


"""simple docstring"""






import math
import os
import sys
def       A_   (   _lowerCAmelCase     :					str       ):






     """simple docstring"""

     _a    	=				''''''
     try:
          with open(_lowerCAmelCase,     '''rb'''       ) as binary_file:
               _a    	=				binary_file.read()
          for dat in data:
               _a    	=				f'{dat:08b}'
               result += curr_byte
          return result
     except OSError:
          print('''File not accessible'''       )
          sys.exit()
def       A_   (   _lowerCAmelCase     :					dict[str, str],     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					int,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     lexicon.pop(_lowerCAmelCase       )
     _a    	=				last_match_id

     if math.loga(_lowerCAmelCase       ).is_integer():
          for curr_key in lexicon:
               _a    	=				'''0''' + lexicon[curr_key]

     _a    	=				bin(_lowerCAmelCase       )[2:]
def       A_   (   _lowerCAmelCase     :					str       ):






     """simple docstring"""

     _a    	=				{'''0''': '''0''', '''1''': '''1'''}
     _a	,      _a    	=				'''''', ''''''
     _a    	=				len(_lowerCAmelCase       )

     for i in range(len(_lowerCAmelCase       )       ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          _a    	=				lexicon[curr_string]
          result += last_match_id
          add_key_to_lexicon(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )
          index += 1
          _a    	=				''''''

     while curr_string != "" and curr_string not in lexicon:
          curr_string += "0"

     if curr_string != "":
          _a    	=				lexicon[curr_string]
          result += last_match_id

     return result
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     _a    	=				os.path.getsize(_lowerCAmelCase       )
     _a    	=				bin(_lowerCAmelCase       )[2:]
     _a    	=				len(_lowerCAmelCase       )

     return "0" * (length_length - 1) + file_length_binary + compressed
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     _a    	=				8
     try:
          with open(_lowerCAmelCase,     '''wb'''       ) as opened_file:
               _a    	=				[
                   to_write[i : i + byte_length]
                   for i in range(0,     len(_lowerCAmelCase       ),     _lowerCAmelCase       )
               ]

               if len(result_byte_array[-1]       ) % byte_length == 0:
                    result_byte_array.append('''10000000'''       )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1]       ) - 1
                    )

               for elem in result_byte_array:
                    opened_file.write(int(_lowerCAmelCase,     2       ).to_bytes(1,     byteorder='''big'''       )       )
     except OSError:
          print('''File not accessible'''       )
          sys.exit()
def       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     _a    	=				read_file_binary(_lowerCAmelCase       )
     _a    	=				compress_data(_lowerCAmelCase       )
     _a    	=				add_file_length(_lowerCAmelCase,     _lowerCAmelCase       )
     write_file_binary(_lowerCAmelCase,     _lowerCAmelCase       )


if __name__ == "__main__":
   compress(sys.argv[1], sys.argv[2])



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
   )
   parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument(
       '''--hf_config''',
       default='''facebook/mbart-large-cc25''',
       type=str,
       help='''Which huggingface architecture to use: mbart-large''',
   )
   parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)