
'''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


a_           =    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  UpperCAmelCase_					(						snake_case__       ):





   def __init__(     self			,							lowercase_ = 1_01):
       snake_case_			:     Optional[int]        =    length





   def __len__(     self):
       return self.length





   def __getitem__(     self			,							lowercase_):
       return i






  class  UpperCAmelCase_					:





   def __call__(     self			,							lowercase_):
       return {"input_ids": torch.tensor(lowercase_), "labels": torch.tensor(lowercase_)}






  class  UpperCAmelCase_					(						nn.Module       ):





   def __init__(     self):
       super().__init__()
       # Add some (unused) params otherwise DDP will complain.
       snake_case_			:     str        =    nn.Linear(1_20			,							80)





   def 							snake_case__		(     self			,							lowercase_			,							lowercase_=None):
       if labels is not None:
           return torch.tensor(0.0			,							device=input_ids.device), input_ids
       else:
           return input_ids

def     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    HfArgumentParser((TrainingArguments,)							)
    sys.argv += ["--output_dir", "./examples"]
    snake_case_			:     Dict        =    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 [1_0_0_1, 2_5_6, 1_5]:
        snake_case_			:     List[Any]        =    DummyDataset(__SCREAMING_SNAKE_CASE							)

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

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

        snake_case_			:     Union[str, Any]        =    trainer.predict(__SCREAMING_SNAKE_CASE							)
        logger.info(p.metrics							)
        if p.metrics["test_success"] is not True:
            logger.error(p.metrics							)
            exit(1							)

        snake_case_			:     Optional[int]        =    2

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

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

        snake_case_			:     str        =    None

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

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    main()


if __name__ == "__main__":
  main()






'''simple docstring'''
import itertools

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE = 1_0_0_0_0_0_0							):
    """simple docstring"""






    snake_case_			:     Optional[int]        =    [1]

    for i in itertools.count(len(__SCREAMING_SNAKE_CASE							)							):
        snake_case_			:     str        =    0
        for j in itertools.count(1							):
            snake_case_			:     List[str]        =    -1 if j % 2 == 0 else +1
            snake_case_			:     str        =    (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(__SCREAMING_SNAKE_CASE							)

    return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




