



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


__lowerCamelCase      =    logging.get_logger(__name__)

__lowerCamelCase      =    {
    """google/pegasus-x-base""": """https://huggingface.co/google/pegasus-x-base/resolve/main/config.json""",
    """google/pegasus-x-large""": """https://huggingface.co/google/pegasus-x-large/resolve/main/config.json""",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}



class 						_snake_case					(	__snake_case ):





 """simple docstring"""



 lowerCamelCase_								=	'pegasus_x'
 lowerCamelCase_								=	['past_key_values']
 lowerCamelCase_								=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(  self		,			a=9_6_1_0_3		,			a=1_6_3_8_4		,			a=1_6		,			a=4_0_9_6		,			a=1_6		,			a=1_6		,			a=4_0_9_6		,			a=1_6		,			a=0.0		,			a=0.0		,			a=True		,			a=True		,			a="gelu"		,			a=1_0_2_4		,			a=0.1		,			a=0.0		,			a=0.0		,			a=0.02		,			a=0		,			a=True		,			a=0		,			a=1		,			a=1		,			a=3_2		,			a=5_1_2		,			a=True		,			**a		,			)		->							int:




   """simple docstring"""







   _A							=			vocab_size
   _A							=			max_position_embeddings
   _A							=			d_model
   _A							=			encoder_ffn_dim
   _A							=			encoder_layers
   _A							=			encoder_attention_heads
   _A							=			decoder_ffn_dim
   _A							=			decoder_layers
   _A							=			decoder_attention_heads
   _A							=			dropout
   _A							=			attention_dropout
   _A							=			activation_dropout
   _A							=			activation_function
   _A							=			init_std
   _A							=			encoder_layerdrop
   _A							=			decoder_layerdrop
   _A							=			use_cache
   _A							=			encoder_layers
   _A							=			scale_embedding  # scale factor will be sqrt(d_model) if True

   _A							=			num_global_tokens
   _A							=			block_size
   _A							=			stagger_local_blocks

   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 lowercase_						(  self    )		->							int:




   """simple docstring"""







   return self.encoder_attention_heads



 @property
 def lowercase_						(  self    )		->							int:




   """simple docstring"""







   return self.d_model
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase						):
					sequence.append(__lowerCamelCase						)

					__a							=   0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__lowerCamelCase						):
										return None

					return index


if __name__ == "__main__":
	lowerCamelCase_				:		List[str]       						=       input("""Enter numbers separated by comma:\n""").strip()
	lowerCamelCase_				:		str       						=       [int(item) for item in user_input.split(""",""")]

	lowerCamelCase_				:		Tuple       						=       input("""Enter a single number to be found in the list:\n""")
	lowerCamelCase_				:		Optional[int]       						=       int(target_input)
	lowerCamelCase_				:		List[Any]       						=       sentinel_linear_search(sequence, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print("""Not found""")




