'''simple docstring'''




from typing import Mapping

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


__SCREAMING_SNAKE_CASE	:					int					=  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:					int					=  {
    """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      lowerCamelCase_     (snake_case__   ):
						'''simple docstring'''

						__UpperCamelCase:  Union[str, Any]      =						"longt5"
						__UpperCamelCase:  Optional[int]      =						["past_key_values"]
						__UpperCamelCase:  Optional[Any]      =						{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




						def __init__(	self						:    Tuple						, A						:    List[Any]=32128						, A						:    Dict=512						, A						:    Union[str, Any]=64						, A						:    List[Any]=2048						, A						:    Any=6						, A						:    List[str]=None						, A						:    Union[str, Any]=8						, A						:    int=127						, A						:    Any=16						, A						:    List[str]=32						, A						:    Tuple=128						, A						:    Optional[Any]=0.1						, A						:    Optional[int]=1E-6						, A						:    int=1.0						, A						:    Optional[int]="relu"						, A						:    Dict=True						, A						:    Union[str, Any]="local"						, A						:    str=True						, A						:    int=0						, A						:    Dict=1						, **A						:    Dict						, ):
													_UpperCAmelCase					:  List[str]											=   vocab_size
													_UpperCAmelCase					:  Optional[int]											=   d_model
													_UpperCAmelCase					:  List[Any]											=   d_kv
													_UpperCAmelCase					:  Any											=   d_ff
													_UpperCAmelCase					:  List[Any]											=   num_layers
													# default = symmetry
													_UpperCAmelCase					:  List[Any]											=   num_decoder_layers if num_decoder_layers is not None else self.num_layers
													_UpperCAmelCase					:  List[Any]											=   num_heads
													_UpperCAmelCase					:  str											=   local_radius
													_UpperCAmelCase					:  List[Any]											=   global_block_size
													_UpperCAmelCase					:  Dict											=   relative_attention_num_buckets
													_UpperCAmelCase					:  str											=   relative_attention_max_distance
													_UpperCAmelCase					:  Optional[Any]											=   dropout_rate
													_UpperCAmelCase					:  Optional[Any]											=   layer_norm_epsilon
													_UpperCAmelCase					:  List[str]											=   initializer_factor
													_UpperCAmelCase					:  str											=   feed_forward_proj
													_UpperCAmelCase					:  str											=   encoder_attention_type
													_UpperCAmelCase					:  Dict											=   use_cache

													_UpperCAmelCase					:  int											=   self.feed_forward_proj.split("-"	)
													_UpperCAmelCase					:  Any											=   act_info[-1]
													_UpperCAmelCase					:  Tuple											=   act_info[0] == "gated"

													if len(A	) > 1 and act_info[0] != "gated" or len(A	) > 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":
																				_UpperCAmelCase					:  int											=   "gelu_new"

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

class      lowerCamelCase_     (snake_case__   ):
						'''simple docstring'''

						@property
						def 					_A     (	self						:    str	):
													_UpperCAmelCase					:  Dict											=   {
													    "input_ids": {0: "batch", 1: "encoder_sequence"},
													    "attention_mask": {0: "batch", 1: "encoder_sequence"},
													}
													if self.use_past:
																				_UpperCAmelCase					:  Dict											=   "past_encoder_sequence + sequence"
																				_UpperCAmelCase					:  List[Any]											=   {0: "batch"}
																				_UpperCAmelCase					:  List[str]											=   {0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																				_UpperCAmelCase					:  List[str]											=   {0: "batch", 1: "decoder_sequence"}
																				_UpperCAmelCase					:  Dict											=   {0: "batch", 1: "decoder_sequence"}

													if self.use_past:
																				self.fill_with_past_key_values_(A						, direction="inputs"	)

													return common_inputs





						@property
						def 					_A     (	self						:    List[Any]	):
													return 13


'''simple docstring'''




def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   bool:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[int]											=   9

       while num > 0:
              if num % 10 != digit:
                     return False
              num //= 100
              digit -= 1

       return True

def      UpperCamelCase_							( )   ->   int:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[Any]											=   138_902_663

       while not is_square_form(num * num   ):
              if num % 10 == 3:
                     num -= 6  # (3 - 6) % 10 = 7
              else:
                     num -= 4  # (7 - 4) % 10 = 3

       return num * 10


if __name__ == "__main__":
  print(F'{solution() = }')

