


from __future__ import annotations

snake_case__			 =						[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
snake_case__			 =						[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]


def 				lowerCamelCase__	(      a	:   list[float]				)    ->	list[float]:





			"""simple docstring"""
			a__ :Tuple						  =    []
			a__ :Tuple						  =    len(a				)

			for i in range(a				):
						a__ :float						  =    -1
						for j in range(i + 1	,  a				):
									if arr[i] < arr[j]:
												a__ :List[Any]						  =    arr[j]
												break
						result.append(a				)
			return result


def 				lowerCamelCase__	(      a	:   list[float]				)    ->	list[float]:





			"""simple docstring"""
			a__ :int						  =    []
			for i, outer in enumerate(a				):
						a__ :float						  =    -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												a__ :Dict						  =    inner
												break
						result.append(a				)
			return result


def 				lowerCamelCase__	(      a	:   list[float]				)    ->	list[float]:





			"""simple docstring"""
			a__ :List[Any]						  =    len(a				)
			a__ :list[float]						  =    []
			a__ :list[float]						  =    [-1] * arr_size

			for index in reversed(range(a				)				):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									a__ :Dict						  =    stack[-1]
						stack.append(arr[index]				)
			return result


if __name__ == "__main__":
		from doctest import testmod
		from timeit import timeit

		testmod()
		print(next_greatest_element_slow(arr))
		print(next_greatest_element_fast(arr))
		print(next_greatest_element(arr))

		snake_case__			 =						(
		    '''from __main__ import arr, next_greatest_element_slow, '''
		    '''next_greatest_element_fast, next_greatest_element'''
		)
		print(
		    '''next_greatest_element_slow():''',
		    timeit('''next_greatest_element_slow(arr)''', setup=setup),
		)
		print(
		    '''next_greatest_element_fast():''',
		    timeit('''next_greatest_element_fast(arr)''', setup=setup),
		)
		print(
		    '''     next_greatest_element():''',
		    timeit('''next_greatest_element(arr)''', setup=setup),
		)






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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'pegasus'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Any   ,     __A						:   Dict=50265   ,     __A						:   List[Any]=1024   ,     __A						:   int=12   ,     __A						:   Optional[Any]=4096   ,     __A						:   Optional[int]=16   ,     __A						:   Dict=12   ,     __A						:   List[Any]=4096   ,     __A						:   List[str]=16   ,     __A						:   Optional[int]=0.0   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=True   ,     __A						:   Optional[int]=True   ,     __A						:   str="gelu"   ,     __A						:   Tuple=1024   ,     __A						:   Any=0.1   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=0.0   ,     __A						:   Tuple=0.02   ,     __A						:   Union[str, Any]=0   ,     __A						:   Union[str, Any]=False   ,     __A						:   Optional[Any]=0   ,     __A						:   Tuple=1   ,     __A						:   str=1   ,     **__A						:   Any   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Any						  =    vocab_size
									a__ :List[str]						  =    max_position_embeddings
									a__ :int						  =    d_model
									a__ :Union[str, Any]						  =    encoder_ffn_dim
									a__ :List[Any]						  =    encoder_layers
									a__ :Union[str, Any]						  =    encoder_attention_heads
									a__ :Tuple						  =    decoder_ffn_dim
									a__ :List[Any]						  =    decoder_layers
									a__ :Tuple						  =    decoder_attention_heads
									a__ :Optional[int]						  =    dropout
									a__ :str						  =    attention_dropout
									a__ :Optional[int]						  =    activation_dropout
									a__ :str						  =    activation_function
									a__ :Dict						  =    init_std
									a__ :Any						  =    encoder_layerdrop
									a__ :int						  =    decoder_layerdrop
									a__ :Union[str, Any]						  =    use_cache
									a__ :List[Any]						  =    encoder_layers
									a__ :Dict						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__A   ,     eos_token_id=__A   ,     is_encoder_decoder=__A   ,     decoder_start_token_id=__A   ,     forced_eos_token_id=__A   ,     **__A   ,     )




						@property
						def  _snake_case					(	self						:   Optional[Any]						)      ->int:






									"""simple docstring"""






									return self.encoder_attention_heads






						@property
						def  _snake_case					(	self						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									return self.d_model


