



'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_UpperCamelCase											=						logging.getLogger(__name__)






@dataclass
class 					lowerCamelCase_    :
  """simple docstring"""




  a_												=field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
  a_												=field(
      default=SCREAMING_SNAKE_CASE_   ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
  a_												=field(
      default="""NER"""   ,   metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}     )
  a_												=field(
      default=SCREAMING_SNAKE_CASE_   ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
  a_												=field(default=SCREAMING_SNAKE_CASE_   ,   metadata={"""help""": """Set this flag to use fast tokenization."""}     )
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  a_												=field(
      default=SCREAMING_SNAKE_CASE_   ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,   )






@dataclass
class 					lowerCamelCase_    :
  """simple docstring"""




  a_												=field(
      metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}     )
  a_												=field(
      default=SCREAMING_SNAKE_CASE_   ,   metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}   ,   )
  a_												=field(
      default=128   ,   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=SCREAMING_SNAKE_CASE_   ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )




def        a_     (							)      ->      Union[str, Any]:
  # 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.

  __lowerCamelCase       :       Optional[int]           = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
  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.
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
  else:
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       Optional[int]           = parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir       )
      and os.listdir(training_args.output_dir       )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
        ' --overwrite_output_dir to overcome.'       )

  __lowerCamelCase       :       List[str]           = import_module('tasks'       )
  try:
    __lowerCamelCase       :       Optional[int]           = getattr(_lowerCAmelCase       ,model_args.task_type       )
    __lowerCamelCase       :       TokenClassificationTask           = token_classification_task_clazz()
  except AttributeError:
    raise ValueError(
        F'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
        F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'       )

  # 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.local_rank != -1       )       ,training_args.fpaa       ,)
  # 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()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('Training/evaluation parameters %s'       ,_lowerCAmelCase       )

  # Set seed
  set_seed(training_args.seed       )

  # Prepare CONLL-2003 task
  __lowerCamelCase       :       Any           = token_classification_task.get_labels(data_args.labels       )
  __lowerCamelCase       :       Dict[int, str]           = dict(enumerate(_lowerCAmelCase       )       )
  __lowerCamelCase       :       Optional[Any]           = len(_lowerCAmelCase       )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,num_labels=_lowerCAmelCase       ,idalabel=_lowerCAmelCase       ,labelaid={label: i for i, label in enumerate(_lowerCAmelCase       )}       ,cache_dir=model_args.cache_dir       ,)
  __lowerCamelCase       :       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       ,use_fast=model_args.use_fast       ,)
  __lowerCamelCase       :       Optional[int]           = AutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path       ,from_tf=bool('.ckpt' in model_args.model_name_or_path       )       ,config=_lowerCAmelCase       ,cache_dir=model_args.cache_dir       ,)

  # Get datasets
  __lowerCamelCase       :       Any           = (
      TokenClassificationDataset(
          token_classification_task=_lowerCAmelCase       ,data_dir=data_args.data_dir       ,tokenizer=_lowerCAmelCase       ,labels=_lowerCAmelCase       ,model_type=config.model_type       ,max_seq_length=data_args.max_seq_length       ,overwrite_cache=data_args.overwrite_cache       ,mode=Split.train       ,)
      if training_args.do_train
      else None
  )
  __lowerCamelCase       :       Any           = (
      TokenClassificationDataset(
          token_classification_task=_lowerCAmelCase       ,data_dir=data_args.data_dir       ,tokenizer=_lowerCAmelCase       ,labels=_lowerCAmelCase       ,model_type=config.model_type       ,max_seq_length=data_args.max_seq_length       ,overwrite_cache=data_args.overwrite_cache       ,mode=Split.dev       ,)
      if training_args.do_eval
      else None
  )

  def align_predictions(_lowerCAmelCase       ,_lowerCAmelCase       ) -> Tuple[List[int], List[int]]:
    __lowerCamelCase       :       List[str]           = np.argmax(_lowerCAmelCase       ,axis=2       )

    __lowerCamelCase       ,__lowerCamelCase       :       Tuple           = preds.shape

    __lowerCamelCase       :       Tuple           = [[] for _ in range(_lowerCAmelCase       )]
    __lowerCamelCase       :       Any           = [[] for _ in range(_lowerCAmelCase       )]

    for i in range(_lowerCAmelCase       ):
      for j in range(_lowerCAmelCase       ):
        if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
          out_label_list[i].append(label_map[label_ids[i][j]]       )
          preds_list[i].append(label_map[preds[i][j]]       )

    return preds_list, out_label_list

  def compute_metrics(_lowerCAmelCase       ) -> Dict:
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = align_predictions(p.predictions       ,p.label_ids       )
    return {
        "accuracy_score": accuracy_score(_lowerCAmelCase       ,_lowerCAmelCase       ),
        "precision": precision_score(_lowerCAmelCase       ,_lowerCAmelCase       ),
        "recall": recall_score(_lowerCAmelCase       ,_lowerCAmelCase       ),
        "f1": fa_score(_lowerCAmelCase       ,_lowerCAmelCase       ),
    }

  # Data collator
  __lowerCamelCase       :       Any           = DataCollatorWithPadding(_lowerCAmelCase       ,pad_to_multiple_of=8       ) if training_args.fpaa else None

  # Initialize our Trainer
  __lowerCamelCase       :       int           = Trainer(
      model=_lowerCAmelCase       ,args=_lowerCAmelCase       ,train_dataset=_lowerCAmelCase       ,eval_dataset=_lowerCAmelCase       ,compute_metrics=_lowerCAmelCase       ,data_collator=_lowerCAmelCase       ,)

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_process_zero():
      tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
  __lowerCamelCase       :       Dict           = {}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'       )

    __lowerCamelCase       :       List[Any]           = trainer.evaluate()

    __lowerCamelCase       :       List[str]           = os.path.join(training_args.output_dir       ,'eval_results.txt'       )
    if trainer.is_world_process_zero():
      with open(_lowerCAmelCase       ,'w'       ) as writer:
        logger.info('***** Eval results *****'       )
        for key, value in result.items():
          logger.info('  %s = %s'       ,_lowerCAmelCase       ,_lowerCAmelCase       )
          writer.write('%s = %s\n' % (key, value)       )

      results.update(_lowerCAmelCase       )

    # Predict
  if training_args.do_predict:
    __lowerCamelCase       :       List[Any]           = TokenClassificationDataset(
        token_classification_task=_lowerCAmelCase       ,data_dir=data_args.data_dir       ,tokenizer=_lowerCAmelCase       ,labels=_lowerCAmelCase       ,model_type=config.model_type       ,max_seq_length=data_args.max_seq_length       ,overwrite_cache=data_args.overwrite_cache       ,mode=Split.test       ,)

    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = trainer.predict(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = align_predictions(_lowerCAmelCase       ,_lowerCAmelCase       )

    __lowerCamelCase       :       Optional[Any]           = os.path.join(training_args.output_dir       ,'test_results.txt'       )
    if trainer.is_world_process_zero():
      with open(_lowerCAmelCase       ,'w'       ) as writer:
        for key, value in metrics.items():
          logger.info('  %s = %s'       ,_lowerCAmelCase       ,_lowerCAmelCase       )
          writer.write('%s = %s\n' % (key, value)       )

        # Save predictions
    __lowerCamelCase       :       Tuple           = os.path.join(training_args.output_dir       ,'test_predictions.txt'       )
    if trainer.is_world_process_zero():
      with open(_lowerCAmelCase       ,'w'       ) as writer:
        with open(os.path.join(data_args.data_dir       ,'test.txt'       )       ,'r'       ) as f:
          token_classification_task.write_predictions_to_file(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )

  return results

def        a_     (							_lowerCAmelCase       )      ->      Union[str, Any]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
   main()











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase											=						{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['SpeechEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






