





'''simple docstring'''





import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def    __UpperCAmelCase(      lowercase_      ):



       def wrapper(*lowercase_ ,    **lowercase_      ):
              _lowerCamelCase							:     List[Any]	=		timeit.default_timer()
              _lowerCamelCase							:     List[Any]	=		func(*_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE      )
              _lowerCamelCase							:     Tuple	=		timeit.default_timer() - starttime
              return delta

       _lowerCamelCase							:     Dict	=		func.__name__

       return wrapper




def    __UpperCAmelCase(      lowercase_ ,    lowercase_=1_00 ,    lowercase_=None      ):
       _lowerCamelCase							:     Union[str, Any]	=		[]
       _lowerCamelCase							:     List[Any]	=		seq_shapes or {}
       for i in range(_SCREAMING_SNAKE_CASE      ):
              _lowerCamelCase							:     int	=		{}
              for col_id, (k, v) in enumerate(features.items()      ):
                     if isinstance(_SCREAMING_SNAKE_CASE ,    _ArrayXD      ):
                            _lowerCamelCase							:     Optional[Any]	=		np.random.rand(*v.shape      ).astype(v.dtype      )
                     elif isinstance(_SCREAMING_SNAKE_CASE ,    datasets.Value      ):
                            if v.dtype == "string":
                                   _lowerCamelCase							:     Optional[int]	=		'''The small grey turtle was surprisingly fast when challenged.'''
                            else:
                                   _lowerCamelCase							:     Union[str, Any]	=		np.random.randint(10 ,    size=1      ).astype(v.dtype      ).item()
                     elif isinstance(_SCREAMING_SNAKE_CASE ,    datasets.Sequence      ):
                            while isinstance(_SCREAMING_SNAKE_CASE ,    datasets.Sequence      ):
                                   _lowerCamelCase							:     Optional[int]	=		v.feature
                            _lowerCamelCase							:     Optional[Any]	=		seq_shapes[k]
                            _lowerCamelCase							:     Any	=		np.random.rand(*_SCREAMING_SNAKE_CASE      ).astype(v.dtype      )
                     _lowerCamelCase							:     Dict	=		data

              dummy_data.append((i, example)      )

       return dummy_data




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_=1_00 ,    lowercase_=None      ):
       _lowerCamelCase							:     Union[str, Any]	=		generate_examples(_SCREAMING_SNAKE_CASE ,    num_examples=_SCREAMING_SNAKE_CASE ,    seq_shapes=_SCREAMING_SNAKE_CASE      )

       with ArrowWriter(features=_SCREAMING_SNAKE_CASE ,    path=_SCREAMING_SNAKE_CASE      ) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase							:     Dict	=		features.encode_example(_SCREAMING_SNAKE_CASE      )
                     writer.write(_SCREAMING_SNAKE_CASE      )

              _lowerCamelCase,  _lowerCamelCase							:     Any	=		writer.finalize()

       if not num_final_examples == num_examples:
              raise ValueError(
                  F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

       _lowerCamelCase							:     str	=		datasets.Dataset.from_file(filename=_SCREAMING_SNAKE_CASE ,    info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE      )      )

       return dataset








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


_lowerCamelCase			       =      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=128 ,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    __UpperCAmelCase(      ):
       # 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							:     Dict	=		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							:     Tuple	=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
       else:
              _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		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							:     Optional[Any]	=		import_module('''tasks'''      )
       try:
              _lowerCamelCase							:     Dict	=		getattr(lowercase_ ,    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''' ,    lowercase_      )

       # Set seed
       set_seed(training_args.seed      )

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

       # 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=lowercase_ ,    idalabel=lowercase_ ,    labelaid={label: i for i, label in enumerate(lowercase_      )} ,    cache_dir=model_args.cache_dir ,    )
       _lowerCamelCase							:     List[str]	=		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							:     Tuple	=		AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path      ) ,    config=lowercase_ ,    cache_dir=model_args.cache_dir ,    )

       # Get datasets
       _lowerCamelCase							:     Optional[int]	=		(
           TokenClassificationDataset(
               token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    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							:     Dict	=		(
           TokenClassificationDataset(
               token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    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(lowercase_ ,    lowercase_      ) -> Tuple[List[int], List[int]]:
              _lowerCamelCase							:     List[Any]	=		np.argmax(lowercase_ ,    axis=2      )

              _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		preds.shape

              _lowerCamelCase							:     Optional[Any]	=		[[] for _ in range(lowercase_      )]
              _lowerCamelCase							:     str	=		[[] for _ in range(lowercase_      )]

              for i in range(lowercase_      ):
                     for j in range(lowercase_      ):
                            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(lowercase_      ) -> Dict:
              _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		align_predictions(p.predictions ,    p.label_ids      )
              return {
                  "accuracy_score": accuracy_score(lowercase_ ,    lowercase_      ),
                  "precision": precision_score(lowercase_ ,    lowercase_      ),
                  "recall": recall_score(lowercase_ ,    lowercase_      ),
                  "f1": fa_score(lowercase_ ,    lowercase_      ),
              }

       # Data collator
       _lowerCamelCase							:     Optional[int]	=		DataCollatorWithPadding(lowercase_ ,    pad_to_multiple_of=8      ) if training_args.fpaa else None

       # Initialize our Trainer
       _lowerCamelCase							:     List[Any]	=		Trainer(
           model=lowercase_ ,    args=lowercase_ ,    train_dataset=lowercase_ ,    eval_dataset=lowercase_ ,    compute_metrics=lowercase_ ,    data_collator=lowercase_ ,    )

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

              _lowerCamelCase							:     str	=		trainer.evaluate()

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

                     results.update(lowercase_      )

    # Predict
       if training_args.do_predict:
              _lowerCamelCase							:     Union[str, Any]	=		TokenClassificationDataset(
                  token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    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							:     Dict	=		trainer.predict(lowercase_      )
              _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		align_predictions(lowercase_ ,    lowercase_      )

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

        # Save predictions
              _lowerCamelCase							:     List[str]	=		os.path.join(training_args.output_dir ,    '''test_predictions.txt'''      )
              if trainer.is_world_process_zero():
                     with open(lowercase_ ,    '''w'''      ) as writer:
                            with open(os.path.join(data_args.data_dir ,    '''test.txt'''      ) ,    '''r'''      ) as f:
                                   token_classification_task.write_predictions_to_file(lowercase_ ,    lowercase_ ,    lowercase_      )

       return results




def    __UpperCAmelCase(      lowercase_      ):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
       main()

