
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


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






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






@dataclass
class      _UpperCAmelCase				:
    __lowercase							: str  =					field(
        metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""})
    __lowercase							: Optional[str]  =					field(
        default=__UpperCAmelCase		,					metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}		,					)
    __lowercase							: int  =					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."""
            )
        }		,					)
    __lowercase							: bool  =					field(
        default=__UpperCAmelCase		,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""})


def 				a__   (   ):

  """simple docstring"""







  _snake_case     :						List[Any]						=   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.
    _snake_case			,					_snake_case			,					_snake_case     :						str						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
  else:
    _snake_case			,					_snake_case			,					_snake_case     :						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."		)

  _snake_case     :						List[str]						=   import_module("tasks"		)
  try:
    _snake_case     :						Optional[Any]						=   getattr(_lowerCAmelCase       ,     model_args.task_type		)
    _snake_case     :						List[str]						=   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
  _snake_case     :						Union[str, Any]						=   token_classification_task.get_labels(data_args.labels		)
  _snake_case     :						List[Any]						=   dict(enumerate(_lowerCAmelCase		)		)
  _snake_case     :						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.

  _snake_case     :						Union[str, 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       ,     )
  _snake_case     :						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       ,     use_fast=model_args.use_fast       ,     )
  _snake_case     :						Any						=   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
  _snake_case     :						Tuple						=   (
      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
  )
  _snake_case     :						List[str]						=   (
      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(a   :				str       ,     a   :				int		) -> Tuple[List[int], List[int]]:
    _snake_case     :						Any						=   np.argmax(_lowerCAmelCase       ,     axis=2		)

    _snake_case			,					_snake_case     :						int						=   preds.shape

    _snake_case     :						List[Any]						=   [[] for _ in range(_lowerCAmelCase		)]
    _snake_case     :						List[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(a   :				str		) -> Dict:
    _snake_case			,					_snake_case     :						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
  _snake_case     :						int						=   DataCollatorWithPadding(_lowerCAmelCase       ,     pad_to_multiple_of=8		) if training_args.fpaa else None

  # Initialize our Trainer
  _snake_case     :						Optional[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
  _snake_case     :						Tuple						=   {}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"		)

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

    _snake_case     :						Union[str, Any]						=   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:
    _snake_case     :						Dict						=   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       ,     )

    _snake_case			,					_snake_case			,					_snake_case     :						List[str]						=   trainer.predict(_lowerCAmelCase		)
    _snake_case			,					_snake_case     :						List[Any]						=   align_predictions(_lowerCAmelCase       ,     _lowerCAmelCase		)

    _snake_case     :						int						=   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
    _snake_case     :						Optional[int]						=   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__   (   a   :				List[Any]		):

  """simple docstring"""







  main()


if __name__ == "__main__":
    main()



"""simple docstring"""
import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)






class      _UpperCAmelCase				(					unittest.TestCase):
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Vector([1, 2, 3]     )
      self.assertEqual(x.component(0     )						,    1     )
      self.assertEqual(x.component(2     )						,    3     )
      _snake_case     :						List[Any]						=   Vector()
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   Vector([0, 0, 0, 0, 0, 1]     )
      self.assertEqual(str(snake_case_     )						,    "(0,0,0,0,0,1)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   Vector([1, 2, 3, 4]     )
      self.assertEqual(len(snake_case_     )						,    4     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Vector([1, 2]     )
      _snake_case     :						List[str]						=   Vector([1, 2, 3, 4, 5]     )
      _snake_case     :						List[Any]						=   Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]     )
      _snake_case     :						Any						=   Vector([1, -1, 1, -1, 2, -3, 4, -5]     )
      self.assertAlmostEqual(x.euclidean_length()						,    2.236						,    3     )
      self.assertAlmostEqual(y.euclidean_length()						,    7.416						,    3     )
      self.assertEqual(z.euclidean_length()						,    0     )
      self.assertAlmostEqual(w.euclidean_length()						,    7.616						,    3     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Vector([1, 2, 3]     )
      _snake_case     :						Any						=   Vector([1, 1, 1]     )
      self.assertEqual((x + y).component(0     )						,    2     )
      self.assertEqual((x + y).component(1     )						,    3     )
      self.assertEqual((x + y).component(2     )						,    4     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   Vector([1, 2, 3]     )
      _snake_case     :						Union[str, Any]						=   Vector([1, 1, 1]     )
      self.assertEqual((x - y).component(0     )						,    0     )
      self.assertEqual((x - y).component(1     )						,    1     )
      self.assertEqual((x - y).component(2     )						,    2     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   Vector([1, 2, 3]     )
      _snake_case     :						List[Any]						=   Vector([2, -1, 4]     )  # for test of dot product
      _snake_case     :						Union[str, Any]						=   Vector([1, -2, -1]     )
      self.assertEqual(str(x * 3.0     )						,    "(3.0,6.0,9.0)"     )
      self.assertEqual((a * b)						,    0     )
    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(str(zero_vector(10     )     ).count("0"     )						,    10     )
    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(str(unit_basis_vector(3						,    1     )     )						,    "(0,1,0)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   Vector([1, 2, 3]     )
      _snake_case     :						Optional[Any]						=   Vector([1, 0, 1]     )
      self.assertEqual(str(axpy(2						,    snake_case_						,    snake_case_     )     )						,    "(3,4,7)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   Vector([1, 0, 0, 0, 0, 0]     )
      _snake_case     :						Optional[int]						=   x.copy()
      self.assertEqual(str(snake_case_     )						,    str(snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   Vector([1, 0, 0]     )
      x.change_component(0						,    0     )
      x.change_component(1						,    1     )
      self.assertEqual(str(snake_case_     )						,    "(0,1,0)"     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      self.assertEqual("|1,2,3|\n|2,4,5|\n|6,7,8|\n"						,    str(snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						str						=   [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
      for x in range(a.height()     ):
        for y in range(a.width()     ):
          self.assertEqual(minors[x][y]						,    a.minor(snake_case_						,    snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						Optional[Any]						=   [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
      for x in range(a.height()     ):
        for y in range(a.width()     ):
          self.assertEqual(cofactors[x][y]						,    a.cofactor(snake_case_						,    snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      self.assertEqual(-5						,    a.determinant()     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						str						=   Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						,    3						,    3     )
      _snake_case     :						List[str]						=   Vector([1, 2, 3]     )
      self.assertEqual("(14,32,50)"						,    str(a * x     )     )
      self.assertEqual("|2,4,6|\n|8,10,12|\n|14,16,18|\n"						,    str(a * 2     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[int]						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      a.change_component(0						,    2						,    5     )
      self.assertEqual("|1,2,5|\n|2,4,5|\n|6,7,8|\n"						,    str(snake_case_     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      self.assertEqual(7						,    a.component(2						,    1     )						,    0.01     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Tuple						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						int						=   Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3     )
      self.assertEqual("|2,4,10|\n|4,8,10|\n|12,14,18|\n"						,    str(a + b     )     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3     )
      _snake_case     :						Optional[Any]						=   Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3     )
      self.assertEqual("|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"						,    str(a - b     )     )


    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(
          "|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"						,    str(square_zero_matrix(5     )     )						,    )


if __name__ == "__main__":
    unittest.main()

