

'''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,
)


lowerCamelCase			:				int 			=					logging.getLogger(__name__)







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







@dataclass
class 							A__   :
  A__					=    field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}  )
  A__					=    field(
      default='summarization'					,       metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}					,       )
  A__					=    field(
      default=10_24					,       metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }					,       )
  A__					=    field(
      default=1_28					,       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__					=    field(
      default=1_42					,       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__					=    field(
      default=1_42					,       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__					=    field(default=-1					,       metadata={'help': '# training examples. -1 means use all.'}  )
  A__					=    field(default=-1					,       metadata={'help': '# validation examples. -1 means use all.'}  )
  A__					=    field(default=-1					,       metadata={'help': '# test examples. -1 means use all.'}  )
  A__					=    field(default=A__					,       metadata={'help': 'Source language id for translation.'}  )
  A__					=    field(default=A__					,       metadata={'help': 'Target language id for translation.'}  )
  A__					=    field(default=A__					,       metadata={'help': '# num_beams to use for evaluation.'}  )
  A__					=    field(
      default=A__					,       metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}					,       )


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Any    )				->      str:
     """simple docstring"""




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


def 	_lowerCAmelCase (						)				->      List[str]:
     """simple docstring"""




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

     check_output_dir(_UpperCamelCase    )

     # 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'							,			_UpperCamelCase    )

     # 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.

     _SCREAMING_SNAKE_CASE							   =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							,			)

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

     _SCREAMING_SNAKE_CASE							   =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							,			)
     _SCREAMING_SNAKE_CASE							   =AutoModelForSeqaSeqLM.from_pretrained(
         model_args.model_name_or_path							,			from_tf='.ckpt' in model_args.model_name_or_path							,			config=_UpperCamelCase							,			cache_dir=model_args.cache_dir							,			)

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

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

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

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

     _SCREAMING_SNAKE_CASE							   =SeqaSeqDataset

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

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

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

          trainer.save_model()  # this also saves the tokenizer

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

               # 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 ***'    )

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

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

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

          _SCREAMING_SNAKE_CASE							   =trainer.predict(test_dataset=_UpperCamelCase							,			metric_key_prefix='test'    )
          _SCREAMING_SNAKE_CASE							   =test_output.metrics
          _SCREAMING_SNAKE_CASE							   =data_args.n_test

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

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

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

     return all_metrics


def 	_lowerCAmelCase (						_UpperCamelCase						:					int    )				->      List[str]:
     """simple docstring"""




     main()


if __name__ == "__main__":
     main()



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				List[Any] 			=					["FNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["FNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FNetForMaskedLM",
         "FNetForMultipleChoice",
         "FNetForNextSentencePrediction",
         "FNetForPreTraining",
         "FNetForQuestionAnswering",
         "FNetForSequenceClassification",
         "FNetForTokenClassification",
         "FNetLayer",
         "FNetModel",
         "FNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
