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






 '''simple docstring'''






 UpperCAmelCase_	 =					"""block_{0}.dat"""




 def __init__(			self					:  Union[str, Any]			,    lowercase__					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					filename
        lowerCAmelCase__     	=					[]




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  str			,    lowercase__					:  Dict):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.BLOCK_FILENAME_FORMAT.format(lowercase__)
        with open(lowercase__			,    'w') as file:
               file.write(lowercase__)
        self.block_filenames.append(lowercase__)




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        return self.block_filenames




 def 		__snake_case						(			self					:  Union[str, Any]			,    lowercase__					:  List[str]			,    lowercase__					:  int=None):







        '''simple docstring'''



        lowerCAmelCase__     	=					0
        with open(self.filename) as file:
               while True:
                      lowerCAmelCase__     	=					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 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        map(os.remove			,    self.block_filenames)






class 				a_ :






 '''simple docstring'''






 def 		__snake_case						(			self					:  Union[str, Any]			,    lowercase__					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					-1
        lowerCAmelCase__     	=					None

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

        return min_index






class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  Tuple			,    lowercase__					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					files
        lowerCAmelCase__     	=					set()
        lowerCAmelCase__     	=					len(lowercase__)
        lowerCAmelCase__     	=					{i: None for i in range(self.num_buffers)}




 def 		__snake_case						(			self					:  int):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  Dict):







        '''simple docstring'''



        for i in range(self.num_buffers):
               if self.buffers[i] is None and i not in self.empty:
                      lowerCAmelCase__     	=					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 		__snake_case						(			self					:  List[str]			,    lowercase__					:  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.buffers[index]
        lowerCAmelCase__     	=					None

        return value






class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  List[Any]			,    lowercase__					:  str):







        '''simple docstring'''



        lowerCAmelCase__     	=					merge_strategy




 def 		__snake_case						(			self					:  Dict			,    lowercase__					:  Union[str, Any]			,    lowercase__					:  List[str]			,    lowercase__					:  Dict):







        '''simple docstring'''



        lowerCAmelCase__     	=					FilesArray(self.get_file_handles(lowercase__			,    lowercase__))
        with open(lowercase__			,    'w'			,    lowercase__) as outfile:
               while buffers.refresh():
                      lowerCAmelCase__     	=					self.merge_strategy.select(buffers.get_dict())
                      outfile.write(buffers.unshift(lowercase__))




 def 		__snake_case						(			self					:  List[str]			,    lowercase__					:  Dict			,    lowercase__					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					{}

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

        return files






class 				a_ :






 '''simple docstring'''






 def __init__(			self					:  Any			,    lowercase__					:  str):







        '''simple docstring'''



        lowerCAmelCase__     	=					block_size




 def 		__snake_case						(			self					:  str			,    lowercase__					:  List[Any]			,    lowercase__					:  Tuple=None):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.get_number_blocks(lowercase__			,    self.block_size)
        lowerCAmelCase__     	=					FileSplitter(lowercase__)
        splitter.split(self.block_size			,    lowercase__)

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

        splitter.cleanup()




 def 		__snake_case						(			self					:  List[str]			,    lowercase__					:  List[str]			,    lowercase__					:  List[str]):







        '''simple docstring'''



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






def       __lowerCamelCase		(						lowerCAmelCase__	):
       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       __lowerCamelCase		(						):
       lowerCAmelCase__     	=					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'	)
       lowerCAmelCase__     	=					parser.parse_args()

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


if __name__ == "__main__":
    main()







import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  # Save pytorch-model
  _snake_case      : List[Any]														=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _snake_case      : Optional[Any]														=		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
  torch.save(model.state_dict()			,							lowerCAmelCase )
  print(F"""Save configuration file to {pytorch_config_dump_path}""" )
  with open(lowerCAmelCase			,							'w'			,							encoding='utf-8' ) as f:
    f.write(config.to_json_string() )


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--imagegpt_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the TensorFlow checkpoint path.""",
      )
      parser.add_argument(
          """--model_size""",
          default=None,
          type=str,
          required=True,
          help="""Size of the model (can be either 'small', 'medium' or 'large').""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )




