



'''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      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__ = None						,				):
   '''simple docstring'''





   A_     :						Any     =					{}

   if train_file is not None:
      A_     :						Tuple     =					[train_file]
   if eval_file is not None:
      A_     :						Tuple     =					[eval_file]
   if test_file is not None:
      A_     :						Tuple     =					[test_file]

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

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

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

   def gen_test():
      for ex in transformed_ds[datasets.Split.TEST]:
         A_     :						str     =					{k: v for k, v in ex.items() if k in input_names}
         A_     :						List[str]     =					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_     :						Dict     =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

   A_     :						Optional[Any]     =					(
       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_     :						Optional[int]     =					(
       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_     :						int     =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

   return train_ds, val_ds, test_ds, labelaid


lowerCamelCase							:Dict       = logging.getLogger(__name__)







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







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






def      a      (			):
   '''simple docstring'''





   A_     :						Optional[int]     =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
   A_     :						str     =					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_     :						Union[str, 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_     :						Dict     =					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_     :						List[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_     :						Optional[int]     =					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(lowerCamelCase__   ) -> Dict:
      A_     :						Any     =					np.argmax(p.predictions						,				axis=1   )

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

   # Initialize our Trainer
   A_     :						str     =					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_     :						List[Any]     =					{}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""   )
      A_     :						Union[str, Any]     =					trainer.evaluate()
      A_     :						str     =					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()




'''simple docstring'''




def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if density <= 0:
						raise ValueError("""Impossible fluid density"""   )
			if bulk_modulus <= 0:
						raise ValueError("""Impossible bulk modulus"""   )

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()