
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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = None ,		):
      UpperCamelCase__    :   List[Any]					=				{}

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

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

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

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

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

      UpperCamelCase__    :   Optional[Any]					=				(
          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:
            UpperCamelCase__    :   Optional[Any]					=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

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

      UpperCamelCase__    :   int					=				(
          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:
            UpperCamelCase__    :   str					=				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 				_lowerCamelCase :





     """simple docstring"""







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




@dataclass
class 				_lowerCamelCase :





     """simple docstring"""







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


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   str					=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Optional[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.

      UpperCamelCase__    :   Optional[int]					=				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__				,UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				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 ,		)

      UpperCamelCase__    :   List[str]					=				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():
            UpperCamelCase__    :   Optional[int]					=				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:
            UpperCamelCase__    :   Optional[Any]					=				np.argmax(p.predictions ,		axis=1		)

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

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




import math


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   List[str]					=				math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

      return exponent == int(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = 1 / 1_2_3_4_5		):
      UpperCamelCase__    :   Dict					=				0
      UpperCamelCase__    :   Tuple					=				0

      UpperCamelCase__    :   Optional[Any]					=				3
      while True:
            UpperCamelCase__    :   Union[str, Any]					=				(integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(UpperCamelCase__		):
                  UpperCamelCase__    :   int					=				int(UpperCamelCase__		)
                  total_partitions += 1
                  if check_partition_perfect(UpperCamelCase__		):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(UpperCamelCase__		)
            integer += 1


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


