






def 					lowerCamelCase__					(_UpperCAmelCase = 10				,							_UpperCAmelCase = 1000				,							_UpperCAmelCase = True):
			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				,							_UpperCAmelCase):
			return int((number_a + number_a) / 2)






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			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) -> str:
						if number > to_guess:
									return "high"
						elif number < to_guess:
									return "low"
						else:
									return "same"

			print('started...')

			SCREAMING_SNAKE_CASE							=			lower
			SCREAMING_SNAKE_CASE							=			higher

			SCREAMING_SNAKE_CASE							=			[]

			while True:
						SCREAMING_SNAKE_CASE							=			get_avg(_UpperCAmelCase				,							_UpperCAmelCase)
						last_numbers.append(_UpperCAmelCase)

						if answer(_UpperCAmelCase) == "low":
									SCREAMING_SNAKE_CASE							=			number
						elif answer(_UpperCAmelCase) == "high":
									SCREAMING_SNAKE_CASE							=			number
						else:
									break

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






def 					lowerCamelCase__					():
			SCREAMING_SNAKE_CASE							=			int(input('Enter lower value : ').strip())
			SCREAMING_SNAKE_CASE							=			int(input('Enter high value : ').strip())
			SCREAMING_SNAKE_CASE							=			int(input('Enter value to guess : ').strip())
			guess_the_number(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)


if __name__ == "__main__":
				main()









"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_						:    Optional[int]				=      {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    List[str]				=      [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							A_						:    str				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





