






'''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 	lowerCAmelCase__      (       SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__ = None							,		):
 __a     :						Optional[Any]          =    {}

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

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

 if len(lowerCamelCase__		) == 1:
  for k in files.keys():
   __a     :						Dict          =    ds[k].map(
       lambda SCREAMING_SNAKE_CASE__		: tokenizer.batch_encode_plus(
           example[features_name[0]]							,		truncation=lowerCamelCase__							,		max_length=lowerCamelCase__							,		padding='max_length'		)							,		batched=lowerCamelCase__							,		)
 elif len(lowerCamelCase__		) == 2:
  for k in files.keys():
   __a     :						Optional[int]          =    ds[k].map(
       lambda SCREAMING_SNAKE_CASE__		: tokenizer.batch_encode_plus(
           (example[features_name[0]], example[features_name[1]])							,		truncation=lowerCamelCase__							,		max_length=lowerCamelCase__							,		padding='max_length'							,		)							,		batched=lowerCamelCase__							,		)

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

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

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

 __a     :						List[str]          =    (
     tf.data.Dataset.from_generator(
         lowerCamelCase__							,		({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     :						Union[str, Any]          =    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

 __a     :						List[str]          =    (
     tf.data.Dataset.from_generator(
         lowerCamelCase__							,		({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     :						Optional[Any]          =    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

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

 return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE_					   =				logging.getLogger(__name__)




@dataclass
class 				lowerCAmelCase       :







   """simple docstring"""







   _A							= field(metadata={'help': 'Which column contains the label'} )
   _A							= field(default=snake_case_				,   metadata={'help': 'The path of the training file'} )
   _A							= field(default=snake_case_				,   metadata={'help': 'The path of the development file'} )
   _A							= field(default=snake_case_				,   metadata={'help': 'The path of the test file'} )
   _A							= 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.'
           )
       }				,   )
   _A							= field(
       default=snake_case_				,   metadata={'help': 'Overwrite the cached training and evaluation sets'} )




@dataclass
class 				lowerCAmelCase       :







   """simple docstring"""







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






def 	lowerCAmelCase__      (       ):
 # 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     :						Optional[Any]          =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
 __a      ,     __a      ,     __a     :						Any          =    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     :						Any          =    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     :						Optional[Any]          =    get_tfds(
     train_file=data_args.train_file							,		eval_file=data_args.dev_file							,		test_file=data_args.test_file							,		tokenizer=lowerCamelCase__							,		label_column_id=data_args.label_column_id							,		max_seq_length=data_args.max_seq_length							,		)

 __a     :						Optional[Any]          =    AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,		num_labels=len(lowerCamelCase__		)							,		labelaid=lowerCamelCase__							,		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     :						Any          =    TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path							,		from_pt=bool('.bin' in model_args.model_name_or_path		)							,		config=lowerCamelCase__							,		cache_dir=model_args.cache_dir							,		)

 def compute_metrics(SCREAMING_SNAKE_CASE__		) -> Dict:
  __a     :						List[str]          =    np.argmax(p.predictions							,		axis=1		)

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

 # Initialize our Trainer
 __a     :						Tuple          =    TFTrainer(
     model=lowerCamelCase__							,		args=lowerCamelCase__							,		train_dataset=lowerCamelCase__							,		eval_dataset=lowerCamelCase__							,		compute_metrics=lowerCamelCase__							,		)

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

 # Evaluation
 __a     :						int          =    {}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'		)
  __a     :						Optional[int]          =    trainer.evaluate()
  __a     :						Any          =    os.path.join(training_args.output_dir							,		'eval_results.txt'		)

  with open(lowerCamelCase__							,		'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(lowerCamelCase__		)

 return results


if __name__ == "__main__":
  main()






import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True





def      lowerCamelCase_					(							lowerCamelCase__ = 1_0_0_0_1       ):

				try:
								lowerCamelCase_         =     int(lowerCamelCase__       )
				except (TypeError, ValueError):
								raise TypeError("Parameter nth must be int or castable to int."       ) from None
				if nth <= 0:
								raise ValueError("Parameter nth must be greater than or equal to one."       )
				lowerCamelCase_         =     []
				lowerCamelCase_         =     2
				while len(lowerCamelCase__       ) < nth:
								if is_prime(lowerCamelCase__       ):
												primes.append(lowerCamelCase__       )
												num += 1
								else:
												num += 1
				return primes[len(lowerCamelCase__       ) - 1]


if __name__ == "__main__":
			print(F"""{solution() = }""")


