREAMING_SNAKE_CASE       :






     def __init__(      self  ,				__UpperCamelCase		)							->     str:
          _a            =		block_size






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=None		)							->     str:
          _a            =		self.get_number_blocks(__UpperCamelCase  ,				self.block_size		)
          _a            =		FileSplitter(__UpperCamelCase		)
          splitter.split(self.block_size  ,				__UpperCamelCase		)

          _a            =		FileMerger(NWayMerge()		)
          _a            =		self.block_size / (num_blocks + 1)
          merger.merge(splitter.get_block_filenames()  ,				filename + ".out"  ,				__UpperCamelCase		)

          splitter.cleanup()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase		)							->     List[str]:
          return (os.stat(__UpperCamelCase		).st_size / block_size) + 1







def __UpperCamelCase (      __lowerCamelCase		:			Any					)   ->  int:







     '''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 __UpperCamelCase (      )   ->  Any:







     '''simple docstring'''






     _a            =		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"					)
     _a            =		parser.parse_args()

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


if __name__ == "__main__":
   main()





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__					  =		False





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






     def 			a_       (      self		)							->     Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Dict:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase		)

          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		generator.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def 			a_       (      self		)							->     int:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		"cyberpunk 2077"
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt=__UpperCamelCase  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"  ,				).images

          _a            =		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a            =		np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2

