





"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase  					=     logging.get_logger(__name__)

_lowerCamelCase  					=     {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Optional[int]			,				lowercase_       :							Any			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							Dict		):

 '''simple docstring'''






 for attribute in key.split('''.'''		):
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	getattr(lowercase_			,				lowercase_		)

 if weight_type is not None:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	getattr(lowercase_			,				lowercase_		).shape
 else:
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_pointer.shape

 assert hf_shape == value.shape, (
     F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
     F''' {value.shape} for {full_name}'''
 )

 if weight_type == "weight":
  __SCREAMING_SNAKE_CASE			:      str 							=	value
 elif weight_type == "weight_g":
  __SCREAMING_SNAKE_CASE			:      Dict 							=	value
 elif weight_type == "weight_v":
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	value
 elif weight_type == "bias":
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	value
 else:
  __SCREAMING_SNAKE_CASE			:      Any 							=	value

 logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''		)




def 		lowerCAmelCase_     (  lowercase_       :							List[Any]			,				lowercase_       :							Optional[int]			,				lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[str] 							=	[]
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	fairseq_model.state_dict()

 __SCREAMING_SNAKE_CASE			:      str 							=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False
  if "conv_layers" in name:
   load_conv_layer(
       lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				hf_model.config.feat_extract_norm == '''group'''			,				)
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	True
  else:
   for key, mapped_key in MAPPING.items():
    __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	'''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

    if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
     __SCREAMING_SNAKE_CASE			:      List[Any] 							=	True
     if "*" in mapped_key:
      __SCREAMING_SNAKE_CASE			:      str 							=	name.split(lowercase_		)[0].split('''.'''		)[-2]
      if not layer_index.isnumeric():
       continue
      __SCREAMING_SNAKE_CASE			:      str 							=	mapped_key.replace('''*'''			,				lowercase_		)
     if "weight_g" in name:
      __SCREAMING_SNAKE_CASE			:      int 							=	'''weight_g'''
     elif "weight_v" in name:
      __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''weight_v'''
     elif "weight" in name:
      __SCREAMING_SNAKE_CASE			:      Any 							=	'''weight'''
     elif "bias" in name:
      __SCREAMING_SNAKE_CASE			:      List[Any] 							=	'''bias'''
     else:
      __SCREAMING_SNAKE_CASE			:      str 							=	None
     set_recursively(lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_		)
    continue
  if not is_used:
   unused_weights.append(lowercase_		)

 logger.warning(F'''Unused weights: {unused_weights}'''		)




def 		lowerCAmelCase_     (  lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							List[str]			,				lowercase_       :							Optional[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	full_name.split('''conv_layers.'''		)[-1]
 __SCREAMING_SNAKE_CASE			:      str 							=	name.split('''.'''		)
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	int(items[0]		)
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	int(items[1]		)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
   )
   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
   )
   __SCREAMING_SNAKE_CASE			:      str 							=	value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
 else:
  unused_weights.append(lowercase_		)




def 		lowerCAmelCase_     (  lowercase_       :							int			,				lowercase_       :							int		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      int 							=	SEWDConfig()
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      str 							=	model.wav_encoder.wav_model.cfg
 else:
  __SCREAMING_SNAKE_CASE			:      Any 							=	model.cfg

 __SCREAMING_SNAKE_CASE			:      Any 							=	fs_config.conv_bias
 __SCREAMING_SNAKE_CASE			:      str 							=	eval(fs_config.conv_feature_layers		)
 __SCREAMING_SNAKE_CASE			:      int 							=	[x[0] for x in conv_layers]
 __SCREAMING_SNAKE_CASE			:      str 							=	[x[1] for x in conv_layers]
 __SCREAMING_SNAKE_CASE			:      str 							=	[x[2] for x in conv_layers]
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	'''gelu'''
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	0.0
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	fs_config.activation_fn.name
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	fs_config.encoder_embed_dim
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	0.02
 __SCREAMING_SNAKE_CASE			:      int 							=	fs_config.encoder_ffn_embed_dim
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	1E-5
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	fs_config.encoder_layerdrop
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	fs_config.encoder_attention_heads
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	fs_config.conv_pos_groups
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	fs_config.conv_pos
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	len(lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	fs_config.encoder_layers
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	fs_config.squeeze_factor
 # DeBERTa-specific parameters:
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	fs_config.max_position_embeddings
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	fs_config.position_buckets
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	fs_config.share_att_key
 __SCREAMING_SNAKE_CASE			:      Any 							=	fs_config.relative_attention
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	fs_config.position_biased_input
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	tuple(fs_config.pos_att_type.split('''|'''		)		)
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	fs_config.norm_rel_ebd

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      int 							=	model.cfg
  __SCREAMING_SNAKE_CASE			:      Dict 							=	fs_config.final_dropout
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	fs_config.layerdrop
 __SCREAMING_SNAKE_CASE			:      int 							=	fs_config.activation_dropout
 __SCREAMING_SNAKE_CASE			:      int 							=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 __SCREAMING_SNAKE_CASE			:      Any 							=	fs_config.attention_dropout
 __SCREAMING_SNAKE_CASE			:      Any 							=	fs_config.dropout_input
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	fs_config.dropout
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	fs_config.mask_channel_length
 __SCREAMING_SNAKE_CASE			:      Dict 							=	fs_config.mask_channel_prob
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	fs_config.mask_length
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	fs_config.mask_prob

 __SCREAMING_SNAKE_CASE			:      Dict 							=	'''Wav2Vec2FeatureExtractor'''
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	'''Wav2Vec2CTCTokenizer'''

 return config




@torch.no_grad()
def 		lowerCAmelCase_     (  lowercase_       :							Dict			,				lowercase_       :							Optional[Any]			,				lowercase_       :							List[Any]=None			,				lowercase_       :							Tuple=None			,				lowercase_       :							str=True		):

 '''simple docstring'''






 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]			,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		)}		)
 else:
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

 if config_path is not None:
  __SCREAMING_SNAKE_CASE			:      Any 							=	SEWDConfig.from_pretrained(lowercase_		)
 else:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	convert_config(model[0]			,				lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	model[0].eval()

 __SCREAMING_SNAKE_CASE			:      Any 							=	True if config.feat_extract_norm == '''layer''' else False
 __SCREAMING_SNAKE_CASE			:      Dict 							=	WavaVecaFeatureExtractor(
     feature_size=1			,				sampling_rate=1_6000			,				padding_value=0			,				do_normalize=lowercase_			,				return_attention_mask=lowercase_			,				)

 if is_finetuned:
  if dict_path:
   __SCREAMING_SNAKE_CASE			:      Dict 							=	Dictionary.load(lowercase_		)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	target_dict.pad_index
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	target_dict.bos_index
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	target_dict.pad_index
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	target_dict.bos_index
   __SCREAMING_SNAKE_CASE			:      Any 							=	target_dict.eos_index
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	len(target_dict.symbols		)
   __SCREAMING_SNAKE_CASE			:      Dict 							=	os.path.join(lowercase_			,				'''vocab.json'''		)
   if not os.path.isdir(lowercase_		):
    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase_		)		)
    return
   os.makedirs(lowercase_			,				exist_ok=lowercase_		)
   with open(lowercase_			,				'''w'''			,				encoding='''utf-8'''		) as vocab_handle:
    json.dump(target_dict.indices			,				lowercase_		)
   __SCREAMING_SNAKE_CASE			:      Tuple 							=	WavaVecaCTCTokenizer(
       lowercase_			,				unk_token=target_dict.unk_word			,				pad_token=target_dict.pad_word			,				bos_token=target_dict.bos_word			,				eos_token=target_dict.eos_word			,				word_delimiter_token='''|'''			,				do_lower_case=lowercase_			,				)
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	WavaVecaProcessor(feature_extractor=lowercase_			,				tokenizer=lowercase_		)
   processor.save_pretrained(lowercase_		)

  __SCREAMING_SNAKE_CASE			:      List[str] 							=	SEWDForCTC(lowercase_		)
 else:
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	SEWDModel(lowercase_		)
  feature_extractor.save_pretrained(lowercase_		)

 recursively_load_weights(lowercase_			,				lowercase_			,				lowercase_		)

 hf_model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase  					=     argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    _lowerCamelCase  					=     parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )








"""simple docstring"""





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





class       snake_case		:
      lowerCamelCase__      =			'''block_{0}.dat'''

      def __init__(      self       :int						,							_lowerCamelCase       :Dict						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	filename
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	[]

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	self.BLOCK_FILENAME_FORMAT.format(_lowerCamelCase						)
       with open(_lowerCamelCase						,							'''w'''						) as file:
        file.write(_lowerCamelCase						)
       self.block_filenames.append(_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       return self.block_filenames

      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Optional[Any]=None						):
       __SCREAMING_SNAKE_CASE			:      int 							=	0
       with open(self.filename						) as file:
        while True:
         __SCREAMING_SNAKE_CASE			:      Any 							=	file.readlines(_lowerCamelCase						)

         if lines == []:
          break

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

         self.write_block(''''''.join(_lowerCamelCase						)						,							_lowerCamelCase						)
         i += 1



      def 						SCREAMING_SNAKE_CASE_      (      self       :Tuple						):
       map(os.remove						,							self.block_filenames						)







class       snake_case		:

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	-1
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	None

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

       return min_index







class       snake_case		:

      def __init__(      self       :str						,							_lowerCamelCase       :Tuple						):
       __SCREAMING_SNAKE_CASE			:      Any 							=	files
       __SCREAMING_SNAKE_CASE			:      str 							=	set()
       __SCREAMING_SNAKE_CASE			:      Dict 							=	len(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      str 							=	{i: None for i in range(self.num_buffers						)}

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

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

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

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

       return True



      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :List[str]						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.buffers[index]
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	None

       return value







class       snake_case		:

      def __init__(      self       :List[Any]						,							_lowerCamelCase       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	merge_strategy

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :str						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	FilesArray(self.get_file_handles(_lowerCamelCase						,							_lowerCamelCase						)						)
       with open(_lowerCamelCase						,							'''w'''						,							_lowerCamelCase						) as outfile:
        while buffers.refresh():
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.merge_strategy.select(buffers.get_dict()						)
         outfile.write(buffers.unshift(_lowerCamelCase						)						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :int						,							_lowerCamelCase       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{}

       for i in range(len(_lowerCamelCase						)						):
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	open(filenames[i]						,							'''r'''						,							_lowerCamelCase						)  # noqa: UP015

       return files







class       snake_case		:

      def __init__(      self       :Dict						,							_lowerCamelCase       :str						):
       __SCREAMING_SNAKE_CASE			:      int 							=	block_size

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Dict=None						):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.get_number_blocks(_lowerCamelCase						,							self.block_size						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	FileSplitter(_lowerCamelCase						)
       splitter.split(self.block_size						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Any 							=	FileMerger(NWayMerge()						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.block_size / (num_blocks + 1)
       merger.merge(splitter.get_block_filenames()						,							filename + '''.out'''						,							_lowerCamelCase						)

       splitter.cleanup()



      def 						SCREAMING_SNAKE_CASE_      (      self       :Dict						,							_lowerCamelCase       :str						,							_lowerCamelCase       :List[Any]						):
       return (os.stat(_lowerCamelCase						).st_size / block_size) + 1







def 		lowerCAmelCase_     (  lowercase_       :							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(lowercase_		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_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'''		)
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	parser.parse_args()

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


if __name__ == "__main__":
    main()

