



"""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 			snake_case_						(						a_				):






							def __init__(   self			,     *a_			,     a_=None			,     a_=None			,     **a_      ):
									super().__init__(*a_			,     **a_      )
									a_     :      int 						=     eval_examples
									a_     :      Optional[Any] 						=     post_process_function






							def snake_case_  (   self			,     a_=None			,     a_=None			,     a_=None			,     a_ = "eval"      ):
									a_     :      Tuple 						=     self.eval_dataset if eval_dataset is None else eval_dataset
									a_     :      List[Any] 						=     self.get_eval_dataloader(a_      )
									a_     :      str 						=     self.eval_examples if eval_examples is None else eval_examples

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

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()      ):
													if not key.startswith(F"""{metric_key_prefix}_"""      ):
															a_     :      Tuple 						=     metrics.pop(a_      )
											metrics.update(output.metrics      )
									else:
											a_     :      Any 						=     output.metrics

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

									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()      )

									a_     :      List[str] 						=     self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     a_      )
									return metrics






							def snake_case_  (   self			,     a_			,     a_			,     a_=None			,     a_ = "test"      ):
									a_     :      Tuple 						=     self.get_test_dataloader(a_      )

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

									a_     :      str 						=     self.post_process_function(a_			,     a_			,     output.predictions			,     "predict"      )
									a_     :      Any 						=     self.compute_metrics(a_      )

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




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True	)			->      int:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		    and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), "Invalid type of value(s) specified to function!"

		if min_val > max_val:
				raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"	)
		return min_val if option else max_val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      int:
		return int((number_a + number_a) / 2	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      None:
		assert (
		    isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	) and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		), 'argument values must be type of "int"'

		if lower > higher:
				raise ValueError("argument value for lower and higher must be(lower > higher)"	)

		if not lower < to_guess < higher:
				raise ValueError(
				    "guess value must be within the range of lower and higher value"	)

		def answer(SCREAMING_SNAKE_CASE__	) -> str:
				if number > to_guess:
						return "high"
				elif number < to_guess:
						return "low"
				else:
						return "same"

		print("started..."	)

		a_     :      List[str] 						=     lower
		a_     :      Dict 						=     higher

		a_     :      str 						=     []

		while True:
				a_     :      List[str] 						=     get_avg(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
				last_numbers.append(SCREAMING_SNAKE_CASE__	)

				if answer(SCREAMING_SNAKE_CASE__	) == "low":
						a_     :      Optional[Any] 						=     number
				elif answer(SCREAMING_SNAKE_CASE__	) == "high":
						a_     :      Union[str, Any] 						=     number
				else:
						break

		print(F"""guess the number : {last_numbers[-1]}"""	)
		print(F"""details : {last_numbers!s}"""	)



def        lowerCAmelCase_						(							)			->      None:
		a_     :      str 						=     int(input("Enter lower value : "	).strip()	)
		a_     :      Dict 						=     int(input("Enter high value : "	).strip()	)
		a_     :      Optional[Any] 						=     int(input("Enter value to guess : "	).strip()	)
		guess_the_number(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
					main()