from typing import Mapping

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


__UpperCamelCase   :						Optional[Any]							=  logging.get_logger(__name__)

__UpperCamelCase   :						Dict							=  {
    """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 					__SCREAMING_SNAKE_CASE(		a_		):
							_UpperCAmelCase	      =    "longt5"
							_UpperCAmelCase	      =    ["past_key_values"]
							_UpperCAmelCase	      =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
							def __init__(       self: Any    ,		UpperCamelCase: int=3_21_28    ,		UpperCamelCase: Tuple=5_12    ,		UpperCamelCase: str=64    ,		UpperCamelCase: List[Any]=20_48    ,		UpperCamelCase: Tuple=6    ,		UpperCamelCase: Optional[Any]=None    ,		UpperCamelCase: Dict=8    ,		UpperCamelCase: Optional[int]=1_27    ,		UpperCamelCase: int=16    ,		UpperCamelCase: List[Any]=32    ,		UpperCamelCase: List[str]=1_28    ,		UpperCamelCase: Optional[Any]=0.1    ,		UpperCamelCase: Any=1e-6    ,		UpperCamelCase: Union[str, Any]=1.0    ,		UpperCamelCase: Dict="relu"    ,		UpperCamelCase: Any=True    ,		UpperCamelCase: Optional[int]="local"    ,		UpperCamelCase: List[Any]=True    ,		UpperCamelCase: Any=0    ,		UpperCamelCase: List[str]=1    ,		**UpperCamelCase: Optional[int]    ,		)						->						str:
											snake_case__													=       vocab_size
											snake_case__													=       d_model
											snake_case__													=       d_kv
											snake_case__													=       d_ff
											snake_case__													=       num_layers
											# default = symmetry
											snake_case__													=       num_decoder_layers if num_decoder_layers is not None else self.num_layers
											snake_case__													=       num_heads
											snake_case__													=       local_radius
											snake_case__													=       global_block_size
											snake_case__													=       relative_attention_num_buckets
											snake_case__													=       relative_attention_max_distance
											snake_case__													=       dropout_rate
											snake_case__													=       layer_norm_epsilon
											snake_case__													=       initializer_factor
											snake_case__													=       feed_forward_proj
											snake_case__													=       encoder_attention_type
											snake_case__													=       use_cache

											snake_case__													=       self.feed_forward_proj.split('-'  )
											snake_case__													=       act_info[-1]
											snake_case__													=       act_info[0] == 'gated'

											if len(UpperCamelCase  ) > 1 and act_info[0] != "gated" or len(UpperCamelCase  ) > 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":
															snake_case__													=       'gelu_new'

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


class 					__SCREAMING_SNAKE_CASE(		a_		):
							@property
							def lowerCAmelCase_	(       self: Dict  )						->						Mapping[str, Mapping[int, str]]:
											snake_case__													=       {
											    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
											    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
											}
											if self.use_past:
															snake_case__													=       'past_encoder_sequence + sequence'
															snake_case__													=       {0: 'batch'}
															snake_case__													=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
											else:
															snake_case__													=       {0: 'batch', 1: 'decoder_sequence'}
															snake_case__													=       {0: 'batch', 1: 'decoder_sequence'}

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

											return common_inputs


							@property
							def lowerCAmelCase_	(       self: Optional[Any]  )						->						int:
											return 13












def        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res
def        a_	( _A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												snake_case__													=       ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




