




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





class  __lowerCamelCase       :




    """simple docstring"""

    snake_case__					=      "block_{0}.dat"


    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Dict   )      ->       Dict:
         lowerCAmelCase__		= filename
         lowerCAmelCase__		= []


    def    a      (       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     Dict   )      ->       Optional[int]:
         lowerCAmelCase__		= self.BLOCK_FILENAME_FORMAT.format(SCREAMING_SNAKE_CASE__   )
         with open(SCREAMING_SNAKE_CASE__   ,	"w"   ) as file:
              file.write(SCREAMING_SNAKE_CASE__   )
         self.block_filenames.append(SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   )      ->       Optional[Any]:
         return self.block_filenames


    def    a      (       self     :     str   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     List[Any]=None   )      ->       Union[str, Any]:
         lowerCAmelCase__		= 0
         with open(self.filename   ) as file:
              while True:
                   lowerCAmelCase__		= file.readlines(SCREAMING_SNAKE_CASE__   )

                   if lines == []:
                        break

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

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


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






class  __lowerCamelCase       :




    """simple docstring"""

    def    a      (       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= -1
         lowerCAmelCase__		= None

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

         return min_index






class  __lowerCamelCase       :




    """simple docstring"""

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


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


    def    a      (       self     :     str   )      ->       List[Any]:
         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(SCREAMING_SNAKE_CASE__   )
                        self.files[i].close()

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

         return True


    def    a      (       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       int:
         lowerCAmelCase__		= self.buffers[index]
         lowerCAmelCase__		= None

         return value






class  __lowerCamelCase       :




    """simple docstring"""

    def __init__(       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= merge_strategy


    def    a      (       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     int   ,	SCREAMING_SNAKE_CASE__     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     Dict   )      ->       Any:
         lowerCAmelCase__		= FilesArray(self.get_file_handles(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   )   )
         with open(SCREAMING_SNAKE_CASE__   ,	"w"   ,	SCREAMING_SNAKE_CASE__   ) as outfile:
              while buffers.refresh():
                   lowerCAmelCase__		= self.merge_strategy.select(buffers.get_dict()   )
                   outfile.write(buffers.unshift(SCREAMING_SNAKE_CASE__   )   )


    def    a      (       self     :     str   ,	SCREAMING_SNAKE_CASE__     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     str   )      ->       List[Any]:
         lowerCAmelCase__		= {}

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

         return files






class  __lowerCamelCase       :




    """simple docstring"""

    def __init__(       self     :     Tuple   ,	SCREAMING_SNAKE_CASE__     :     List[str]   )      ->       Union[str, Any]:
         lowerCAmelCase__		= block_size


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     str=None   )      ->       str:
         lowerCAmelCase__		= self.get_number_blocks(SCREAMING_SNAKE_CASE__   ,	self.block_size   )
         lowerCAmelCase__		= FileSplitter(SCREAMING_SNAKE_CASE__   )
         splitter.split(self.block_size   ,	SCREAMING_SNAKE_CASE__   )

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

         splitter.cleanup()


    def    a      (       self     :     List[str]   ,	SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   )      ->       Dict:
         return (os.stat(SCREAMING_SNAKE_CASE__   ).st_size / block_size) + 1






def 							_A   (						lowerCAmelCase_     :					List[str]			):
     """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   (						):
     """simple docstring"""

     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 flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					int			):
     """simple docstring"""

     lowerCAmelCase__		= FNetConfig.from_json_file(lowerCAmelCase_			)
     print(F'Building PyTorch model from configuration: {config}'			)
     lowerCAmelCase__		= FNetForPreTraining(lowerCAmelCase_			)

     lowerCAmelCase__		= restore_checkpoint(lowerCAmelCase_   ,				lowerCAmelCase_			)
     lowerCAmelCase__		= checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     lowerCAmelCase__		= fnet_pretraining_model.state_dict()

     lowerCAmelCase__		= state_dict["fnet.embeddings.position_ids"]
     lowerCAmelCase__		= {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]			)

     # Encoder Layers
     for layer in range(config.num_hidden_layers			):
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'encoder_{layer}']["mixing_layer_norm"]["scale"]			)
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'encoder_{layer}']["mixing_layer_norm"]["bias"]			)

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["intermediate"]["kernel"]			).T
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["intermediate"]["bias"]			)

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["output"]["kernel"]			).T
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'feed_forward_{layer}']["output"]["bias"]			)

          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'encoder_{layer}']["output_layer_norm"]["scale"]			)
          lowerCAmelCase__		= torch.tensor(
              pretrained_model_params["encoder"][F'encoder_{layer}']["output_layer_norm"]["bias"]			)

     # Pooler Layers
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]			)

     # Masked LM Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]			).T
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]			)
     lowerCAmelCase__		= torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]			)

     # Seq Relationship Layers
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]			)
     lowerCAmelCase__		= torch.tensor(
         pretrained_model_params["classification"]["output_bias"]			)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase_			)

     # Save PreTrained
     print(F'Saving pretrained model to {save_path}'			)
     fnet_pretraining_model.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--flax_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--fnet_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained FNet model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument('--save_path', default=None, type=str, required=True, help='Path to the output model.')
     UpperCamelCase								=     parser.parse_args()
     convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)
