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		(			_A							,				_A							,				_A							,				_A							,				_A							,				_A = None							,				):



 """simple docstring"""


 __a       =  {}

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

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

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

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

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

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

 __a       =  (
     tf.data.Dataset.from_generator(
         _A							,				({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:
  __a       =  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]					)					)					)

 __a       =  (
     tf.data.Dataset.from_generator(
         _A							,				({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:
  __a       =  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]					)					)					)

 __a       =  (
     tf.data.Dataset.from_generator(
         _A							,				({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:
  __a       =  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]					)					)					)

 return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE :	Any							     =		logging.getLogger(__name__)
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(metadata={"""help""": """Which column contains the label"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """The path of the training file"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """The path of the development file"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       metadata={"""help""": """The path of the test file"""} )
  _SCREAMING_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."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(default=a_			,       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.
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,       )



def   __A		(			):



 """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."					)

 # 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.

 __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							,				)

 __a       ,     __a       ,     __a       ,     __a       =  get_tfds(
     train_file=data_args.train_file							,				eval_file=data_args.dev_file							,				test_file=data_args.test_file							,				tokenizer=_A							,				label_column_id=data_args.label_column_id							,				max_seq_length=data_args.max_seq_length							,				)

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

 with training_args.strategy.scope():
  __a       =  TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path							,				from_pt=bool(".bin" in model_args.model_name_or_path					)							,				config=_A							,				cache_dir=model_args.cache_dir							,				)

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

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

 # Initialize our Trainer
 __a       =  TFTrainer(
     model=_A							,				args=_A							,				train_dataset=_A							,				eval_dataset=_A							,				compute_metrics=_A							,				)

 # 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(_A							,				"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(_A					)

 return results


if __name__ == "__main__":
  main()



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


