





'''simple docstring'''

# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


lowercase_         =			logging.get_logger(__name__)


if is_torch_available():
      import torch
      from torch import nn
      from torch.utils.data import Dataset

      from transformers import Trainer


      class  a_					(    snake_case_	):

          '''simple docstring'''


          def __init__(		self			,		A = 101	)		->       Union[str, Any]:
              _SCREAMING_SNAKE_CASE     =      length


          def __len__(		self	)		->       List[str]:
              return self.length





          def __getitem__(		self			,		A	)		->       int:
              return i




      class  a_					:

          '''simple docstring'''


          def __call__(		self			,		A	)		->       int:
              return {"input_ids": torch.tensor(A	), "labels": torch.tensor(A	)}




      class  a_					(    nn.Module	):

          '''simple docstring'''


          def __init__(		self	)		->       List[Any]:
              super().__init__()
              # Add some (unused) params otherwise DDP will complain.
              _SCREAMING_SNAKE_CASE     =      nn.Linear(120			,		80	)





          def       snake_case_(		self			,		A			,		A=None	)		->       Tuple:
              if labels is not None:
                  return torch.tensor(0.0			,		device=input_ids.device	), input_ids
              else:
                  return input_ids





def lowerCamelCase						(					)						->int:
    _SCREAMING_SNAKE_CASE     =      HfArgumentParser((TrainingArguments,)   )
    sys.argv += ["--output_dir", "./examples"]
    _SCREAMING_SNAKE_CASE     =      parser.parse_args_into_dataclasses()[0]

    logger.warning(
        F'Process rank: {training_args.local_rank}, device: {training_args.device}, '
        F'tpu_num_cores: {training_args.tpu_num_cores}' ,	)

    # Essentially, what we want to verify in the distributed case is
    # that we get all samples back, in the right order.
    # (this is crucial for prediction for instance)
    for dataset_length in [1001, 256, 15]:
        _SCREAMING_SNAKE_CASE     =      DummyDataset(__lowerCamelCase   )

        def compute_metrics(__lowerCamelCase			:   EvalPrediction   ) -> Dict:
            _SCREAMING_SNAKE_CASE     =      list(range(len(__lowerCamelCase   )   )   )
            _SCREAMING_SNAKE_CASE     =      p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
            return {"success": success}

        _SCREAMING_SNAKE_CASE     =      Trainer(
            model=DummyModel() ,	args=__lowerCamelCase ,	data_collator=DummyDataCollator() ,	eval_dataset=__lowerCamelCase ,	compute_metrics=__lowerCamelCase ,	)
        _SCREAMING_SNAKE_CASE     =      trainer.evaluate()
        logger.info(__lowerCamelCase   )
        if metrics["eval_success"] is not True:
            logger.error(__lowerCamelCase   )
            exit(1   )

        _SCREAMING_SNAKE_CASE     =      trainer.predict(__lowerCamelCase   )
        logger.info(p.metrics   )
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics   )
            exit(1   )

        _SCREAMING_SNAKE_CASE     =      2

        _SCREAMING_SNAKE_CASE     =      trainer.evaluate()
        logger.info(__lowerCamelCase   )
        if metrics["eval_success"] is not True:
            logger.error(__lowerCamelCase   )
            exit(1   )

        _SCREAMING_SNAKE_CASE     =      trainer.predict(__lowerCamelCase   )
        logger.info(p.metrics   )
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics   )
            exit(1   )

        _SCREAMING_SNAKE_CASE     =      None

    logger.info("""🔥 All distributed tests successful"""   )





def lowerCamelCase						(					__lowerCamelCase			:   List[Any]   )						->List[str]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
      main()










'''simple docstring'''

def lowerCamelCase						(					__lowerCamelCase			:   int   )						->int:
    barcode //= 10  # exclude the last digit
    _SCREAMING_SNAKE_CASE     =      False
    _SCREAMING_SNAKE_CASE     =      0

    # extract and check each digit
    while barcode != 0:
        _SCREAMING_SNAKE_CASE     =      1 if checker else 3
        s += mult * (barcode % 10)
        barcode //= 10
        _SCREAMING_SNAKE_CASE     =      not checker

    return (10 - (s % 10)) % 10





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->bool:
    return len(str(__lowerCamelCase   )   ) == 13 and get_check_digit(__lowerCamelCase   ) == barcode % 10





def lowerCamelCase						(					__lowerCamelCase			:   str   )						->int:
    if str(__lowerCamelCase   ).isalpha():
        _SCREAMING_SNAKE_CASE     =      F'Barcode \'{barcode}\' has alphabetic characters.'
        raise ValueError(__lowerCamelCase   )
    elif int(__lowerCamelCase   ) < 0:
        raise ValueError("""The entered barcode has a negative value. Try again."""   )
    else:
        return int(__lowerCamelCase   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase_         =			get_barcode(input("""Barcode: """).strip())

      if is_valid(barcode):
            print(f"""'{barcode}' is a valid barcode.""")
      else:
            print(f"""'{barcode}' is NOT a valid barcode.""")



