



'''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(				a_ ):


						'''simple docstring'''



						def __init__( self     ,	*A_     ,	A_=None     ,	A_=None     ,	**A_				)     ->     int:
							super().__init__(*lowercase_     ,	**lowercase_				)
							lowerCAmelCase   =     eval_examples
							lowerCAmelCase   =     post_process_function


						def     __snake_case   ( self     ,	A_ = None     ,	A_=None     ,	A_ = None     ,	A_ = "eval"     ,	**A_     ,	)     ->     Dict[str, float]:
							lowerCAmelCase   =     gen_kwargs.copy()
							lowerCAmelCase   =     (
							    gen_kwargs["""max_length"""] if gen_kwargs.get("""max_length"""				) is not None else self.args.generation_max_length
							)
							lowerCAmelCase   =     (
							    gen_kwargs["""num_beams"""] if gen_kwargs.get("""num_beams"""				) is not None else self.args.generation_num_beams
							)
							lowerCAmelCase   =     gen_kwargs

							lowerCAmelCase   =     self.eval_dataset if eval_dataset is None else eval_dataset
							lowerCAmelCase   =     self.get_eval_dataloader(lowercase_				)
							lowerCAmelCase   =     self.eval_examples if eval_examples is None else eval_examples

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

								metrics.update(output.metrics				)
							else:
								lowerCAmelCase   =     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()				)

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




						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_=None     ,	A_ = "test"     ,	**A_				)     ->     List[Any]:
							lowerCAmelCase   =     gen_kwargs.copy()

							lowerCAmelCase   =     self.get_test_dataloader(lowercase_				)

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

							lowerCAmelCase   =     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

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




'''simple docstring'''

import cmath
import math






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							)      ->						complex:







	"""simple docstring"""
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)

	# Convert voltage and current to rectangular form
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()