

"""simple docstring"""






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    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()





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase = None      ,   ) ->      Dict:
 __magic_name__     =      {}

 if train_file is not None:
  __magic_name__     =      [train_file]
 if eval_file is not None:
  __magic_name__     =      [eval_file]
 if test_file is not None:
  __magic_name__     =      [test_file]

 __magic_name__     =      datasets.load_dataset('''csv'''      ,   data_files=__UpperCamelCase						)
 __magic_name__     =      list(ds[list(files.keys()						)[0]].features.keys()						)
 __magic_name__     =      features_name.pop(__UpperCamelCase						)
 __magic_name__     =      list(set(ds[list(files.keys()						)[0]][label_name]						)						)
 __magic_name__     =      {label: i for i, label in enumerate(__UpperCamelCase						)}
 __magic_name__     =      tokenizer.model_input_names
 __magic_name__     =      {}

 if len(__UpperCamelCase						) == 1:
  for k in files.keys():
   __magic_name__     =      ds[k].map(
       lambda __UpperCamelCase						: tokenizer.batch_encode_plus(
           example[features_name[0]]      ,   truncation=__UpperCamelCase      ,   max_length=__UpperCamelCase      ,   padding='''max_length'''						)      ,   batched=__UpperCamelCase      ,   )
 elif len(__UpperCamelCase						) == 2:
  for k in files.keys():
   __magic_name__     =      ds[k].map(
       lambda __UpperCamelCase						: tokenizer.batch_encode_plus(
           (example[features_name[0]], example[features_name[1]])      ,   truncation=__UpperCamelCase      ,   max_length=__UpperCamelCase      ,   padding='''max_length'''      ,   )      ,   batched=__UpperCamelCase      ,   )

 def gen_train():
  for ex in transformed_ds[datasets.Split.TRAIN]:
   __magic_name__     =      {k: v for k, v in ex.items() if k in input_names}
   __magic_name__     =      labelaid[ex[label_name]]
   yield (d, label)

 def gen_val():
  for ex in transformed_ds[datasets.Split.VALIDATION]:
   __magic_name__     =      {k: v for k, v in ex.items() if k in input_names}
   __magic_name__     =      labelaid[ex[label_name]]
   yield (d, label)

 def gen_test():
  for ex in transformed_ds[datasets.Split.TEST]:
   __magic_name__     =      {k: v for k, v in ex.items() if k in input_names}
   __magic_name__     =      labelaid[ex[label_name]]
   yield (d, label)

 __magic_name__     =      (
     tf.data.Dataset.from_generator(
         __UpperCamelCase      ,   ({k: tf.intaa for k in input_names}, tf.intaa)      ,   ({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))      ,   )
     if datasets.Split.TRAIN in transformed_ds
     else None
 )

 if train_ds is not None:
  __magic_name__     =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

 __magic_name__     =      (
     tf.data.Dataset.from_generator(
         __UpperCamelCase      ,   ({k: tf.intaa for k in input_names}, tf.intaa)      ,   ({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))      ,   )
     if datasets.Split.VALIDATION in transformed_ds
     else None
 )

 if val_ds is not None:
  __magic_name__     =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

 __magic_name__     =      (
     tf.data.Dataset.from_generator(
         __UpperCamelCase      ,   ({k: tf.intaa for k in input_names}, tf.intaa)      ,   ({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))      ,   )
     if datasets.Split.TEST in transformed_ds
     else None
 )

 if test_ds is not None:
  __magic_name__     =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]						)						)						)

 return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase										=		logging.getLogger(__name__)
@dataclass
class        _lowercase					:
     _lowerCamelCase     =       field(metadata={'''help''': '''Which column contains the label'''}     )
     _lowerCamelCase     =       field(default=__UpperCAmelCase   , metadata={'''help''': '''The path of the training file'''}     )
     _lowerCamelCase     =       field(default=__UpperCAmelCase   , metadata={'''help''': '''The path of the development file'''}     )
     _lowerCamelCase     =       field(default=__UpperCAmelCase   , metadata={'''help''': '''The path of the test file'''}     )
     _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=__UpperCAmelCase   , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )


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





def 	lowercase      (   ) ->      Union[str, Any]:
 # 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.
 __magic_name__     =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
 __magic_name__   ,						__magic_name__   ,						__magic_name__     =      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.'''						)

 # 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(
     f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1						)}, '''
     f'''16-bits training: {training_args.fpaa}'''						)
 logger.info(f'''Training/evaluation parameters {training_args}'''						)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 __magic_name__     =      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      ,   )

 __magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__     =      get_tfds(
     train_file=data_args.train_file      ,   eval_file=data_args.dev_file      ,   test_file=data_args.test_file      ,   tokenizer=__UpperCamelCase      ,   label_column_id=data_args.label_column_id      ,   max_seq_length=data_args.max_seq_length      ,   )

 __magic_name__     =      AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,   num_labels=len(__UpperCamelCase						)      ,   labelaid=__UpperCamelCase      ,   idalabel={id: label for label, id in labelaid.items()}      ,   finetuning_task='''text-classification'''      ,   cache_dir=model_args.cache_dir      ,   )

 with training_args.strategy.scope():
  __magic_name__     =      TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path      ,   from_pt=bool('''.bin''' in model_args.model_name_or_path						)      ,   config=__UpperCamelCase      ,   cache_dir=model_args.cache_dir      ,   )

 def compute_metrics(__UpperCamelCase						) -> Dict:
  __magic_name__     =      np.argmax(p.predictions      ,   axis=1						)

  return {"acc": (preds == p.label_ids).mean()}

 # Initialize our Trainer
 __magic_name__     =      TFTrainer(
     model=__UpperCamelCase      ,   args=__UpperCamelCase      ,   train_dataset=__UpperCamelCase      ,   eval_dataset=__UpperCamelCase      ,   compute_metrics=__UpperCamelCase      ,   )

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir						)

 # Evaluation
 __magic_name__     =      {}
 if training_args.do_eval:
  logger.info('''*** Evaluate ***'''						)
  __magic_name__     =      trainer.evaluate()
  __magic_name__     =      os.path.join(training_args.output_dir      ,   '''eval_results.txt'''						)

  with open(__UpperCamelCase      ,   '''w'''						) as writer:
   logger.info('''***** Eval results *****'''						)

   for key, value in result.items():
    logger.info(f'''  {key} = {value}'''						)
    writer.write(f'''{key} = {value}\n'''						)

   results.update(__UpperCamelCase						)

 return results


if __name__ == "__main__":
 main()




"""simple docstring"""






class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      size
      __magic_name__     =      [0] * size
      __magic_name__     =      [0] * size







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return index | (index + 1)







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return (index & (index + 1)) - 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      value
      while index < self.size:
       __magic_name__     =      self.get_prev(UpperCamelCase_       ) + 1
       if current_left_border == index:
        __magic_name__     =      value
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      self.get_next(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      right -= 1  # Because of right is exclusive
      __magic_name__     =      0
      while left <= right:
       __magic_name__     =      self.get_prev(UpperCamelCase_       )
       if left <= current_left:
        __magic_name__     =      max(UpperCamelCase_ ,						self.tree[right]       )
        __magic_name__     =      current_left
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						self.arr[right]       )
        right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()

