


'''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 							__magic_name__	:

     """simple docstring"""






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







@dataclass
class 							__magic_name__	:

     """simple docstring"""






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


def 		_lowercase ():




     '''simple docstring'''





     __A	:       str      =							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  ,__A  ,__A	:       Any      =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
     else:
          __A  ,__A  ,__A	:       List[str]      =							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	:       List[Any]      =							import_module("tasks"							)
     try:
          __A	:       List[str]      =							getattr(SCREAMING_SNAKE_CASE       ,					model_args.task_type							)
          __A	:       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"       ,					SCREAMING_SNAKE_CASE							)

     # Set seed
     set_seed(training_args.seed							)

     # Prepare CONLL-2003 task
     __A	:       List[str]      =							token_classification_task.get_labels(data_args.labels							)
     __A	:       Dict[int, str]      =							dict(enumerate(SCREAMING_SNAKE_CASE							)							)
     __A	:       int      =							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	:       Any      =							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	:       Dict      =							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	:       Optional[int]      =							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	:       str      =							(
         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	:       str      =							(
         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(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> Tuple[List[int], List[int]]:
          __A	:       Optional[int]      =							np.argmax(SCREAMING_SNAKE_CASE       ,					axis=2							)

          __A  ,__A	:       Optional[int]      =							preds.shape

          __A	:       List[Any]      =							[[] for _ in range(SCREAMING_SNAKE_CASE							)]
          __A	:       Union[str, Any]      =							[[] 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(SCREAMING_SNAKE_CASE							) -> Dict:
          __A  ,__A	:       Optional[Any]      =							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	:       Any      =							DataCollatorWithPadding(SCREAMING_SNAKE_CASE       ,					pad_to_multiple_of=8							) if training_args.fpaa else None

     # Initialize our Trainer
     __A	:       Dict      =							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	:       Any      =							{}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"							)

          __A	:       Any      =							trainer.evaluate()

          __A	:       int      =							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	:       Tuple      =							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  ,__A  ,__A	:       int      =							trainer.predict(SCREAMING_SNAKE_CASE							)
          __A  ,__A	:       Dict      =							align_predictions(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

          __A	:       List[str]      =							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	:       str      =							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 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     main()


if __name__ == "__main__":
      main()





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase 							=  {
    """configuration_upernet""": ["""UperNetConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCamelCase 							=  [
          """UperNetForSemanticSegmentation""",
          """UperNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_upernet import UperNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
      import sys

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

