


def 	__lowerCamelCase						(							lowerCamelCase__		:						int = 10  ,							lowerCamelCase__		:						int = 1000  ,							lowerCamelCase__		:						bool = True      ):





			'''simple docstring'''




			assert (
			    isinstance(__lowerCamelCase  ,							__lowerCamelCase      )
			    and isinstance(__lowerCamelCase  ,							__lowerCamelCase      )
			    and isinstance(__lowerCamelCase  ,							__lowerCamelCase      )
			), "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						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			return int((number_a + number_a) / 2      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			assert (
			    isinstance(__lowerCamelCase  ,							__lowerCamelCase      ) and isinstance(__lowerCamelCase  ,							__lowerCamelCase      ) and isinstance(__lowerCamelCase  ,							__lowerCamelCase      )
			), '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(lowerCamelCase__		:						int      ) -> str:
						if number > to_guess:
									return "high"
						elif number < to_guess:
									return "low"
						else:
									return "same"

			print("""started..."""      )

			lowerCamelCase    		=  lower
			lowerCamelCase    		=  higher

			lowerCamelCase    		=  []

			while True:
						lowerCamelCase    		=  get_avg(__lowerCamelCase  ,							__lowerCamelCase      )
						last_numbers.append(__lowerCamelCase      )

						if answer(__lowerCamelCase      ) == "low":
									lowerCamelCase    		=  number
						elif answer(__lowerCamelCase      ) == "high":
									lowerCamelCase    		=  number
						else:
									break

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






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  int(input("""Enter lower value : """      ).strip()      )
			lowerCamelCase    		=  int(input("""Enter high value : """      ).strip()      )
			lowerCamelCase    		=  int(input("""Enter value to guess : """      ).strip()      )
			guess_the_number(__lowerCamelCase  ,							__lowerCamelCase  ,							__lowerCamelCase      )


if __name__ == "__main__":
		main()





'''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    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				*snake_case    ,				snake_case=None    ,				snake_case=None    ,				**snake_case)		->    Tuple:




          '''simple docstring'''
          super().__init__(*snake_case    ,				**snake_case)
          _UpperCAmelCase  :  Any				       =eval_examples
          _UpperCAmelCase  :  Optional[int]				       =post_process_function






       def      lowerCAmelCase						(     self    ,				snake_case = None    ,				snake_case=None    ,				snake_case = None    ,				snake_case = "eval"    ,				**snake_case    ,				)		->    Dict[str, float]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =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  :  Tuple				       =self.eval_dataset if eval_dataset is None else eval_dataset
          _UpperCAmelCase  :  Union[str, Any]				       =self.get_eval_dataloader(snake_case)
          _UpperCAmelCase  :  Tuple				       =self.eval_examples if eval_examples is None else eval_examples

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

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys()):
                if not key.startswith(f"{metric_key_prefix}_"):
                   _UpperCAmelCase  :  Any				       =metrics.pop(snake_case)

             metrics.update(output.metrics)
          else:
             _UpperCAmelCase  :  str				       =output.metrics

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

          _UpperCAmelCase  :  Any				       =self.callback_handler.on_evaluate(self.args    ,				self.state    ,				self.control    ,				snake_case)
          return metrics






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case=None    ,				snake_case = "test"    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =gen_kwargs.copy()

          _UpperCAmelCase  :  int				       =self.get_test_dataloader(snake_case)

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

          _UpperCAmelCase  :  Dict				       =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(
                  snake_case    ,				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

          _UpperCAmelCase  :  Tuple				       =self.post_process_function(snake_case    ,				snake_case    ,				snake_case    ,				'predict')
          _UpperCAmelCase  :  Union[str, Any]				       =self.compute_metrics(snake_case)

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

