
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    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	*SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   eval_examples
   lowercase__	: Any     		=   post_process_function


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_ = "eval"):


   '''simple docstring'''




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

   # Temporarily disable metric computation, we will do it in the loop here.
   lowercase__	: Optional[Any]     		=   self.compute_metrics
   lowercase__	: Dict     		=   None
   lowercase__	: List[Any]     		=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
   lowercase__	: Tuple     		=   time.time()
   try:
     lowercase__	: str     		=   eval_loop(
         SCREAMING_SNAKE_CASE_       ,	description="""Evaluation"""       ,	prediction_loss_only=True if compute_metrics is None else None       ,	ignore_keys=SCREAMING_SNAKE_CASE_       ,	metric_key_prefix=SCREAMING_SNAKE_CASE_       ,	)
   finally:
     lowercase__	: Optional[Any]     		=   compute_metrics
   lowercase__	: 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(
           SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	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
     lowercase__	: int     		=   self.post_process_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	output.predictions)
     lowercase__	: List[str]     		=   self.compute_metrics(SCREAMING_SNAKE_CASE_)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()):
       if not key.startswith(f'{metric_key_prefix}_'):
         lowercase__	: List[str]     		=   metrics.pop(SCREAMING_SNAKE_CASE_)
     metrics.update(output.metrics)
   else:
     lowercase__	: Tuple     		=   output.metrics

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

   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())

   lowercase__	: Tuple     		=   self.callback_handler.on_evaluate(self.args       ,	self.state       ,	self.control       ,	SCREAMING_SNAKE_CASE_)
   return metrics



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_ = "test"):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.get_test_dataloader(SCREAMING_SNAKE_CASE_)

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

   lowercase__	: Dict     		=   self.post_process_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	output.predictions       ,	"""predict""")
   lowercase__	: Any     		=   self.compute_metrics(SCREAMING_SNAKE_CASE_)

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









'''simple docstring'''

from __future__ import annotations

def 					__UpperCamelCase				(						lowercase__    :	float,       lowercase__    :	float,       lowercase__    :	float			):






     '''simple docstring'''





     if (voltage, current, resistance).count(0			) != 1:
          raise ValueError('One and only one argument must be 0'			)
     if resistance < 0:
          raise ValueError('Resistance cannot be negative'			)
     if voltage == 0:
          return {"voltage": float(current * resistance			)}
     elif current == 0:
          return {"current": voltage / resistance}
     elif resistance == 0:
          return {"resistance": voltage / current}
     else:
          raise ValueError('Exactly one argument must be 0'			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


