






def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    str:






						"""simple docstring"""





						return (input_a, input_a).count(0						) % 2

def 			__lowerCamelCase     (						)				->    int:






						"""simple docstring"""





						assert xor_gate(0			,0						) == 0
						assert xor_gate(0			,1						) == 1
						assert xor_gate(1			,0						) == 1
						assert xor_gate(1			,1						) == 0


if __name__ == "__main__":
		print(xor_gate(0, 0))
		print(xor_gate(0, 1))









'''simple docstring'''







import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


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






@dataclass
class        a__     :
     '''simple docstring'''







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







@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}    )
     _snake_case							   =	field(
         metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    )
     _snake_case							   =	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.'
             )
         }			,    )
     _snake_case							   =	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.
      __A		 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      __A ,					__A ,					__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(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				,      )
      logger.info(
          "n_replicas: %s, distributed training: %s, 16-bits training: %s"				,      training_args.n_replicas				,      bool(training_args.n_replicas > 1				)				,      training_args.fpaa				,      )
      logger.info("Training/evaluation parameters %s"				,      snake_case__				)

      # Prepare Token Classification task
      __A		 =   token_classification_task.get_labels(data_args.labels				)
      __A		 =   dict(enumerate(snake_case__				)				)
      __A		 =   len(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=snake_case__				,      idalabel=snake_case__				,      labelaid={label: i for i, label in enumerate(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				,      )

      with training_args.strategy.scope():
            __A		 =   TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path				,      from_pt=bool(".bin" in model_args.model_name_or_path				)				,      config=snake_case__				,      cache_dir=model_args.cache_dir				,      )

      # Get datasets
      __A		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=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		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=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(snake_case__				,      snake_case__				) -> Tuple[List[int], List[int]]:
            __A		 =   np.argmax(snake_case__				,      axis=2				)
            __A ,					__A		 =   preds.shape
            __A		 =   [[] for _ in range(snake_case__				)]
            __A		 =   [[] for _ in range(snake_case__				)]

            for i in range(snake_case__				):
                  for j in range(snake_case__				):
                        if label_ids[i, j] != -100:
                              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(snake_case__				) -> Dict:
            __A ,					__A		 =   align_predictions(p.predictions				,      p.label_ids				)

            return {
                "precision": precision_score(snake_case__				,      snake_case__				),
                "recall": recall_score(snake_case__				,      snake_case__				),
                "f1": fa_score(snake_case__				,      snake_case__				),
            }

      # Initialize our Trainer
      __A		 =   TFTrainer(
          model=snake_case__				,      args=snake_case__				,      train_dataset=train_dataset.get_dataset() if train_dataset else None				,      eval_dataset=eval_dataset.get_dataset() if eval_dataset else None				,      compute_metrics=snake_case__				,      )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            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"				)

            with open(snake_case__				,      "w"				) as writer:
                  logger.info("***** Eval results *****"				)

                  for key, value in result.items():
                        logger.info("  %s = %s"				,      snake_case__				,      snake_case__				)
                        writer.write("%s = %s\n" % (key, value)				)

                  results.update(snake_case__				)

    # Predict
      if training_args.do_predict:
            __A		 =   TFTokenClassificationDataset(
                token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=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		 =   trainer.predict(test_dataset.get_dataset()				)
            __A ,					__A		 =   align_predictions(snake_case__				,      snake_case__				)
            __A		 =   classification_report(snake_case__				,      snake_case__				)

            logger.info("\n%s"				,      snake_case__				)

            __A		 =   os.path.join(training_args.output_dir				,      "test_results.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  writer.write("%s\n" % report				)

            # Save predictions
            __A		 =   os.path.join(training_args.output_dir				,      "test_predictions.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  with open(os.path.join(data_args.data_dir				,      "test.txt"				)				,      "r"				) as f:
                        __A		 =   0

                        for line in f:
                              if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                                    writer.write(snake_case__				)

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    __A		 =   line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"

                                    writer.write(snake_case__				)
                              else:
                                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."				,      line.split()[0]				)

      return results


if __name__ == "__main__":
       main()


