'''simple docstring'''





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,
)


SCREAMING_SNAKE_CASE_:     str  =logging.getLogger(__name__)




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




@dataclass
class        __A      :
    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=1_024		,	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=UpperCamelCase__		,	metadata={"""help""": """Source language id for translation."""}	)
    a__       :						Optional[str]          =  field(default=UpperCamelCase__		,	metadata={"""help""": """Target language id for translation."""}	)
    a__       :						Optional[int]          =  field(default=UpperCamelCase__		,	metadata={"""help""": """# num_beams to use for evaluation."""}	)
    a__       :						bool          =  field(
        default=UpperCamelCase__		,	metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}		,	)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : Union[str, Any]					,    snake_case_       : List[Any]			)    ->      str:


     '''simple docstring'''




     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 	lowerCAmelCase_				(					)    ->      Dict:


     '''simple docstring'''




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

     UpperCAmelCase_           =   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					,    )

     UpperCAmelCase_           =   ("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_			)			)

     UpperCAmelCase_           =   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					,    )
     UpperCAmelCase_           =   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:
          UpperCAmelCase_           =   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_			):
               UpperCAmelCase_           =   tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               UpperCAmelCase_           =   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()			)

     UpperCAmelCase_           =   SeqaSeqDataset

     # Get datasets
     UpperCAmelCase_           =   (
         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
     )
     UpperCAmelCase_           =   (
         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
     )
     UpperCAmelCase_           =   (
         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
     UpperCAmelCase_           =   (
         build_compute_metrics_fn(data_args.task					,    snake_case_			) if training_args.predict_with_generate else None
     )
     UpperCAmelCase_           =   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_					,    )

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

          UpperCAmelCase_           =   trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
          UpperCAmelCase_           =   train_result.metrics
          UpperCAmelCase_           =   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 ***"			)

          UpperCAmelCase_           =   trainer.evaluate(metric_key_prefix="val"			)
          UpperCAmelCase_           =   data_args.n_val
          UpperCAmelCase_           =   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 ***"			)

          UpperCAmelCase_           =   trainer.predict(test_dataset=snake_case_					,    metric_key_prefix="test"			)
          UpperCAmelCase_           =   test_output.metrics
          UpperCAmelCase_           =   data_args.n_test

          if trainer.is_world_process_zero():
               UpperCAmelCase_           =   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:
                    UpperCAmelCase_           =   tokenizer.batch_decode(
                        test_output.predictions					,    skip_special_tokens=snake_case_					,    clean_up_tokenization_spaces=snake_case_			)
                    UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : str			)    ->      List[str]:


     '''simple docstring'''




     main()


if __name__ == "__main__":
    main()





'''simple docstring'''





import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_:     str  =logging.getLogger(__name__)




@dataclass(frozen=UpperCamelCase__	)
class        __A      :
    a__       :						str
    a__       :						str
    a__       :						Optional[str]          =  None
    a__       :						Optional[str]          =  None
    a__       :						Optional[str]          =  None




@dataclass(frozen=UpperCamelCase__	)
class        __A      :
    a__       :						List[int]
    a__       :						Optional[List[int]]          =  None
    a__       :						Optional[List[int]]          =  None
    a__       :						Optional[Union[int, float]]          =  None
    a__       :						Optional[int]          =  None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset




    class        __A      ( UpperCamelCase__	):
        a__       :						List[InputFeatures]



        def __init__(self    :    Any   ,			__a    :    str   ,			__a    :    PreTrainedTokenizer   ,			__a    :    str   ,			__a    :    Optional[int] = None   ,			__a    :    Dict=False   ,			__a    :    bool = False   ,			):
             UpperCAmelCase_           =   hans_processors[task]()

             UpperCAmelCase_           =   os.path.join(
                 __a   ,			"cached_{}_{}_{}_{}".format(
                     "dev" if evaluate else "train"   ,			tokenizer.__class__.__name__   ,			str(__a )   ,			__a   ,			)   ,			)
             UpperCAmelCase_           =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  UpperCAmelCase_    , UpperCAmelCase_           =   label_list[2], label_list[1]
             UpperCAmelCase_           =   label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             UpperCAmelCase_           =   cached_features_file + ".lock"
             with FileLock(__a ):
                  if os.path.exists(__a ) and not overwrite_cache:
                       logger.info(f"""Loading features from cached file {cached_features_file}""" )
                       UpperCAmelCase_           =   torch.load(__a )
                  else:
                       logger.info(f"""Creating features from dataset file at {data_dir}""" )

                       UpperCAmelCase_           =   (
                           processor.get_dev_examples(__a ) if evaluate else processor.get_train_examples(__a )
                       )

                       logger.info("Training examples: %s"   ,			len(__a ) )
                       UpperCAmelCase_           =   hans_convert_examples_to_features(__a   ,			__a   ,			__a   ,			__a )
                       logger.info("Saving features into cached file %s"   ,			__a )
                       torch.save(self.features   ,			__a )



        def __len__(self    :    List[Any] ):
             return len(self.features )



        def __getitem__(self    :    Any   ,			__a    :    Optional[Any] ):
             return self.features[i]






        def 					_lowercase						(self    :    Union[str, Any] ):
             return self.label_list


if is_tf_available():
    import tensorflow as tf




    class        __A      :
        a__       :						List[InputFeatures]



        def __init__(self    :    Union[str, Any]   ,			__a    :    str   ,			__a    :    PreTrainedTokenizer   ,			__a    :    str   ,			__a    :    Optional[int] = 128   ,			__a    :    Any=False   ,			__a    :    bool = False   ,			):
             UpperCAmelCase_           =   hans_processors[task]()
             UpperCAmelCase_           =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  UpperCAmelCase_    , UpperCAmelCase_           =   label_list[2], label_list[1]
             UpperCAmelCase_           =   label_list

             UpperCAmelCase_           =   processor.get_dev_examples(__a ) if evaluate else processor.get_train_examples(__a )
             UpperCAmelCase_           =   hans_convert_examples_to_features(__a   ,			__a   ,			__a   ,			__a )

             def gen():
                  for ex_index, ex in tqdm.tqdm(enumerate(self.features )   ,			desc="convert examples to features" ):
                       if ex_index % 10000 == 0:
                            logger.info("Writing example %d of %d" % (ex_index, len(__a )) )

                       yield (
                           {
                               "example_id": 0,
                               "input_ids": ex.input_ids,
                               "attention_mask": ex.attention_mask,
                               "token_type_ids": ex.token_type_ids,
                           },
                           ex.label,
                       )

             UpperCAmelCase_           =   tf.data.Dataset.from_generator(
                 __a   ,			(
                     {
                         "example_id": tf.intaa,
                         "input_ids": tf.intaa,
                         "attention_mask": tf.intaa,
                         "token_type_ids": tf.intaa,
                     },
                     tf.intaa,
                 )   ,			(
                     {
                         "example_id": tf.TensorShape([] ),
                         "input_ids": tf.TensorShape([None, None] ),
                         "attention_mask": tf.TensorShape([None, None] ),
                         "token_type_ids": tf.TensorShape([None, None] ),
                     },
                     tf.TensorShape([] ),
                 )   ,			)



        def 					_lowercase						(self    :    int ):
             return self.dataset



        def __len__(self    :    Any ):
             return len(self.features )



        def __getitem__(self    :    int   ,			__a    :    Union[str, Any] ):
             return self.features[i]






        def 					_lowercase						(self    :    int ):
             return self.label_list




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    Dict ):
         return self._create_examples(self._read_tsv(os.path.join(__a   ,			"heuristics_train_set.txt" ) )   ,			"train" )



    def 					_lowercase						(self    :    Any   ,			__a    :    List[Any] ):
         return self._create_examples(self._read_tsv(os.path.join(__a   ,			"heuristics_evaluation_set.txt" ) )   ,			"dev" )



    def 					_lowercase						(self    :    Any ):
         return ["contradiction", "entailment", "neutral"]






    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Optional[int]   ,			__a    :    Union[str, Any] ):
         UpperCAmelCase_           =   []
         for i, line in enumerate(__a ):
              if i == 0:
                   continue
              UpperCAmelCase_           =   "%s-%s" % (set_type, line[0])
              UpperCAmelCase_           =   line[5]
              UpperCAmelCase_           =   line[6]
              UpperCAmelCase_           =   line[7][2:] if line[7].startswith("ex" ) else line[7]
              UpperCAmelCase_           =   line[0]
              examples.append(InputExample(guid=__a   ,			text_a=__a   ,			text_b=__a   ,			label=__a   ,			pairID=__a ) )
         return examples





def 	lowerCAmelCase_				(					snake_case_       : List[InputExample]					,    snake_case_       : List[str]					,    snake_case_       : int					,    snake_case_       : PreTrainedTokenizer					,    )    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   {label: i for i, label in enumerate(snake_case_			)}

     UpperCAmelCase_           =   []
     for ex_index, example in tqdm.tqdm(enumerate(snake_case_			)					,    desc="convert examples to features"			):
          if ex_index % 1_00_00 == 0:
               logger.info("Writing example %d" % (ex_index)			)

          UpperCAmelCase_           =   tokenizer(
              example.text_a					,    example.text_b					,    add_special_tokens=snake_case_					,    max_length=snake_case_					,    padding="max_length"					,    truncation=snake_case_					,    return_overflowing_tokens=snake_case_					,    )

          UpperCAmelCase_           =   label_map[example.label] if example.label in label_map else 0

          UpperCAmelCase_           =   int(example.pairID			)

          features.append(InputFeatures(**snake_case_					,    label=snake_case_					,    pairID=snake_case_			)			)

     for i, example in enumerate(examples[:5]			):
          logger.info("*** Example ***"			)
          logger.info(f"""guid: {example}"""			)
          logger.info(f"""features: {features[i]}"""			)

     return features


SCREAMING_SNAKE_CASE_:     int  ={
    'hans': 3,
}






SCREAMING_SNAKE_CASE_:     Any  ={
    'hans': HansProcessor,
}




