




'''simple docstring'''






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

class 	SCREAMING_SNAKE_CASE_		:
 __snake_case						:  List[Any]       =   "block_{0}.dat"



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   filename
        lowercase	:		int    =   []



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Tuple    =   self.BLOCK_FILENAME_FORMAT.format(A			)
        with open(A ,		'''w'''			) as file:
               file.write(A			)
        self.block_filenames.append(A			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self.block_filenames



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A=None			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   0
        with open(self.filename			) as file:
               while True:
                      lowercase	:		int    =   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  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        map(os.remove ,		self.block_filenames			)






class 	SCREAMING_SNAKE_CASE_		:



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   -1
        lowercase	:		Optional[int]    =   None

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

        return min_index






class 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   files
        lowercase	:		Tuple    =   set()
        lowercase	:		Optional[int]    =   len(A			)
        lowercase	:		List[str]    =   {i: None for i in range(self.num_buffers			)}



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        for i in range(self.num_buffers			):
               if self.buffers[i] is None and i not in self.empty:
                      lowercase	:		int    =   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  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.buffers[index]
        lowercase	:		Union[str, Any]    =   None

        return value






class 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Tuple    =   merge_strategy



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		str    =   FilesArray(self.get_file_handles(A ,		A			)			)
        with open(A ,		'''w''' ,		A			) as outfile:
               while buffers.refresh():
                      lowercase	:		Optional[int]    =   self.merge_strategy.select(buffers.get_dict()			)
                      outfile.write(buffers.unshift(A			)			)





 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Tuple    =   {}

        for i in range(len(A			)			):
               lowercase	:		List[Any]    =   open(filenames[i] ,		'''r''' ,		A			)  # noqa: UP015

        return files






class 	SCREAMING_SNAKE_CASE_		:



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		Tuple    =   block_size



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A=None			):






        '''simple docstring'''




        lowercase	:		int    =   self.get_number_blocks(A ,		self.block_size			)
        lowercase	:		Tuple    =   FileSplitter(A			)
        splitter.split(self.block_size ,		A			)

        lowercase	:		str    =   FileMerger(NWayMerge()			)
        lowercase	:		Tuple    =   self.block_size / (num_blocks + 1)
        merger.merge(splitter.get_block_filenames() ,		filename + '''.out''' ,		A			)

        splitter.cleanup()





 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A			):






        '''simple docstring'''




        return (os.stat(A			).st_size / block_size) + 1






def 		a_(			lowerCamelCase   )					->List[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(lowerCamelCase   )




def 		a_(			)					->int:






       '''simple docstring'''




       lowercase	:		Optional[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'''   )
       lowercase	:		Optional[int]    =   parser.parse_args()

       lowercase	:		Union[str, Any]    =   ExternalSort(parse_memory(args.mem   )   )
       sorter.sort(args.filename[0]   )


if __name__ == "__main__":
      main()










'''simple docstring'''






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
      from .testing_utils import (
          floats_tensor,
          load_hf_numpy,
          load_image,
          load_numpy,
          load_pt,
          nightly,
          parse_flag_from_env,
          print_tensor_test,
          require_torch_a,
          require_torch_gpu,
          skip_mps,
          slow,
          torch_all_close,
          torch_device,
      )
      from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		List[str]    =   f"""This example requires a minimum version of {min_version},"""
              error_message += f""" but the version found is {__version__}.\n"""
              raise ImportError(lowerCamelCase   )




