


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		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase = None					,  )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			{}

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

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

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

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

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

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

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

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

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

     return train_ds, val_ds, test_ds, labelaid


__snake_case   							=						logging.getLogger(__name__)



@dataclass
class 	__lowerCamelCase			:
       _lowercase											=     field(metadata={"""help""": """Which column contains the label"""}						)
       _lowercase											=     field(default=__SCREAMING_SNAKE_CASE				,      metadata={"""help""": """The path of the training file"""}						)
       _lowercase											=     field(default=__SCREAMING_SNAKE_CASE				,      metadata={"""help""": """The path of the development file"""}						)
       _lowercase											=     field(default=__SCREAMING_SNAKE_CASE				,      metadata={"""help""": """The path of the test file"""}						)
       _lowercase											=     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."""
               )
           }				,      )
       _lowercase											=     field(
           default=__SCREAMING_SNAKE_CASE				,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)



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






def 		_A		(		)					-> List[Any]:


     """simple docstring"""


     __UpperCamelCase =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
     __UpperCamelCase =			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.

     __UpperCamelCase =			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					,  )

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

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

     with training_args.strategy.scope():
          __UpperCamelCase =			TFAutoModelForSequenceClassification.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					,  )

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

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

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

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

     # Evaluation
     __UpperCamelCase =			{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***' )
          __UpperCamelCase =			trainer.evaluate()
          __UpperCamelCase =			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(f'''  {key} = {value}''' )
                    writer.write(f'''{key} = {value}\n''' )

               results.update(_lowercase )

     return results


if __name__ == "__main__":
    main()









"""simple docstring"""


from math import ceil, sqrt

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 100_0000     ):
						UpperCamelCase				:		int				   =			0

						for outer_width in range(3							,     (limit // 4) + 2     ):
												if outer_width**2 > limit:
																		UpperCamelCase				:		Optional[Any]				   =			max(ceil(sqrt(outer_width**2 - limit     )     )							,     1     )
												else:
																		UpperCamelCase				:		str				   =			1
												if (outer_width - hole_width_lower_bound) % 2:
																		hole_width_lower_bound += 1

												answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

						return answer


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



