



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase_		 =					logging.getLogger(__name__)


@dataclass
class 	lowerCamelCase__	:



 """simple docstring"""

 a__					: str =  field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
 a__					: Optional[str] =  field(
     default=_A							,  metadata={"help": "Pretrained config name or path if not the same as model_name"})
 a__					: Optional[str] =  field(
     default=_A							,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
 a__					: Optional[str] =  field(
     default=_A							,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,  )
 a__					: bool =  field(default=_A							,  metadata={"help": "Whether tp freeze the encoder."})
 a__					: bool =  field(default=_A							,  metadata={"help": "Whether  to freeze the embeddings."})


@dataclass
class 	lowerCamelCase__	:



 """simple docstring"""

 a__					: str =  field(
     metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."})
 a__					: Optional[str] =  field(
     default="summarization"							,  metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}							,  )
 a__					: Optional[int] =  field(
     default=1024							,  metadata={
         "help": (
             "The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }							,  )
 a__					: Optional[int] =  field(
     default=128							,  metadata={
         "help": (
             "The maximum total sequence length for target text after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }							,  )
 a__					: Optional[int] =  field(
     default=142							,  metadata={
         "help": (
             "The maximum total sequence length for validation target text after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded. "
             "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
             "during ``evaluate`` and ``predict``."
         )
     }							,  )
 a__					: Optional[int] =  field(
     default=142							,  metadata={
         "help": (
             "The maximum total sequence length for test target text after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded."
         )
     }							,  )
 a__					: Optional[int] =  field(default=-1							,  metadata={"help": "# training examples. -1 means use all."})
 a__					: Optional[int] =  field(default=-1							,  metadata={"help": "# validation examples. -1 means use all."})
 a__					: Optional[int] =  field(default=-1							,  metadata={"help": "# test examples. -1 means use all."})
 a__					: Optional[str] =  field(default=_A							,  metadata={"help": "Source language id for translation."})
 a__					: Optional[str] =  field(default=_A							,  metadata={"help": "Target language id for translation."})
 a__					: Optional[int] =  field(default=_A							,  metadata={"help": "# num_beams to use for evaluation."})
 a__					: bool =  field(
     default=_A							,  metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}							,  )

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]	,   _snake_case	:Dict	,   _snake_case	:Optional[Any]				)  ->     Optional[int]:
   logger.info(F'''***** {split} metrics *****'''				)
   for key in sorted(metrics.keys()				):
      logger.info(F'''  {key} = {metrics[key]}'''				)
   save_json(_snake_case	,   os.path.join(_snake_case	,   F'''{split}_results.json'''				)				)

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   _A      =       HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)				)

   if len(sys.argv				) == 2 and sys.argv[1].endswith('''.json'''				):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      _A  ,    _A  ,    _A      =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
   else:
      _A  ,    _A  ,    _A      =       parser.parse_args_into_dataclasses()

   check_output_dir(_snake_case				)

   # Setup logging
   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,   datefmt='''%m/%d/%Y %H:%M:%S'''	,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,   )
   logger.warning(
       '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''	,   training_args.local_rank	,   training_args.device	,   training_args.n_gpu	,   bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED				)	,   training_args.fpaa	,   )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank				):
      transformers.utils.logging.set_verbosity_info()
   logger.info('''Training/evaluation parameters %s'''	,   _snake_case				)

   # Set seed
   set_seed(training_args.seed				)

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   _A      =       AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path	,   cache_dir=model_args.cache_dir	,   )

   _A      =       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
   for p in extra_model_params:
      if getattr(_snake_case	,   _snake_case	,   _snake_case				):
         assert hasattr(_snake_case	,   _snake_case				), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
         setattr(_snake_case	,   _snake_case	,   getattr(_snake_case	,   _snake_case				)				)

   _A      =       AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,   cache_dir=model_args.cache_dir	,   )
   _A      =       AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path	,   from_tf='''.ckpt''' in model_args.model_name_or_path	,   config=_snake_case	,   cache_dir=model_args.cache_dir	,   )

   # use task specific params
   use_task_specific_params(_snake_case	,   data_args.task				)

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      _A      =       model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(_snake_case	,   (MBartTokenizer, MBartTokenizerFast)				):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(_snake_case	,   _snake_case				):
         _A      =       tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         _A      =       tokenizer.convert_tokens_to_ids(data_args.tgt_lang				)

   if model_args.freeze_embeds:
      freeze_embeds(_snake_case				)
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()				)
      assert_all_frozen(model.get_encoder()				)

   _A      =       SeqaSeqDataset

   # Get datasets
   _A      =       (
       dataset_class(
           _snake_case	,   type_path='''train'''	,   data_dir=data_args.data_dir	,   n_obs=data_args.n_train	,   max_target_length=data_args.max_target_length	,   max_source_length=data_args.max_source_length	,   prefix=model.config.prefix or ''''''	,   )
       if training_args.do_train
       else None
   )
   _A      =       (
       dataset_class(
           _snake_case	,   type_path='''val'''	,   data_dir=data_args.data_dir	,   n_obs=data_args.n_val	,   max_target_length=data_args.val_max_target_length	,   max_source_length=data_args.max_source_length	,   prefix=model.config.prefix or ''''''	,   )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   _A      =       (
       dataset_class(
           _snake_case	,   type_path='''test'''	,   data_dir=data_args.data_dir	,   n_obs=data_args.n_test	,   max_target_length=data_args.test_max_target_length	,   max_source_length=data_args.max_source_length	,   prefix=model.config.prefix or ''''''	,   )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   _A      =       (
       build_compute_metrics_fn(data_args.task	,   _snake_case				) if training_args.predict_with_generate else None
   )
   _A      =       SeqaSeqTrainer(
       model=_snake_case	,   args=_snake_case	,   data_args=_snake_case	,   train_dataset=_snake_case	,   eval_dataset=_snake_case	,   data_collator=SeqaSeqDataCollator(
           _snake_case	,   _snake_case	,   model.config.decoder_start_token_id	,   training_args.tpu_num_cores				)	,   compute_metrics=_snake_case	,   tokenizer=_snake_case	,   )

   _A      =       {}
   # Training
   if training_args.do_train:
      logger.info('''*** Train ***'''				)

      _A      =       trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
      _A      =       train_result.metrics
      _A      =       data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics('''train'''	,   _snake_case	,   training_args.output_dir				)
         all_metrics.update(_snake_case				)

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir	,   '''trainer_state.json'''				)				)

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''				)

      _A      =       trainer.evaluate(metric_key_prefix='''val'''				)
      _A      =       data_args.n_val
      _A      =       round(metrics['''val_loss''']	,   4				)

      if trainer.is_world_process_zero():
         handle_metrics('''val'''	,   _snake_case	,   training_args.output_dir				)
         all_metrics.update(_snake_case				)

   if training_args.do_predict:
      logger.info('''*** Predict ***'''				)

      _A      =       trainer.predict(test_dataset=_snake_case	,   metric_key_prefix='''test'''				)
      _A      =       test_output.metrics
      _A      =       data_args.n_test

      if trainer.is_world_process_zero():
         _A      =       round(metrics['''test_loss''']	,   4				)
         handle_metrics('''test'''	,   _snake_case	,   training_args.output_dir				)
         all_metrics.update(_snake_case				)

         if training_args.predict_with_generate:
            _A      =       tokenizer.batch_decode(
                test_output.predictions	,   skip_special_tokens=_snake_case	,   clean_up_tokenization_spaces=_snake_case				)
            _A      =       lmap(str.strip	,   _snake_case				)
            write_txt_file(_snake_case	,   os.path.join(training_args.output_dir	,   '''test_generations.txt'''				)				)

   if trainer.is_world_process_zero():
      save_json(_snake_case	,   os.path.join(training_args.output_dir	,   '''all_results.json'''				)				)

   return all_metrics

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any				)  ->     List[Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()





"""simple docstring"""




import math
import sys




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  ''''''
  try:
    with open(_UpperCamelCase			,					'''rb'''    ) as binary_file:
      _lowercase:      Dict   			=  binary_file.read()
    for dat in data:
      _lowercase:      List[str]   			=  f'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  {'''0''': '''0''', '''1''': '''1'''}
  _lowercase			,					_lowercase:      Optional[int]   			=  '''''', ''''''
  _lowercase:      Dict   			=  len(_UpperCamelCase    )

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

    _lowercase:      Any   			=  lexicon[curr_string]
    result += last_match_id
    _lowercase:      Optional[int]   			=  last_match_id + '''0'''

    if math.loga(_UpperCamelCase    ).is_integer():
      _lowercase:      str   			=  {}
      for curr_key in list(_UpperCamelCase    ):
        _lowercase:      List[Any]   			=  lexicon.pop(_UpperCamelCase    )
      _lowercase:      Tuple   			=  new_lex

    _lowercase:      List[Any]   			=  last_match_id + '''1'''
    index += 1
    _lowercase:      List[Any]   			=  ''''''
  return result




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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

      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[:-1]:
        opened_file.write(int(_UpperCamelCase			,					2    ).to_bytes(1			,					byteorder='''big'''    )    )
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  0
  for letter in data_bits:
    if letter == "1":
      break
    counter += 1

  _lowercase:      List[str]   			=  data_bits[counter:]
  _lowercase:      Union[str, Any]   			=  data_bits[counter + 1 :]
  return data_bits




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  read_file_binary(_UpperCamelCase    )
  _lowercase:      Optional[Any]   			=  remove_prefix(_UpperCamelCase    )
  _lowercase:      Dict   			=  decompress_data(_UpperCamelCase    )
  write_file_binary(_UpperCamelCase			,					_UpperCamelCase    )


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