



"""simple docstring"""







import argparse
import os

class 			_a     :
   __magic_name__    				=    """block_{0}.dat"""



   def __init__( self       :							Any      ,  _lowercase       :							Tuple		)       ->				int:
         snake_case      : Optional[Any]							=   filename
         snake_case      : Optional[Any]							=   []



   def 							__lowercase				( self       :							Tuple      ,  _lowercase       :							List[str]      ,  _lowercase       :							Optional[int]		)       ->				Optional[Any]:
         snake_case      : Union[str, Any]							=   self.BLOCK_FILENAME_FORMAT.format(_lowercase		)
         with open(_lowercase      ,  "w"		) as file:
               file.write(_lowercase		)
         self.block_filenames.append(_lowercase		)



   def 							__lowercase				( self       :							Dict		)       ->				str:
         return self.block_filenames



   def 							__lowercase				( self       :							Optional[int]      ,  _lowercase       :							Tuple      ,  _lowercase       :							Optional[int]=None		)       ->				Tuple:
         snake_case      : Dict							=   0
         with open(self.filename		) as file:
               while True:
                     snake_case      : Tuple							=   file.readlines(_lowercase		)

                     if lines == []:
                           break

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

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



   def 							__lowercase				( self       :							List[str]		)       ->				List[str]:
         map(os.remove      ,  self.block_filenames		)



class 			_a     :



   def 							__lowercase				( self       :							Tuple      ,  _lowercase       :							str		)       ->				str:
         snake_case      : List[str]							=   -1
         snake_case      : str							=   None

         for i in range(len(_lowercase		)		):
               if min_str is None or choices[i] < min_str:
                     snake_case      : Tuple							=   i

         return min_index



class 			_a     :



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



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



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

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

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

         return True



   def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							Union[str, Any]		)       ->				int:
         snake_case      : Tuple							=   self.buffers[index]
         snake_case      : List[str]							=   None

         return value



class 			_a     :



   def __init__( self       :							Any      ,  _lowercase       :							str		)       ->				List[Any]:
         snake_case      : Dict							=   merge_strategy



   def 							__lowercase				( self       :							int      ,  _lowercase       :							Union[str, Any]      ,  _lowercase       :							Union[str, Any]      ,  _lowercase       :							Any		)       ->				Tuple:
         snake_case      : Dict							=   FilesArray(self.get_file_handles(_lowercase      ,  _lowercase		)		)
         with open(_lowercase      ,  "w"      ,  _lowercase		) as outfile:
               while buffers.refresh():
                     snake_case      : Optional[Any]							=   self.merge_strategy.select(buffers.get_dict()		)
                     outfile.write(buffers.unshift(_lowercase		)		)



   def 							__lowercase				( self       :							List[str]      ,  _lowercase       :							Any      ,  _lowercase       :							Tuple		)       ->				int:
         snake_case      : Tuple							=   {}

         for i in range(len(_lowercase		)		):
               snake_case      : List[str]							=   open(filenames[i]      ,  "r"      ,  _lowercase		)  # noqa: UP015

         return files



class 			_a     :



   def __init__( self       :							Dict      ,  _lowercase       :							Union[str, Any]		)       ->				Optional[int]:
         snake_case      : str							=   block_size



   def 							__lowercase				( self       :							str      ,  _lowercase       :							Any      ,  _lowercase       :							List[Any]=None		)       ->				str:
         snake_case      : Optional[Any]							=   self.get_number_blocks(_lowercase      ,  self.block_size		)
         snake_case      : Optional[int]							=   FileSplitter(_lowercase		)
         splitter.split(self.block_size      ,  _lowercase		)

         snake_case      : Optional[Any]							=   FileMerger(NWayMerge()		)
         snake_case      : int							=   self.block_size / (num_blocks + 1)
         merger.merge(splitter.get_block_filenames()      ,  filename + ".out"      ,  _lowercase		)

         splitter.cleanup()



   def 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							str      ,  _lowercase       :							Tuple		)       ->				Tuple:
         return (os.stat(_lowercase		).st_size / block_size) + 1



def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Union[str, Any]   ):
      """simple docstring"""






      if string[-1].lower() == "k":
            return int(string[:-1]   ) * 1_0_2_4
      elif string[-1].lower() == "m":
            return int(string[:-1]   ) * 1_0_2_4 * 1_0_2_4
      elif string[-1].lower() == "g":
            return int(string[:-1]   ) * 1_0_2_4 * 1_0_2_4 * 1_0_2_4
      else:
            return int(lowerCamelCase_   )

def    __SCREAMING_SNAKE_CASE		( ):
      """simple docstring"""






      snake_case      : 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"   )
      snake_case      : Optional[int]							=   parser.parse_args()

      snake_case      : List[str]							=   ExternalSort(parse_memory(args.mem   )   )
      sorter.sort(args.filename[0]   )


if __name__ == "__main__":
   main()








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A    =	{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'GPTJForCausalLM',
			    'GPTJForQuestionAnswering',
			    'GPTJForSequenceClassification',
			    'GPTJModel',
			    'GPTJPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TFGPTJForCausalLM',
			    'TFGPTJForQuestionAnswering',
			    'TFGPTJForSequenceClassification',
			    'TFGPTJModel',
			    'TFGPTJPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'FlaxGPTJForCausalLM',
			    'FlaxGPTJModel',
			    'FlaxGPTJPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gptj import (
						    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTJForCausalLM,
						    GPTJForQuestionAnswering,
						    GPTJForSequenceClassification,
						    GPTJModel,
						    GPTJPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    TFGPTJPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



