




'''simple docstring'''

import math
import time

from transformers import Trainer, 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 						a				(   _lowerCamelCase     ):


				def __init__(		self    :					List[str]       ,				*lowercase_    :					List[str]       ,				lowercase_    :					Optional[int]=None       ,				lowercase_    :					Optional[int]=None       ,				**lowercase_    :					Optional[int]					):
									super().__init__(*lowercase_       ,				**lowercase_					)
									snake_case_									=				eval_examples
									snake_case_									=				post_process_function


				def   A_ (		self    :					Dict       ,				lowercase_    :					int=None       ,				lowercase_    :					Optional[Any]=None       ,				lowercase_    :					Optional[int]=None       ,				lowercase_    :					str = "eval"					):
									snake_case_									=				self.eval_dataset if eval_dataset is None else eval_dataset
									snake_case_									=				self.get_eval_dataloader(lowercase_					)
									snake_case_									=				self.eval_examples if eval_examples is None else eval_examples

									# Temporarily disable metric computation, we will do it in the loop here.
									snake_case_									=				self.compute_metrics
									snake_case_									=				None
									snake_case_									=				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
									snake_case_									=				time.time()
									try:
														snake_case_									=				eval_loop(
														    lowercase_       ,				description='''Evaluation'''       ,				prediction_loss_only=True if compute_metrics is None else None       ,				ignore_keys=lowercase_       ,				metric_key_prefix=lowercase_       ,				)
									finally:
														snake_case_									=				compute_metrics
									snake_case_									=				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
														snake_case_									=				self.post_process_function(lowercase_       ,				lowercase_       ,				output.predictions					)
														snake_case_									=				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}_"					):
																								snake_case_									=				metrics.pop(lowercase_					)
														metrics.update(output.metrics					)
									else:
														snake_case_									=				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()					)

									snake_case_									=				self.callback_handler.on_evaluate(self.args       ,				self.state       ,				self.control       ,				lowercase_					)
									return metrics


				def   A_ (		self    :					str       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Optional[int]=None       ,				lowercase_    :					str = "test"					):
									snake_case_									=				self.get_test_dataloader(lowercase_					)

									# Temporarily disable metric computation, we will do it in the loop here.
									snake_case_									=				self.compute_metrics
									snake_case_									=				None
									snake_case_									=				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
									snake_case_									=				time.time()
									try:
														snake_case_									=				eval_loop(
														    lowercase_       ,				description='''Prediction'''       ,				prediction_loss_only=True if compute_metrics is None else None       ,				ignore_keys=lowercase_       ,				metric_key_prefix=lowercase_       ,				)
									finally:
														snake_case_									=				compute_metrics
									snake_case_									=				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

									snake_case_									=				self.post_process_function(lowercase_       ,				lowercase_       ,				output.predictions       ,				'''predict'''					)
									snake_case_									=				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}_"					):
																			snake_case_									=				metrics.pop(lowercase_					)
									metrics.update(output.metrics					)
									return PredictionOutput(predictions=predictions.predictions       ,				label_ids=predictions.label_ids       ,				metrics=lowercase_					)







'''simple docstring'''

import math
from collections.abc import Callable


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					float:

					'''simple docstring'''

					snake_case_									=				xa
					snake_case_									=				xa
					while True:
										if x_n == x_na or function(__UpperCAmelCase	) == function(__UpperCAmelCase	):
															raise ZeroDivisionError('''float division by zero, could not find root'''	)
										snake_case_									=				x_na - (
										    function(__UpperCAmelCase	) / ((function(__UpperCAmelCase	) - function(__UpperCAmelCase	)) / (x_na - x_n))
										)
										if abs(x_na - x_na	) < 10**-5:
															return x_na
										snake_case_									=				x_na
										snake_case_									=				x_na


def 		__magic_name__    (	__UpperCAmelCase	)		->					float:

					'''simple docstring'''

					return math.pow(__UpperCAmelCase,      3	) - (2 * x) - 5


if __name__ == "__main__":
							print(intersection(f, 3, 3.5))

