






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_       :							str             =				logging.get_logger(__name__)

UpperCAmelCase_       :							List[str]             =				{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}


class 	__UpperCAmelCase		(				_a	):


	'''simple docstring'''





	lowercase		:	Optional[Any]							     =				"""gptsan-japanese"""
	lowercase		:	Any							     =				[
	    """past_key_values""",
	]
	lowercase		:	str							     =				{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}


	def __init__(     self      ,      _A=3_6_0_0_0      ,      _A=1_2_8_0      ,      _A=1_0_2_4      ,      _A=8_1_9_2      ,      _A=4_0_9_6      ,      _A=1_2_8      ,      _A=1_0      ,      _A=0      ,      _A=1_6      ,      _A=1_6      ,      _A=1_2_8      ,      _A=0.0      ,      _A=1E-5      ,      _A=False      ,      _A=0.0      ,      _A="float32"      ,      _A=False      ,      _A=False      ,      _A=False      ,      _A=0.002      ,      _A=False      ,      _A=True      ,      _A=3_5_9_9_8      ,      _A=3_5_9_9_5      ,      _A=3_5_9_9_9      ,      **_A      ,      ):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=vocab_size
		_SCREAMING_SNAKE_CASE						=max_position_embeddings
		_SCREAMING_SNAKE_CASE						=d_model
		_SCREAMING_SNAKE_CASE						=d_ff
		_SCREAMING_SNAKE_CASE						=d_ext
		_SCREAMING_SNAKE_CASE						=d_spout
		_SCREAMING_SNAKE_CASE						=num_switch_layers
		_SCREAMING_SNAKE_CASE						=num_ext_layers
		_SCREAMING_SNAKE_CASE						=num_switch_layers + num_ext_layers
		_SCREAMING_SNAKE_CASE						=num_heads
		_SCREAMING_SNAKE_CASE						=num_experts
		_SCREAMING_SNAKE_CASE						=expert_capacity
		_SCREAMING_SNAKE_CASE						=dropout_rate
		_SCREAMING_SNAKE_CASE						=layer_norm_epsilon
		_SCREAMING_SNAKE_CASE						=router_bias
		_SCREAMING_SNAKE_CASE						=router_jitter_noise
		_SCREAMING_SNAKE_CASE						=router_dtype
		_SCREAMING_SNAKE_CASE						=router_ignore_padding_tokens
		_SCREAMING_SNAKE_CASE						=output_hidden_states
		_SCREAMING_SNAKE_CASE						=output_attentions
		_SCREAMING_SNAKE_CASE						=initializer_factor
		_SCREAMING_SNAKE_CASE						=output_router_logits
		_SCREAMING_SNAKE_CASE						=use_cache

		super().__init__(
		    separator_token_id=__lowerCAmelCase      ,      pad_token_id=__lowerCAmelCase      ,      eos_token_id=__lowerCAmelCase      ,      **__lowerCAmelCase      ,      )











from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__           =			10





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							for i in range(a__ ,      a__							):
														if array[i] == target:
																					return i
							return -1





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      0
							UpperCamelCase__					  =      len(a__							)
							while left <= right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)

														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					UpperCamelCase__					  =      one_third - 1
														elif array[two_third] < target:
																					UpperCamelCase__					  =      two_third + 1

														else:
																					UpperCamelCase__					  =      one_third + 1
																					UpperCamelCase__					  =      two_third - 1
							else:
														return -1





def      _UpperCamelCase  (a__ :int ,      a__ :int ,      a__ :list[int] ,      a__ :int							):






							"""simple docstring"""





							if left < right:
														if right - left < precision:
																					return lin_search(a__ ,      a__ ,      a__ ,      a__							)
														UpperCamelCase__					  =      (left + right) // 3 + 1
														UpperCamelCase__					  =      2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					return rec_ternary_search(a__ ,      one_third - 1 ,      a__ ,      a__							)
														elif array[two_third] < target:
																					return rec_ternary_search(two_third + 1 ,      a__ ,      a__ ,      a__							)
														else:
																					return rec_ternary_search(one_third + 1 ,      two_third - 1 ,      a__ ,      a__							)
							else:
														return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase__           =			input("Enter numbers separated by comma:\n").strip()
					UpperCamelCase__           =			[int(item.strip()) for item in user_input.split(",")]
					assert collection == sorted(collection), f"List must be ordered.\n{collection}."
					UpperCamelCase__           =			int(input("Enter the number to be found in the list:\n").strip())
					UpperCamelCase__           =			ite_ternary_search(collection, target)
					UpperCamelCase__           =			rec_ternary_search(0, len(collection) - 1, collection, target)
					if resulta != -1:
										print(f"""Iterative search: {target} found at positions: {resulta}""")
										print(f"""Recursive search: {target} found at positions: {resulta}""")
					else:
										print("Not found")




