


from typing import Mapping

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


UpperCamelCase :				Any						    =       logging.get_logger(__name__)

UpperCamelCase :				List[str]						    =       {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Optional[Any] 				= "longt5"
					SCREAMING_SNAKE_CASE_		:					Optional[int] 				= ["past_key_values"]
					SCREAMING_SNAKE_CASE_		:					Optional[int] 				= {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
					def __init__(	self					:	Any  ,			_lowerCAmelCase					:	Union[str, Any]=3_2_1_2_8  ,			_lowerCAmelCase					:	Any=5_1_2  ,			_lowerCAmelCase					:	Tuple=6_4  ,			_lowerCAmelCase					:	Dict=2_0_4_8  ,			_lowerCAmelCase					:	int=6  ,			_lowerCAmelCase					:	List[Any]=None  ,			_lowerCAmelCase					:	List[str]=8  ,			_lowerCAmelCase					:	Optional[int]=1_2_7  ,			_lowerCAmelCase					:	Dict=1_6  ,			_lowerCAmelCase					:	Any=3_2  ,			_lowerCAmelCase					:	Union[str, Any]=1_2_8  ,			_lowerCAmelCase					:	Any=0.1  ,			_lowerCAmelCase					:	Any=1E-6  ,			_lowerCAmelCase					:	Tuple=1.0  ,			_lowerCAmelCase					:	List[Any]="relu"  ,			_lowerCAmelCase					:	Union[str, Any]=True  ,			_lowerCAmelCase					:	Any="local"  ,			_lowerCAmelCase					:	Optional[Any]=True  ,			_lowerCAmelCase					:	Optional[Any]=0  ,			_lowerCAmelCase					:	str=1  ,			**_lowerCAmelCase					:	List[Any]  ,			)       ->				Dict:



											'''simple docstring'''





											__magic_name__ 		=			vocab_size
											__magic_name__ 		=			d_model
											__magic_name__ 		=			d_kv
											__magic_name__ 		=			d_ff
											__magic_name__ 		=			num_layers
											# default = symmetry
											__magic_name__ 		=			num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__magic_name__ 		=			num_heads
											__magic_name__ 		=			local_radius
											__magic_name__ 		=			global_block_size
											__magic_name__ 		=			relative_attention_num_buckets
											__magic_name__ 		=			relative_attention_max_distance
											__magic_name__ 		=			dropout_rate
											__magic_name__ 		=			layer_norm_epsilon
											__magic_name__ 		=			initializer_factor
											__magic_name__ 		=			feed_forward_proj
											__magic_name__ 		=			encoder_attention_type
											__magic_name__ 		=			use_cache

											__magic_name__ 		=			self.feed_forward_proj.split('-'     )
											__magic_name__ 		=			act_info[-1]
											__magic_name__ 		=			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":
																	__magic_name__ 		=			'gelu_new'

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







class     __snake_case							(      _UpperCAmelCase):
					@property
					def   a (	self					:	List[str]     )       ->				Mapping[str, Mapping[int, str]]:



											'''simple docstring'''





											__magic_name__ 		=			{
											    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
											    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
											}
											if self.use_past:
																	__magic_name__ 		=			'past_encoder_sequence + sequence'
																	__magic_name__ 		=			{0: 'batch'}
																	__magic_name__ 		=			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
																	__magic_name__ 		=			{0: 'batch', 1: 'decoder_sequence'}
																	__magic_name__ 		=			{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					:	Union[str, Any]     )       ->				int:



											'''simple docstring'''





											return 1_3





def 							UpperCamelCase_  (				__snake_case			:						str     )->      int:




						'''simple docstring'''
						__magic_name__ 		=			__magic_name__ 		=			0
						for letter in data:
												__magic_name__ 		=			(hash_ << 4) + ord(__snake_case     )
												__magic_name__ 		=			hash_ & 0xf000_0000
												if x != 0:
																		hash_ ^= x >> 24
												hash_ &= ~x
						return hash_


if __name__ == "__main__":
					import doctest

					doctest.testmod()

