





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_   :				Optional[Any]						      = logging.getLogger(__name__)
@dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  str       =		field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
      snake_case__       :  Optional[str]       =		field(
          default=lowercase__		,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
      snake_case__       :  Optional[str]       =		field(
          default=lowercase__		,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
      snake_case__       :  Optional[str]       =		field(
          default=lowercase__		,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,					)
      snake_case__       :  bool       =		field(default=lowercase__		,					metadata={'''help''': '''Whether tp freeze the encoder.'''}    )
      snake_case__       :  bool       =		field(default=lowercase__		,					metadata={'''help''': '''Whether  to freeze the embeddings.'''}    )
@dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  str       =		field(
          metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}    )
      snake_case__       :  Optional[str]       =		field(
          default='''summarization'''		,					metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}		,					)
      snake_case__       :  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.'''
              )
          }		,					)
      snake_case__       :  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.'''
              )
          }		,					)
      snake_case__       :  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``.'''
              )
          }		,					)
      snake_case__       :  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.'''
              )
          }		,					)
      snake_case__       :  Optional[int]       =		field(default=-1		,					metadata={'''help''': '''# training examples. -1 means use all.'''}    )
      snake_case__       :  Optional[int]       =		field(default=-1		,					metadata={'''help''': '''# validation examples. -1 means use all.'''}    )
      snake_case__       :  Optional[int]       =		field(default=-1		,					metadata={'''help''': '''# test examples. -1 means use all.'''}    )
      snake_case__       :  Optional[str]       =		field(default=lowercase__		,					metadata={'''help''': '''Source language id for translation.'''}    )
      snake_case__       :  Optional[str]       =		field(default=lowercase__		,					metadata={'''help''': '''Target language id for translation.'''}    )
      snake_case__       :  Optional[int]       =		field(default=lowercase__		,					metadata={'''help''': '''# num_beams to use for evaluation.'''}    )
      snake_case__       :  bool       =		field(
          default=lowercase__		,					metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}		,					)







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Tuple						,					__A				:    int       )			->	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  SCREAMING_SNAKE_CASE_		(    )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	str							=						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_      :	Tuple							=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
       else:
              a_      :	Dict							=						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.

       a_      :	str							=						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_      :	int							=						('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       )       )

       a_      :	Optional[Any]							=						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_      :	Optional[Any]							=						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:
              a_      :	List[Any]							=						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       ):
                     a_      :	Dict							=						tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     a_      :	int							=						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()       )

       a_      :	Union[str, Any]							=						SeqaSeqDataset

       # Get datasets
       a_      :	Optional[int]							=						(
           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
       )
       a_      :	str							=						(
           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
       )
       a_      :	int							=						(
           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
       a_      :	List[str]							=						(
           build_compute_metrics_fn(data_args.task						,					__A       ) if training_args.predict_with_generate else None
       )
       a_      :	Any							=						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						,					)

       a_      :	List[str]							=						{}
       # Training
       if training_args.do_train:
              logger.info('*** Train ***'       )

              a_      :	Union[str, Any]							=						trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
              a_      :	Optional[int]							=						train_result.metrics
              a_      :	Union[str, Any]							=						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 ***'       )

              a_      :	Any							=						trainer.evaluate(metric_key_prefix='val'       )
              a_      :	Dict							=						data_args.n_val
              a_      :	Optional[int]							=						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 ***'       )

              a_      :	Dict							=						trainer.predict(test_dataset=__A						,					metric_key_prefix='test'       )
              a_      :	Optional[int]							=						test_output.metrics
              a_      :	List[Any]							=						data_args.n_test

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

                     if training_args.predict_with_generate:
                            a_      :	Dict							=						tokenizer.batch_decode(
                                test_output.predictions						,					skip_special_tokens=__A						,					clean_up_tokenization_spaces=__A       )
                            a_      :	Union[str, Any]							=						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  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       main()


if __name__ == "__main__":
   main()






from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_   :				Dict						      = [
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


