
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__:    Optional[int]      = logging.get_logger(__name__)

lowerCAmelCase__:    Optional[int]      = {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}

class 							snake_case_ (				lowercase_    ):
			__lowerCamelCase							:     Optional[Any]						=			"""t5"""
			__lowerCamelCase							:     Union[str, Any]						=			["""past_key_values"""]
			__lowerCamelCase							:     str						=			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





			def __init__(		self      ,						__lowerCAmelCase=32_128      ,						__lowerCAmelCase=512      ,						__lowerCAmelCase=64      ,						__lowerCAmelCase=2_048      ,						__lowerCAmelCase=6      ,						__lowerCAmelCase=None      ,						__lowerCAmelCase=8      ,						__lowerCAmelCase=32      ,						__lowerCAmelCase=128      ,						__lowerCAmelCase=0.1      ,						__lowerCAmelCase=1e-6      ,						__lowerCAmelCase=1.0      ,						__lowerCAmelCase="relu"      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=True      ,						__lowerCAmelCase=0      ,						__lowerCAmelCase=1      ,						**__lowerCAmelCase      ,						):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			vocab_size
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			d_model
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			d_kv
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			d_ff
					SCREAMING_SNAKE_CASE_					:	Dict		  =			num_layers
					SCREAMING_SNAKE_CASE_					:	int		  =			(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					SCREAMING_SNAKE_CASE_					:	int		  =			num_heads
					SCREAMING_SNAKE_CASE_					:	str		  =			relative_attention_num_buckets
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			relative_attention_max_distance
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			dropout_rate
					SCREAMING_SNAKE_CASE_					:	str		  =			layer_norm_epsilon
					SCREAMING_SNAKE_CASE_					:	Dict		  =			initializer_factor
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			feed_forward_proj
					SCREAMING_SNAKE_CASE_					:	Dict		  =			use_cache

					SCREAMING_SNAKE_CASE_					:	Tuple		  =			self.feed_forward_proj.split('-'      )
					SCREAMING_SNAKE_CASE_					:	int		  =			act_info[-1]
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			act_info[0] == 'gated'

					if len(__lowerCAmelCase      ) > 1 and act_info[0] != "gated" or len(__lowerCAmelCase      ) > 2:
							raise ValueError(
							    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
							    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
							    '\'gated-gelu\' or \'relu\''      )

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
							SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			'gelu_new'

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

class 							snake_case_ (				lowercase_    ):





			@property
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			{
					    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
					    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
					}
					if self.use_past:
							SCREAMING_SNAKE_CASE_					:	List[str]		  =			'past_encoder_sequence + sequence'
							SCREAMING_SNAKE_CASE_					:	Dict		  =			{0: 'batch'}
							SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
					else:
							SCREAMING_SNAKE_CASE_					:	List[str]		  =			{0: 'batch', 1: 'decoder_sequence'}
							SCREAMING_SNAKE_CASE_					:	Tuple		  =			{0: 'batch', 1: 'decoder_sequence'}

					if self.use_past:
							self.fill_with_past_key_values_(__lowerCAmelCase      ,						direction='inputs'      )

					return common_inputs






			@property
			def 						__A						(		self      ):
					return 13










'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A = 1_000			)			->			int:






       """simple docstring"""







       return sum(2 * a * ((a - 1) // 2) for a in range(3    ,					n + 1			)			)


if __name__ == "__main__":
 print(solution())




