



'''simple docstring'''



from functools import lru_cache


@lru_cache
def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						int:
				if not isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
								raise TypeError('''k must be an integer.'''			)
				if k < 0:
								raise ValueError('''k integer must be greater or equal to zero.'''			)
				if k == 0:
								return 0
				elif k == 1:
								return 1
				else:
								return fibonacci(k - 1			) + fibonacci(k - 2			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:list   ,				UpperCamelCase__				:int			)		->						int:
				snake_case__      :   List[Any]      =    len(UpperCamelCase__			)
				# Find m such that F_m >= n where F_i is the i_th fibonacci number.
				snake_case__      :   Any      =    0
				while True:
								if fibonacci(UpperCamelCase__			) >= len_list:
												snake_case__      :   Union[str, Any]      =    i
												break
								i += 1
				snake_case__      :   Dict      =    0
				while fibb_k > 0:
								snake_case__      :   Optional[Any]      =    min(
								    offset + fibonacci(fibb_k - 1			)   ,				len_list - 1			)  # Prevent out of range
								snake_case__      :   str      =    arr[index_k]
								if item_k_1 == val:
												return index_k
								elif val < item_k_1:
												fibb_k -= 1
								elif val > item_k_1:
												offset += fibonacci(fibb_k - 1			)
												fibb_k -= 2
				else:
								return -1


if __name__ == "__main__":
				import doctest

				doctest.testmod()







from typing import Callable, Optional, Union

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


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""prophetnet"""
							__UpperCAmelCase      =["""past_key_values"""]
							__UpperCAmelCase      ={
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(			self       ,    _A = 0.1       ,    _A = "gelu"       ,    _A = 3_0_5_2_2       ,    _A = 1_0_2_4       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 0.1       ,    _A = 0.1       ,    _A = 5_1_2       ,    _A = 0.02       ,    _A = True       ,    _A = True       ,    _A = 0       ,    _A = 2       ,    _A = 3_2       ,    _A = 1_2_8       ,    _A = False       ,    _A = 0.0       ,    _A = True       ,    _A = 0       ,    _A = 1       ,    _A = 2       ,    **_A       ,    ):
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				hidden_size
												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				num_encoder_layers
												__lowerCAmelCase       =				num_encoder_attention_heads
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				num_decoder_layers
												__lowerCAmelCase       =				num_decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												# parameters for prophetnet
												__lowerCAmelCase       =				ngram
												__lowerCAmelCase       =				num_buckets
												__lowerCAmelCase       =				relative_max_distance
												__lowerCAmelCase       =				disable_ngram_loss
												__lowerCAmelCase       =				eps

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache

												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    is_encoder_decoder=_A       ,    add_cross_attention=_A       ,    decoder_start_token_id=_A       ,    **_A       ,    )




							@property
							def 							A__	(			self				):
												return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 							A__	(			self       ,    _A				):
												raise NotImplementedError(
												    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
												    ' `num_decoder_layers`.'				)






