





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__       :			Optional[Any]			      =						logging.getLogger(__name__)







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







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						str     		=				field(
       metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(
       default="""summarization"""		,metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}		,)
   _UpperCamelCase :						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."""
           )
       }		,)
   _UpperCamelCase :						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."""
           )
       }		,)
   _UpperCamelCase :						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``."""
           )
       }		,)
   _UpperCamelCase :						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."""
           )
       }		,)
   _UpperCamelCase :						Optional[int]     		=				field(default=-1		,metadata={"""help""": """# training examples. -1 means use all."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=-1		,metadata={"""help""": """# validation examples. -1 means use all."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=-1		,metadata={"""help""": """# test examples. -1 means use all."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """Source language id for translation."""}  )
   _UpperCamelCase :						Optional[str]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """Target language id for translation."""}  )
   _UpperCamelCase :						Optional[int]     		=				field(default=_UpperCamelCase		,metadata={"""help""": """# num_beams to use for evaluation."""}  )
   _UpperCamelCase :						bool     		=				field(
       default=_UpperCamelCase		,metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}		,)







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






def       _A  (      ):
   # 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__	:						List[Any]      		=  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__	:						str      		=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      a__		,     a__		,     a__	:						Dict      		=  parser.parse_args_into_dataclasses()

   check_output_dir(lowerCamelCase   )

   # 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"							, lowerCamelCase   )

   # 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__	:						List[Any]      		=  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__	:						Optional[Any]      		=  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
         assert hasattr(lowerCamelCase							, lowerCamelCase   ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
         setattr(lowerCamelCase							, lowerCamelCase							, getattr(lowerCamelCase							, lowerCamelCase   )   )

   a__	:						Tuple      		=  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__	:						Union[str, Any]      		=  AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path							, from_tf=".ckpt" in model_args.model_name_or_path							, config=lowerCamelCase							, cache_dir=model_args.cache_dir							, )

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

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

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(lowerCamelCase							, (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(lowerCamelCase							, lowerCamelCase   ):
         a__	:						Optional[int]      		=  tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         a__	:						List[Any]      		=  tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

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

   a__	:						str      		=  SeqaSeqDataset

   # Get datasets
   a__	:						List[Any]      		=  (
       dataset_class(
           lowerCamelCase							, 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__	:						List[str]      		=  (
       dataset_class(
           lowerCamelCase							, 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__	:						List[str]      		=  (
       dataset_class(
           lowerCamelCase							, 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__	:						Any      		=  (
       build_compute_metrics_fn(data_args.task							, lowerCamelCase   ) if training_args.predict_with_generate else None
   )
   a__	:						Optional[int]      		=  SeqaSeqTrainer(
       model=lowerCamelCase							, args=lowerCamelCase							, data_args=lowerCamelCase							, train_dataset=lowerCamelCase							, eval_dataset=lowerCamelCase							, data_collator=SeqaSeqDataCollator(
           lowerCamelCase							, lowerCamelCase							, model.config.decoder_start_token_id							, training_args.tpu_num_cores   )							, compute_metrics=lowerCamelCase							, tokenizer=lowerCamelCase							, )

   a__	:						Any      		=  {}
   # 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[Any]      		=  train_result.metrics
      a__	:						List[Any]      		=  data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

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

         # 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__	:						str      		=  trainer.evaluate(metric_key_prefix="val"   )
      a__	:						Any      		=  data_args.n_val
      a__	:						Tuple      		=  round(metrics["val_loss"]							, 4   )

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

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

      a__	:						Optional[Any]      		=  trainer.predict(test_dataset=lowerCamelCase							, metric_key_prefix="test"   )
      a__	:						int      		=  test_output.metrics
      a__	:						Union[str, Any]      		=  data_args.n_test

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

         if training_args.predict_with_generate:
            a__	:						Union[str, Any]      		=  tokenizer.batch_decode(
                test_output.predictions							, skip_special_tokens=lowerCamelCase							, clean_up_tokenization_spaces=lowerCamelCase   )
            a__	:						int      		=  lmap(str.strip							, lowerCamelCase   )
            write_txt_file(lowerCamelCase							, os.path.join(training_args.output_dir							, "test_generations.txt"   )   )

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

   return all_metrics






def       _A  (      lowerCamelCase   ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       :			str			      =						{
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						[
      """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DistilBertForMaskedLM""",
      """DistilBertForMultipleChoice""",
      """DistilBertForQuestionAnswering""",
      """DistilBertForSequenceClassification""",
      """DistilBertForTokenClassification""",
      """DistilBertModel""",
      """DistilBertPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[Any]			      =						[
      """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDistilBertForMaskedLM""",
      """TFDistilBertForMultipleChoice""",
      """TFDistilBertForQuestionAnswering""",
      """TFDistilBertForSequenceClassification""",
      """TFDistilBertForTokenClassification""",
      """TFDistilBertMainLayer""",
      """TFDistilBertModel""",
      """TFDistilBertPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Any			      =						[
      """FlaxDistilBertForMaskedLM""",
      """FlaxDistilBertForMultipleChoice""",
      """FlaxDistilBertForQuestionAnswering""",
      """FlaxDistilBertForSequenceClassification""",
      """FlaxDistilBertForTokenClassification""",
      """FlaxDistilBertModel""",
      """FlaxDistilBertPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_distilbert import (
      DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DistilBertConfig,
      DistilBertOnnxConfig,
  )
  from .tokenization_distilbert import DistilBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_distilbert_fast import DistilBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_distilbert import (
        DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DistilBertForMaskedLM,
        DistilBertForMultipleChoice,
        DistilBertForQuestionAnswering,
        DistilBertForSequenceClassification,
        DistilBertForTokenClassification,
        DistilBertModel,
        DistilBertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_distilbert import (
        TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDistilBertForMaskedLM,
        TFDistilBertForMultipleChoice,
        TFDistilBertForQuestionAnswering,
        TFDistilBertForSequenceClassification,
        TFDistilBertForTokenClassification,
        TFDistilBertMainLayer,
        TFDistilBertModel,
        TFDistilBertPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
        FlaxDistilBertPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

