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,
)


lowerCAmelCase_ : Tuple						  =      logging.getLogger(__name__)





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
   lowerCamelCase_    					=field(
       default=_UpperCAmelCase ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
   lowerCamelCase_    					=field(
       default=_UpperCAmelCase ,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
   lowerCamelCase_    					=field(
       default=_UpperCAmelCase ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,				)
   lowerCamelCase_    					=field(default=_UpperCAmelCase ,				metadata={'help': 'Whether tp freeze the encoder.'} )
   lowerCamelCase_    					=field(default=_UpperCAmelCase ,				metadata={'help': 'Whether  to freeze the embeddings.'} )





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(
       metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
   lowerCamelCase_    					=field(
       default='summarization' ,				metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} ,				)
   lowerCamelCase_    					=field(
       default=1_0_2_4 ,				metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=1_2_8 ,				metadata={
           'help': (
               'The maximum total sequence length for target text after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=1_4_2 ,				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``.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=1_4_2 ,				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.'
           )
       } ,				)
   lowerCamelCase_    					=field(default=-1 ,				metadata={'help': '# training examples. -1 means use all.'} )
   lowerCamelCase_    					=field(default=-1 ,				metadata={'help': '# validation examples. -1 means use all.'} )
   lowerCamelCase_    					=field(default=-1 ,				metadata={'help': '# test examples. -1 means use all.'} )
   lowerCamelCase_    					=field(default=_UpperCAmelCase ,				metadata={'help': 'Source language id for translation.'} )
   lowerCamelCase_    					=field(default=_UpperCAmelCase ,				metadata={'help': 'Target language id for translation.'} )
   lowerCamelCase_    					=field(default=_UpperCAmelCase ,				metadata={'help': '# num_beams to use for evaluation.'} )
   lowerCamelCase_    					=field(
       default=_UpperCAmelCase ,				metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} ,				)




def __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						str				)	->							Union[str, Any]:
      '''simple docstring'''


      logger.info(f'***** {split} metrics *****'				)
      for key in sorted(metrics.keys()				):
            logger.info(f'  {key} = {metrics[key]}'				)
      save_json(_A ,		os.path.join(_A ,		f'{split}_results.json'				)				)


def __a (	)	->							Tuple:
      '''simple docstring'''


      lowercase_     =      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.
            lowercase_ ,							lowercase_ ,							lowercase_     =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
            lowercase_ ,							lowercase_ ,							lowercase_     =      parser.parse_args_into_dataclasses()

      check_output_dir(_A				)

      # 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" ,		_A				)

      # 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.

      lowercase_     =      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 ,		)

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

      lowercase_     =      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 ,		)
      lowercase_     =      AutoModelForSeqaSeqLM.from_pretrained(
          model_args.model_name_or_path ,		from_tf=".ckpt" in model_args.model_name_or_path ,		config=_A ,		cache_dir=model_args.cache_dir ,		)

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

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

      # set decoder_start_token_id for MBart
      if model.config.decoder_start_token_id is None and isinstance(_A ,		(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(_A ,		_A				):
                  lowercase_     =      tokenizer.lang_code_to_id[data_args.tgt_lang]
            else:
                  lowercase_     =      tokenizer.convert_tokens_to_ids(data_args.tgt_lang				)

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

      lowercase_     =      SeqaSeqDataset

      # Get datasets
      lowercase_     =      (
          dataset_class(
              _A ,		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
      )
      lowercase_     =      (
          dataset_class(
              _A ,		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
      )
      lowercase_     =      (
          dataset_class(
              _A ,		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
      lowercase_     =      (
          build_compute_metrics_fn(data_args.task ,		_A				) if training_args.predict_with_generate else None
      )
      lowercase_     =      SeqaSeqTrainer(
          model=_A ,		args=_A ,		data_args=_A ,		train_dataset=_A ,		eval_dataset=_A ,		data_collator=SeqaSeqDataCollator(
              _A ,		_A ,		model.config.decoder_start_token_id ,		training_args.tpu_num_cores				) ,		compute_metrics=_A ,		tokenizer=_A ,		)

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

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

            trainer.save_model()  # this also saves the tokenizer

            if trainer.is_world_process_zero():
                  handle_metrics("train" ,		_A ,		training_args.output_dir				)
                  all_metrics.update(_A				)

                  # 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 ***"				)

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

            if trainer.is_world_process_zero():
                  handle_metrics("val" ,		_A ,		training_args.output_dir				)
                  all_metrics.update(_A				)

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

            lowercase_     =      trainer.predict(test_dataset=_A ,		metric_key_prefix="test"				)
            lowercase_     =      test_output.metrics
            lowercase_     =      data_args.n_test

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

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

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

      return all_metrics


def __a (	__lowerCamelCase			:						int				)	->							List[str]:
      '''simple docstring'''


      main()


if __name__ == "__main__":
       main()







'''simple docstring'''

def __a (	__lowerCamelCase			:						list[int] ,		__lowerCamelCase			:						int				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      lowercase_     =      [[False] * (required_sum + 1) for _ in range(arr_len + 1				)]

      # for each arr value, a sum of zero(0) can be formed by not taking any element
      # hence True/1
      for i in range(arr_len + 1				):
            lowercase_     =      True

      # sum is not zero and set is empty then false
      for i in range(1 ,		required_sum + 1				):
            lowercase_     =      False

      for i in range(1 ,		arr_len + 1				):
            for j in range(1 ,		required_sum + 1				):
                  if arr[i - 1] > j:
                        lowercase_     =      subset[i - 1][j]
                  if arr[i - 1] <= j:
                        lowercase_     =      subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

      return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()






