




def 			lowerCamelCase_		(       UpperCamelCase__				:      int = 10 ,     UpperCamelCase__				:      int = 1000 ,     UpperCamelCase__				:      bool = True      )     ->       int:





				"""simple docstring"""


				assert (
				    isinstance(UpperCamelCase__ ,     UpperCamelCase__      )
				    and isinstance(UpperCamelCase__ ,     UpperCamelCase__      )
				    and isinstance(UpperCamelCase__ ,     UpperCamelCase__      )
				), "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_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				return int((number_a + number_a) / 2      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      int      )     ->       None:





				"""simple docstring"""


				assert (
				    isinstance(UpperCamelCase__ ,     UpperCamelCase__      ) and isinstance(UpperCamelCase__ ,     UpperCamelCase__      ) and isinstance(UpperCamelCase__ ,     UpperCamelCase__      )
				), '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(UpperCamelCase__				:      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(UpperCamelCase__ ,     UpperCamelCase__      )
								last_numbers.append(UpperCamelCase__      )

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

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







def 			lowerCamelCase_		(       )     ->       None:





				"""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(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )


if __name__ == "__main__":
					main()







'''simple docstring'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
