





"""simple docstring"""





import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
  import torch_xla.debug.metrics as met




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      def __init__(							self       :      Tuple						,				*__lowercase       :      Any						,				__lowercase       :      Optional[Any]=None						,				__lowercase       :      Tuple=None						,				**__lowercase       :      Any ):


             '''simple docstring'''




             super().__init__(*__lowercase						,				**__lowercase )
             __UpperCAmelCase    :						Tuple       =						eval_examples
             __UpperCAmelCase    :						Dict       =						post_process_function

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      Optional[Dataset] = None						,				__lowercase       :      str=None						,				__lowercase       :      Optional[List[str]] = None						,				__lowercase       :      str = "eval"						,				**__lowercase       :      Any						,				):


             '''simple docstring'''




             __UpperCAmelCase    :						List[str]       =						gen_kwargs.copy()
             __UpperCAmelCase    :						int       =						(
                 gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''' ) is not None else self.args.generation_max_length
             )
             __UpperCAmelCase    :						Dict       =						(
                 gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''' ) is not None else self.args.generation_num_beams
             )
             __UpperCAmelCase    :						Dict       =						gen_kwargs

             __UpperCAmelCase    :						List[str]       =						self.eval_dataset if eval_dataset is None else eval_dataset
             __UpperCAmelCase    :						str       =						self.get_eval_dataloader(__lowercase )
             __UpperCAmelCase    :						Union[str, Any]       =						self.eval_examples if eval_examples is None else eval_examples

             # Temporarily disable metric computation, we will do it in the loop here.
             __UpperCAmelCase    :						List[str]       =						self.compute_metrics
             __UpperCAmelCase    :						Tuple       =						None
             __UpperCAmelCase    :						str       =						time.time()
             __UpperCAmelCase    :						Optional[int]       =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
             try:
                    __UpperCAmelCase    :						Union[str, Any]       =						eval_loop(
                        __lowercase						,				description='''Evaluation'''						,				prediction_loss_only=True if compute_metrics is None else None						,				ignore_keys=__lowercase						,				metric_key_prefix=__lowercase						,				)
             finally:
                    __UpperCAmelCase    :						Any       =						compute_metrics
             __UpperCAmelCase    :						Tuple       =						self.args.eval_batch_size * self.args.world_size
             if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                    start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
             output.metrics.update(
                 speed_metrics(
                     __lowercase						,				__lowercase						,				num_samples=output.num_samples						,				num_steps=math.ceil(output.num_samples / total_batch_size )						,				) )

             if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
                    # Only the main node write the results by default
                    __UpperCAmelCase    :						str       =						self.post_process_function(__lowercase						,				__lowercase						,				__lowercase )
                    __UpperCAmelCase    :						Union[str, Any]       =						self.compute_metrics(__lowercase )

                    # Prefix all keys with metric_key_prefix + '_'
                    for key in list(metrics.keys() ):
                           if not key.startswith(f'''{metric_key_prefix}_''' ):
                                  __UpperCAmelCase    :						List[Any]       =						metrics.pop(__lowercase )

                    metrics.update(output.metrics )
             else:
                    __UpperCAmelCase    :						Optional[int]       =						output.metrics

             if self.args.should_log:
                    # Only the main node log the results by default
                    self.log(__lowercase )

             if self.args.tpu_metrics_debug or self.args.debug:
                    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
                    xm.master_print(met.metrics_report() )

             __UpperCAmelCase    :						str       =						self.callback_handler.on_evaluate(self.args						,				self.state						,				self.control						,				__lowercase )
             return metrics







      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      Tuple						,				__lowercase       :      Dict						,				__lowercase       :      List[Any]=None						,				__lowercase       :      str = "test"						,				**__lowercase       :      Optional[int] ):


             '''simple docstring'''




             __UpperCAmelCase    :						Union[str, Any]       =						gen_kwargs.copy()

             __UpperCAmelCase    :						Optional[int]       =						self.get_test_dataloader(__lowercase )

             # Temporarily disable metric computation, we will do it in the loop here.
             __UpperCAmelCase    :						Union[str, Any]       =						self.compute_metrics
             __UpperCAmelCase    :						List[Any]       =						None
             __UpperCAmelCase    :						Optional[int]       =						time.time()
             __UpperCAmelCase    :						List[Any]       =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
             try:
                    __UpperCAmelCase    :						List[str]       =						eval_loop(
                        __lowercase						,				description='''Prediction'''						,				prediction_loss_only=True if compute_metrics is None else None						,				ignore_keys=__lowercase						,				metric_key_prefix=__lowercase						,				)
             finally:
                    __UpperCAmelCase    :						Dict       =						compute_metrics

             __UpperCAmelCase    :						List[str]       =						self.args.eval_batch_size * self.args.world_size
             if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
                    start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
             output.metrics.update(
                 speed_metrics(
                     __lowercase						,				__lowercase						,				num_samples=output.num_samples						,				num_steps=math.ceil(output.num_samples / total_batch_size )						,				) )
             if self.post_process_function is None or self.compute_metrics is None:
                    return output

             __UpperCAmelCase    :						List[Any]       =						self.post_process_function(__lowercase						,				__lowercase						,				__lowercase						,				'''predict''' )
             __UpperCAmelCase    :						Optional[Any]       =						self.compute_metrics(__lowercase )

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys() ):
                    if not key.startswith(f'''{metric_key_prefix}_''' ):
                           __UpperCAmelCase    :						Optional[Any]       =						metrics.pop(__lowercase )
             metrics.update(output.metrics )
             return PredictionOutput(predictions=predictions.predictions						,				label_ids=predictions.label_ids						,				metrics=__lowercase )






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  )						->float:





  """simple docstring"""


  return 10 - x * x


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->float:





  """simple docstring"""


  if equation(UpperCAmelCase_  ) * equation(UpperCAmelCase_  ) >= 0:
    raise ValueError('''Wrong space!'''  )

  __UpperCAmelCase    :						Tuple       =						a
  while (b - a) >= 0.01:
    # Find middle point
    __UpperCAmelCase    :						List[str]       =						(a + b) / 2
    # Check if middle point is root
    if equation(UpperCAmelCase_  ) == 0.0:
      break
    # Decide the side to repeat the steps
    if equation(UpperCAmelCase_  ) * equation(UpperCAmelCase_  ) < 0:
      __UpperCAmelCase    :						Union[str, Any]       =						c
    else:
      __UpperCAmelCase    :						str       =						c
  return c


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(bisection(-2, 5))
  print(bisection(0, 6))