





'''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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = None							,	)						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       {}

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

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

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

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

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

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

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

   __SCREAMING_SNAKE_CASE      							=       (
       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:
      __SCREAMING_SNAKE_CASE      							=       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

   return train_ds, val_ds, test_ds, labelaid


a        =						logging.getLogger(__name__)







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







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





def 						__magic_name__       (							)						->					Dict:

   '''simple docstring'''







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

   __SCREAMING_SNAKE_CASE      							=       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							,	)

   __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       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							,	)

   __SCREAMING_SNAKE_CASE      							=       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():
      __SCREAMING_SNAKE_CASE      							=       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:
      __SCREAMING_SNAKE_CASE      							=       np.argmax(p.predictions							,	axis=1      )

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

   # Initialize our Trainer
   __SCREAMING_SNAKE_CASE      							=       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
   __SCREAMING_SNAKE_CASE      							=       {}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""      )
      __SCREAMING_SNAKE_CASE      							=       trainer.evaluate()
      __SCREAMING_SNAKE_CASE      							=       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()












'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable

a        =						list[list[float | int]]

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Matrix:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       [[0 for _ in range(size + 1      )] for _ in range(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   for row in range(__UpperCAmelCase      ):
      for col in range(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       matrix[row][col]

      __SCREAMING_SNAKE_CASE      							=       vector[row][0]

   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       0
   while row < size and col < size:
      # pivoting
      __SCREAMING_SNAKE_CASE      							=       max((abs(augmented[rowa][col]      ), rowa) for rowa in range(__UpperCAmelCase							,	__UpperCAmelCase      )      )[
          1
      ]
      if augmented[pivot_row][col] == 0:
         col += 1
         continue
      else:
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       augmented[pivot_row], augmented[row]

      for rowa in range(row + 1							,	__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       augmented[rowa][col] / augmented[row][col]
         __SCREAMING_SNAKE_CASE      							=       0
         for cola in range(col + 1							,	size + 1      ):
            augmented[rowa][cola] -= augmented[row][cola] * ratio

      row += 1
      col += 1

   # back substitution
   for col in range(1							,	__UpperCAmelCase      ):
      for row in range(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       augmented[row][col] / augmented[col][col]
         for cola in range(__UpperCAmelCase							,	size + 1      ):
            augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
   return [
       [round(augmented[row][size] / augmented[row][row]							,	10      )] for row in range(__UpperCAmelCase      )
   ]

def 						__magic_name__       (							__UpperCAmelCase      )						->					Callable[[int], int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       [[0 for _ in range(__UpperCAmelCase      )] for _ in range(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       [[0] for _ in range(__UpperCAmelCase      )]
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   for x_val, y_val in enumerate(__UpperCAmelCase      ):
      for col in range(__UpperCAmelCase      ):
         __SCREAMING_SNAKE_CASE      							=       (x_val + 1) ** (size - col - 1)
      __SCREAMING_SNAKE_CASE      							=       y_val

   __SCREAMING_SNAKE_CASE      							=       solve(__UpperCAmelCase							,	__UpperCAmelCase      )

   def interpolated_func(__UpperCAmelCase      ) -> int:
      return sum(
          round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
          for x_val in range(__UpperCAmelCase      )      )

   return interpolated_func

def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   return (
       1
       - variable
       + variable**2
       - variable**3
       + variable**4
       - variable**5
       + variable**6
       - variable**7
       + variable**8
       - variable**9
       + variable**10
   )

def 						__magic_name__       (							__UpperCAmelCase = question_function							,	__UpperCAmelCase = 10      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       [func(__UpperCAmelCase      ) for x_val in range(1							,	order + 1      )]

   __SCREAMING_SNAKE_CASE      							=       [
       interpolate(data_points[:max_coeff]      ) for max_coeff in range(1							,	order + 1      )
   ]

   __SCREAMING_SNAKE_CASE      							=       0
   __SCREAMING_SNAKE_CASE      							=       42
   __SCREAMING_SNAKE_CASE      							=       42

   for poly in polynomials:
      __SCREAMING_SNAKE_CASE      							=       1
      while func(__UpperCAmelCase      ) == poly(__UpperCAmelCase      ):
         x_val += 1

      ret += poly(__UpperCAmelCase      )

   return ret


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





