


import sys
from typing import Dict

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


A_    :					List[str]             =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        lowercase_				( snake_case_):






        """simple docstring"""







        def __init__(   self			,    _UpperCAmelCase = 101		):







              """simple docstring"""


              a_					=		length


        def __len__(   self		):







              """simple docstring"""


              return self.length






        def __getitem__(   self			,    _UpperCAmelCase		):







              """simple docstring"""


              return i





 class        lowercase_				:






        """simple docstring"""







        def __call__(   self			,    _UpperCAmelCase		):







              """simple docstring"""


              return {"input_ids": torch.tensor(_UpperCAmelCase		), "labels": torch.tensor(_UpperCAmelCase		)}





 class        lowercase_				( nn.Module):






        """simple docstring"""







        def __init__(   self		):







              """simple docstring"""


              super().__init__()
              # Add some (unused) params otherwise DDP will complain.
              a_					=		nn.Linear(120			,    80		)






        def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase=None		):







              """simple docstring"""


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




def   lowerCamelCase_       (					):



      """simple docstring"""




      a_					=		HfArgumentParser((TrainingArguments,)      )
      sys.argv += ["--output_dir", "./examples"]
      a_					=		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_001, 256, 15]:
            a_					=		DummyDataset(__lowerCamelCase      )

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

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

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

            a_					=		2

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

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

            a_					=		None

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




def   lowerCamelCase_       (					UpperCAmelCase__      ):



      """simple docstring"""




      main()


if __name__ == "__main__":
 main()



from collections.abc import Callable

A_    :					Any             =Callable[[float], float]  # type alias for a real -> real function




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    ):



						"""simple docstring"""




						a_					=		float(UpperCAmelCase__      )
						while True:
												try:
																		a_					=		prev_guess - function(UpperCAmelCase__      ) / derivative(UpperCAmelCase__      )
												except ZeroDivisionError:
																		raise ZeroDivisionError("""Could not find root"""      ) from None
												if abs(prev_guess - next_guess      ) < 10**-5:
																		return next_guess
												a_					=		next_guess




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						return (x**3) - (2 * x) - 5




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						return 3 * (x**2) - 2


if __name__ == "__main__":
	print(newton(f, fa, 3))