





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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = None      ,  )    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						List[str]        =							{}

   if train_file is not None:
      lowerCamelCase__       :						str        =							[train_file]
   if eval_file is not None:
      lowerCamelCase__       :						List[Any]        =							[eval_file]
   if test_file is not None:
      lowerCamelCase__       :						int        =							[test_file]

   lowerCamelCase__       :						str        =							datasets.load_dataset('''csv'''      ,  data_files=UpperCAmelCase					)
   lowerCamelCase__       :						int        =							list(ds[list(files.keys()					)[0]].features.keys()					)
   lowerCamelCase__       :						Union[str, Any]        =							features_name.pop(UpperCAmelCase					)
   lowerCamelCase__       :						int        =							list(set(ds[list(files.keys()					)[0]][label_name]					)					)
   lowerCamelCase__       :						int        =							{label: i for i, label in enumerate(UpperCAmelCase					)}
   lowerCamelCase__       :						Any        =							tokenizer.model_input_names
   lowerCamelCase__       :						List[Any]        =							{}

   if len(UpperCAmelCase					) == 1:
      for k in files.keys():
         lowerCamelCase__       :						Optional[int]        =							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():
         lowerCamelCase__       :						int        =							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]:
         lowerCamelCase__       :						Optional[Any]        =							{k: v for k, v in ex.items() if k in input_names}
         lowerCamelCase__       :						List[Any]        =							labelaid[ex[label_name]]
         yield (d, label)

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

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

   lowerCamelCase__       :						Dict        =							(
       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:
      lowerCamelCase__       :						Optional[int]        =							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]					)					)					)

   lowerCamelCase__       :						str        =							(
       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:
      lowerCamelCase__       :						Tuple        =							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]					)					)					)

   lowerCamelCase__       :						str        =							(
       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:
      lowerCamelCase__       :						List[Any]        =							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]					)					)					)

   return train_ds, val_ds, test_ds, labelaid


_A :   Dict  			=  logging.getLogger(__name__)


@dataclass
class        __SCREAMING_SNAKE_CASE      :
       _UpperCAmelCase				:  int			   =   field(metadata={"help": "Which column contains the label"} )
       _UpperCAmelCase				:  str			   =   field(default=lowerCAmelCase_					,metadata={"help": "The path of the training file"} )
       _UpperCAmelCase				:  Optional[str]			   =   field(default=lowerCAmelCase_					,metadata={"help": "The path of the development file"} )
       _UpperCAmelCase				:  Optional[str]			   =   field(default=lowerCAmelCase_					,metadata={"help": "The path of the test file"} )
       _UpperCAmelCase				:  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."
               )
           }					,)
       _UpperCAmelCase				:  bool			   =   field(
           default=lowerCAmelCase_					,metadata={"help": "Overwrite the cached training and evaluation sets"} )


@dataclass
class        __SCREAMING_SNAKE_CASE      :
       _UpperCAmelCase				:  str			   =   field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
       _UpperCAmelCase				:  Optional[str]			   =   field(
           default=lowerCAmelCase_					,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
       _UpperCAmelCase				:  Optional[str]			   =   field(
           default=lowerCAmelCase_					,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
       _UpperCAmelCase				:  bool			   =   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				:  Optional[str]			   =   field(
           default=lowerCAmelCase_					,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,)



def 	_a						(   )    ->    str:







   """simple docstring"""
   # 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__       :						Optional[Any]        =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
   lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							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.

   lowerCamelCase__       :						Dict        =							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      ,  )

   lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       :						str        =							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      ,  )

   lowerCamelCase__       :						Tuple        =							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():
      lowerCamelCase__       :						Any        =							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:
      lowerCamelCase__       :						Any        =							np.argmax(p.predictions      ,  axis=1					)

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

   # Initialize our Trainer
   lowerCamelCase__       :						List[str]        =							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
   lowerCamelCase__       :						Optional[int]        =							{}
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''					)
      lowerCamelCase__       :						Tuple        =							trainer.evaluate()
      lowerCamelCase__       :						Optional[int]        =							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()










def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if b == 0:
      return 1
   if (b % 2) == 0:
      return actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)
   else:
      return a * actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   if b < 0:
      return 1 / actual_power(UpperCAmelCase      ,  UpperCAmelCase					)
   return actual_power(UpperCAmelCase      ,  UpperCAmelCase					)


if __name__ == "__main__":
       print(power(-2, -3))



