
# 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


UpperCAmelCase__						:	Tuple	     = 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  (		SCREAMING_SNAKE_CASE__  ):





      '''simple docstring'''


      def __init__(   self	:				List[str]     ,     lowerCAmelCase_	:				int = 1_0_1	):

         """simple docstring"""
         _A:    Dict						     =			length


      def __len__(   self	:				Optional[int]	):

         """simple docstring"""
         return self.length


      def __getitem__(   self	:				List[str]     ,     lowerCAmelCase_	:				str	):

         """simple docstring"""
         return i






   class UpperCAmelCase  :





      '''simple docstring'''


      def __call__(   self	:				List[str]     ,     lowerCAmelCase_	:				Union[str, Any]	):

         """simple docstring"""
         return {"input_ids": torch.tensor(lowerCAmelCase_	), "labels": torch.tensor(lowerCAmelCase_	)}






   class UpperCAmelCase  (		nn.Module  ):





      '''simple docstring'''


      def __init__(   self	:				Union[str, Any]	):

         """simple docstring"""
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         _A:    int						     =			nn.Linear(1_2_0     ,     8_0	)


      def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				Optional[Any]     ,     lowerCAmelCase_	:				Union[str, Any]=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__				(   ) ->  Dict:
   _A:    Dict						     =			HfArgumentParser((TrainingArguments,) )
   sys.argv += ["--output_dir", "./examples"]
   _A:    List[Any]						     =			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 [10_01, 2_56, 15]:
      _A:    Dict						     =			DummyDataset(a )

      def compute_metrics(a ) -> Dict:
         _A:    Optional[Any]						     =			list(range(len(a ) ) )
         _A:    Union[str, Any]						     =			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         return {"success": success}

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

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

      _A:    int						     =			2

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

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

      _A:    Dict						     =			None

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






def     lowerCamelCase__				(   a ) ->  Optional[Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()





import math as m






def     lowerCamelCase__				(   a						,						a						,						a=0.001 ) ->  Optional[int]:
   return (f(a + h ) - f(a - h )) / (2 * h)






def     lowerCamelCase__				(   a						,						a=0						,						a=1_00						,						a=0.0001						,						a=1E-6						,						a=False ) ->  List[Any]:
   _A:    Optional[Any]						     =			xa  # set the initial guess
   _A:    Dict						     =			[a]
   _A:    Tuple						     =			abs(f(a ) )
   _A:    Tuple						     =			lambda a : calc_derivative(a						,						a						,						h=a )  # noqa: E731  Derivative of f(x)
   for _ in range(a ):
      if fa(a ) == 0:
         raise ValueError('''No converging solution found''' )
      _A:    Any						     =			a - f(a ) / fa(a )  # Calculate the next estimate
      if logsteps:
         steps.append(a )
      if error < maxerror:
         break
   else:
      raise ValueError('''Iteration limit reached, no converging solution found''' )
   if logsteps:
      # If logstep is true, then log intermediate steps
      return a, error, steps
   return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   UpperCAmelCase__						:	Optional[Any]	     = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__						:	str	     = newton_raphson(
       f, xa=10, maxiter=1000, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")



