






'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
    lowerCamelCase_							      =      argparse.ArgumentParser()

    # Hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument('''--epochs''', type=int, default=1)
    parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
    parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
    parser.add_argument('''--model_name_or_path''', type=str)
    parser.add_argument('''--learning_rate''', type=str, default=5e-5)
    parser.add_argument('''--do_train''', type=bool, default=True)
    parser.add_argument('''--do_eval''', type=bool, default=True)
    parser.add_argument('''--output_dir''', type=str)

    lowerCamelCase_							      =      parser.parse_known_args()

    # overwrite batch size until we have tf_glue.py
    lowerCamelCase_							      =      16
    lowerCamelCase_							      =      16

    # Set up logging
    lowerCamelCase_							      =      logging.getLogger(__name__)

    logging.basicConfig(
        level=logging.getLevelName('''INFO'''),
        handlers=[logging.StreamHandler(sys.stdout)],
        format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
    )

    # Load model and tokenizer
    lowerCamelCase_							      =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
    lowerCamelCase_							      =      AutoTokenizer.from_pretrained(args.model_name_or_path)

    # Load dataset
    lowerCamelCase_							      =      load_dataset('''imdb''', split=['''train''', '''test'''])
    lowerCamelCase_							      =      train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
    lowerCamelCase_							      =      test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

    # Preprocess train dataset
    lowerCamelCase_							      =      train_dataset.map(
        lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
    )
    train_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

    lowerCamelCase_							      =      {
        x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
        for x in ['input_ids', 'attention_mask']
    }
    lowerCamelCase_							      =      tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
        args.per_device_train_batch_size
    )

    # Preprocess test dataset
    lowerCamelCase_							      =      test_dataset.map(
        lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
    )
    test_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

    lowerCamelCase_							      =      {
        x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
        for x in ['input_ids', 'attention_mask']
    }
    lowerCamelCase_							      =      tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
        args.per_device_eval_batch_size
    )

    # fine optimizer and loss
    lowerCamelCase_							      =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
    lowerCamelCase_							      =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    lowerCamelCase_							      =      [tf.keras.metrics.SparseCategoricalAccuracy()]
    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

    lowerCamelCase_							      =      time.time()
    lowerCamelCase_							      =      model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
    lowerCamelCase_							      =      time.time() - start_train_time

    logger.info('''*** Train ***''')
    logger.info(F"""train_runtime = {end_train_time}""")
    for key, value in train_results.history.items():
        logger.info(F"""  {key} = {value}""")








'''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()


lowerCamelCase_							      =      logging.getLogger(__name__)

@dataclass
class      _UpperCAmelCase    :




   """simple docstring"""

   snake_case					       =		field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		)
   snake_case					       =		field(
       default=snake_case_							,					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=snake_case_							,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
   snake_case					       =		field(default=snake_case_							,					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=snake_case_							,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,					)





@dataclass
class      _UpperCAmelCase    :




   """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=1_28							,					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=snake_case_							,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def       __lowercase		(    ) ->		Any:

      '''simple docstring'''


      _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(__lowercase     ,						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"     ,						__lowercase )

      # Prepare Token Classification task
      _A      =			token_classification_task.get_labels(data_args.labels )
      _A      =			dict(enumerate(__lowercase ) )
      _A      =			len(__lowercase )

      # 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=__lowercase     ,						idalabel=__lowercase     ,						labelaid={label: i for i, label in enumerate(__lowercase )}     ,						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=__lowercase     ,						cache_dir=model_args.cache_dir     ,						)

      # Get datasets
      _A      =			(
          TFTokenClassificationDataset(
              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
      )
      _A      =			(
          TFTokenClassificationDataset(
              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]]:
            _A      =			np.argmax(__lowercase     ,						axis=2 )
            _A  ,			_A      =			preds.shape
            _A      =			[[] for _ in range(__lowercase )]
            _A      =			[[] for _ in range(__lowercase )]

            for i in range(__lowercase ):
                  for j in range(__lowercase ):
                        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(__lowercase ) -> Dict:
            _A  ,			_A      =			align_predictions(p.predictions     ,						p.label_ids )

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

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

      # 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(__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:
            _A      =			TFTokenClassificationDataset(
                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     ,						)

            _A  ,			_A  ,			_A      =			trainer.predict(test_dataset.get_dataset() )
            _A  ,			_A      =			align_predictions(__lowercase     ,						__lowercase )
            _A      =			classification_report(__lowercase     ,						__lowercase )

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

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

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

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

            with open(__lowercase     ,						"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(__lowercase )

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

      return results


if __name__ == "__main__":
    main()





