


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets

class       __magic_name__	(     SCREAMING_SNAKE_CASE_):


     @slow
     @require_torch
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''							,	'''prajjwal1/bert-tiny'''			)
      UpperCamelCase__					:    Union[str, Any]          =   BertTokenizer.from_pretrained('''bert-base-uncased'''			)

      UpperCamelCase__					:    Optional[int]          =   bertabert.config.encoder.vocab_size
      UpperCamelCase__					:    str          =   tokenizer.sep_token_id
      UpperCamelCase__					:    Any          =   tokenizer.cls_token_id
      UpperCamelCase__					:    Optional[int]          =   128

      UpperCamelCase__					:    int          =   datasets.load_dataset('''cnn_dailymail'''							,	'''3.0.0'''							,	split='''train[:1%]'''			)
      UpperCamelCase__					:    Any          =   datasets.load_dataset('''cnn_dailymail'''							,	'''3.0.0'''							,	split='''validation[:1%]'''			)

      UpperCamelCase__					:    Tuple          =   train_dataset.select(range(32			)			)
      UpperCamelCase__					:    Any          =   val_dataset.select(range(16			)			)

      UpperCamelCase__					:    List[Any]          =   4

      def _map_to_encoder_decoder_inputs(lowerCamelCase__							:     str			):
       # Tokenizer will automatically set [BOS] <text> [EOS]
       UpperCamelCase__					:    Dict          =   tokenizer(batch['''article''']							,	padding='''max_length'''							,	truncation=UpperCamelCase__							,	max_length=512			)
       UpperCamelCase__					:    Any          =   tokenizer(batch['''highlights''']							,	padding='''max_length'''							,	truncation=UpperCamelCase__							,	max_length=128			)
       UpperCamelCase__					:    Optional[Any]          =   inputs.input_ids
       UpperCamelCase__					:    List[Any]          =   inputs.attention_mask

       UpperCamelCase__					:    Optional[Any]          =   outputs.input_ids
       UpperCamelCase__					:    List[Any]          =   outputs.input_ids.copy()
       UpperCamelCase__					:    Dict          =   [
           [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
       ]
       UpperCamelCase__					:    List[Any]          =   outputs.attention_mask

       assert all(len(UpperCamelCase__			) == 512 for x in inputs.input_ids			)
       assert all(len(UpperCamelCase__			) == 128 for x in outputs.input_ids			)

       return batch

      def _compute_metrics(lowerCamelCase__							:     Union[str, Any]			):
       UpperCamelCase__					:    int          =   pred.label_ids
       UpperCamelCase__					:    Dict          =   pred.predictions

       # all unnecessary tokens are removed
       UpperCamelCase__					:    List[str]          =   tokenizer.batch_decode(UpperCamelCase__							,	skip_special_tokens=UpperCamelCase__			)
       UpperCamelCase__					:    Dict          =   tokenizer.batch_decode(UpperCamelCase__							,	skip_special_tokens=UpperCamelCase__			)

       UpperCamelCase__					:    Dict          =   sum([int(pred_str[i] == label_str[i]			) for i in range(len(UpperCamelCase__			)			)]			) / len(UpperCamelCase__			)

       return {"accuracy": accuracy}

      # map train dataset
      UpperCamelCase__					:    Optional[Any]          =   train_dataset.map(
          _map_to_encoder_decoder_inputs							,	batched=UpperCamelCase__							,	batch_size=UpperCamelCase__							,	remove_columns=['''article''', '''highlights''']							,	)
      train_dataset.set_format(
          type='''torch'''							,	columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']							,	)

      # same for validation dataset
      UpperCamelCase__					:    Optional[int]          =   val_dataset.map(
          _map_to_encoder_decoder_inputs							,	batched=UpperCamelCase__							,	batch_size=UpperCamelCase__							,	remove_columns=['''article''', '''highlights''']							,	)
      val_dataset.set_format(
          type='''torch'''							,	columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']							,	)

      UpperCamelCase__					:    Any          =   self.get_auto_remove_tmp_dir()

      UpperCamelCase__					:    Optional[Any]          =   SeqaSeqTrainingArguments(
          output_dir=UpperCamelCase__							,	per_device_train_batch_size=UpperCamelCase__							,	per_device_eval_batch_size=UpperCamelCase__							,	predict_with_generate=UpperCamelCase__							,	evaluation_strategy='''steps'''							,	do_train=UpperCamelCase__							,	do_eval=UpperCamelCase__							,	warmup_steps=0							,	eval_steps=2							,	logging_steps=2							,	)

      # instantiate trainer
      UpperCamelCase__					:    List[str]          =   SeqaSeqTrainer(
          model=UpperCamelCase__							,	args=UpperCamelCase__							,	compute_metrics=_compute_metrics							,	train_dataset=UpperCamelCase__							,	eval_dataset=UpperCamelCase__							,	tokenizer=UpperCamelCase__							,	)

      # start training
      trainer.train()






import math
import os
import sys

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   ''''''
 try:
  with open(SCREAMING_SNAKE_CASE    , '''rb'''     ) as binary_file:
   UpperCamelCase__					:    int          =   binary_file.read()
  for dat in data:
   UpperCamelCase__					:    Dict          =   F"{dat:08b}"
   result += curr_byte
  return result
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict[str, str]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 lexicon.pop(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    str          =   last_match_id

 if math.loga(SCREAMING_SNAKE_CASE     ).is_integer():
  for curr_key in lexicon:
   UpperCamelCase__					:    Any          =   '''0''' + lexicon[curr_key]

 UpperCamelCase__					:    Dict          =   bin(SCREAMING_SNAKE_CASE     )[2:]

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   {'''0''': '''0''', '''1''': '''1'''}
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   '''''', ''''''
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )

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

  UpperCamelCase__					:    List[str]          =   lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
  index += 1
  UpperCamelCase__					:    Dict          =   ''''''

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

 if curr_string != "":
  UpperCamelCase__					:    Union[str, Any]          =   lexicon[curr_string]
  result += last_match_id

 return result

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   os.path.getsize(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   bin(SCREAMING_SNAKE_CASE     )[2:]
 UpperCamelCase__					:    Tuple          =   len(SCREAMING_SNAKE_CASE     )

 return "0" * (length_length - 1) + file_length_binary + compressed

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   8
 try:
  with open(SCREAMING_SNAKE_CASE    , '''wb'''     ) as opened_file:
   UpperCamelCase__					:    Optional[Any]          =   [
       to_write[i : i + byte_length]
       for i in range(0    , len(SCREAMING_SNAKE_CASE     )    , SCREAMING_SNAKE_CASE     )
   ]

   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(SCREAMING_SNAKE_CASE    , 2     ).to_bytes(1    , byteorder='''big'''     )     )
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   read_file_binary(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   compress_data(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   add_file_length(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 write_file_binary(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


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


