

"""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


SCREAMING_SNAKE_CASE_    :		List[str]							     =  logging.getLogger(__name__)



@dataclass
class     a  :



    """simple docstring"""



    UpperCAmelCase         =    field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
    UpperCAmelCase         =    field(
        default=_lowerCamelCase,		metadata={"help": "Pretrained config name or path if not the same as model_name"} )
    UpperCAmelCase         =    field(
        default="NER",		metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
    UpperCAmelCase         =    field(
        default=_lowerCamelCase,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
    UpperCAmelCase         =    field(default=_lowerCamelCase,		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.
    UpperCAmelCase         =    field(
        default=_lowerCamelCase,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},		)



@dataclass
class     a  :



    """simple docstring"""



    UpperCAmelCase         =    field(
        metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
    UpperCAmelCase         =    field(
        default=_lowerCamelCase,		metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."},		)
    UpperCAmelCase         =    field(
        default=1_2_8,		metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },		)
    UpperCAmelCase         =    field(
        default=_lowerCamelCase,		metadata={"help": "Overwrite the cached training and evaluation sets"} )





def        _snake_case   (			):
     # 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__								=     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.
          A__								=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
     else:
          A__								=     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."""	)

     A__								=     import_module("""tasks"""	)
     try:
          A__								=     getattr(SCREAMING_SNAKE_CASE_    ,      model_args.task_type	)
          A__								=     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"""    ,      SCREAMING_SNAKE_CASE_	)

     # Set seed
     set_seed(training_args.seed	)

     # Prepare CONLL-2003 task
     A__								=     token_classification_task.get_labels(data_args.labels	)
     A__								=     dict(enumerate(SCREAMING_SNAKE_CASE_	)	)
     A__								=     len(SCREAMING_SNAKE_CASE_	)

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

     A__								=     AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,      num_labels=SCREAMING_SNAKE_CASE_    ,      idalabel=SCREAMING_SNAKE_CASE_    ,      labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_	)}    ,      cache_dir=model_args.cache_dir    ,      )
     A__								=     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    ,      )
     A__								=     AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path    ,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path	)    ,      config=SCREAMING_SNAKE_CASE_    ,      cache_dir=model_args.cache_dir    ,      )

     # Get datasets
     A__								=     (
         TokenClassificationDataset(
             token_classification_task=SCREAMING_SNAKE_CASE_    ,      data_dir=data_args.data_dir    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      labels=SCREAMING_SNAKE_CASE_    ,      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
     )
     A__								=     (
         TokenClassificationDataset(
             token_classification_task=SCREAMING_SNAKE_CASE_    ,      data_dir=data_args.data_dir    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      labels=SCREAMING_SNAKE_CASE_    ,      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(UpperCAmelCase_     :    List[str]    ,      UpperCAmelCase_     :    Tuple	) -> Tuple[List[int], List[int]]:
          A__								=     np.argmax(SCREAMING_SNAKE_CASE_    ,      axis=2	)

          A__								=     preds.shape

          A__								=     [[] for _ in range(SCREAMING_SNAKE_CASE_	)]
          A__								=     [[] for _ in range(SCREAMING_SNAKE_CASE_	)]

          for i in range(SCREAMING_SNAKE_CASE_	):
               for j in range(SCREAMING_SNAKE_CASE_	):
                    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(UpperCAmelCase_     :    Any	) -> Dict:
          A__								=     align_predictions(p.predictions    ,      p.label_ids	)
          return {
              "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_	),
              "precision": precision_score(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_	),
              "recall": recall_score(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_	),
              "f1": fa_score(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_	),
          }

     # Data collator
     A__								=     DataCollatorWithPadding(SCREAMING_SNAKE_CASE_    ,      pad_to_multiple_of=8	) if training_args.fpaa else None

     # Initialize our Trainer
     A__								=     Trainer(
         model=SCREAMING_SNAKE_CASE_    ,      args=SCREAMING_SNAKE_CASE_    ,      train_dataset=SCREAMING_SNAKE_CASE_    ,      eval_dataset=SCREAMING_SNAKE_CASE_    ,      compute_metrics=SCREAMING_SNAKE_CASE_    ,      data_collator=SCREAMING_SNAKE_CASE_    ,      )

     # 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
     A__								=     {}
     if training_args.do_eval:
          logger.info("""*** Evaluate ***"""	)

          A__								=     trainer.evaluate()

          A__								=     os.path.join(training_args.output_dir    ,      """eval_results.txt"""	)
          if trainer.is_world_process_zero():
               with open(SCREAMING_SNAKE_CASE_    ,      """w"""	) as writer:
                    logger.info("""***** Eval results *****"""	)
                    for key, value in result.items():
                         logger.info("""  %s = %s"""    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_	)
                         writer.write("""%s = %s\n""" % (key, value)	)

               results.update(SCREAMING_SNAKE_CASE_	)

    # Predict
     if training_args.do_predict:
          A__								=     TokenClassificationDataset(
              token_classification_task=SCREAMING_SNAKE_CASE_    ,      data_dir=data_args.data_dir    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      labels=SCREAMING_SNAKE_CASE_    ,      model_type=config.model_type    ,      max_seq_length=data_args.max_seq_length    ,      overwrite_cache=data_args.overwrite_cache    ,      mode=Split.test    ,      )

          A__								=     trainer.predict(SCREAMING_SNAKE_CASE_	)
          A__								=     align_predictions(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_	)

          A__								=     os.path.join(training_args.output_dir    ,      """test_results.txt"""	)
          if trainer.is_world_process_zero():
               with open(SCREAMING_SNAKE_CASE_    ,      """w"""	) as writer:
                    for key, value in metrics.items():
                         logger.info("""  %s = %s"""    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_	)
                         writer.write("""%s = %s\n""" % (key, value)	)

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

     return results





def        _snake_case   (			UpperCAmelCase_     :    Union[str, Any]	):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate







@dataclass(frozen=__magic_name__	)
class      A__							(     __magic_name__	):
				lowercase			    =  field(default='audio-classification'						,   metadata={'include_in_asdict_even_if_is_default': True}	)
				lowercase			    =  Features({'audio': Audio()}	)
				lowercase			    =  Features({'labels': ClassLabel}	)
				lowercase			    =  "audio"
				lowercase			    =  "labels"






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      Tuple   ):






							'''simple docstring'''







							if self.label_column not in features:
										raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
							if not isinstance(features[self.label_column]    ,     a   ):
										raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
							lowerCAmelCase__      :      Tuple         =		copy.deepcopy(self   )
							lowerCAmelCase__      :      List[Any]         =		self.label_schema.copy()
							lowerCAmelCase__      :      List[Any]         =		features[self.label_column]
							lowerCAmelCase__      :      Optional[int]         =		label_schema
							return task_template






				@property
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							return {
							    self.audio_column: "audio",
							    self.label_column: "labels",
							}