


from typing import Mapping

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


__UpperCAmelCase       :       Any       	= logging.get_logger(__name__)

__UpperCAmelCase       :       Optional[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       UpperCAmelCase_		(							a__):
       '''simple docstring'''
       __UpperCamelCase					:   List[Any]							=    "longt5"
       __UpperCamelCase					:   Dict							=    ["past_key_values"]
       __UpperCamelCase					:   Union[str, Any]							=    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






       def __init__(   self       ,    __SCREAMING_SNAKE_CASE=32_128       ,    __SCREAMING_SNAKE_CASE=512       ,    __SCREAMING_SNAKE_CASE=64       ,    __SCREAMING_SNAKE_CASE=2_048       ,    __SCREAMING_SNAKE_CASE=6       ,    __SCREAMING_SNAKE_CASE=None       ,    __SCREAMING_SNAKE_CASE=8       ,    __SCREAMING_SNAKE_CASE=127       ,    __SCREAMING_SNAKE_CASE=16       ,    __SCREAMING_SNAKE_CASE=32       ,    __SCREAMING_SNAKE_CASE=128       ,    __SCREAMING_SNAKE_CASE=0.1       ,    __SCREAMING_SNAKE_CASE=1e-6       ,    __SCREAMING_SNAKE_CASE=1.0       ,    __SCREAMING_SNAKE_CASE="relu"       ,    __SCREAMING_SNAKE_CASE=True       ,    __SCREAMING_SNAKE_CASE="local"       ,    __SCREAMING_SNAKE_CASE=True       ,    __SCREAMING_SNAKE_CASE=0       ,    __SCREAMING_SNAKE_CASE=1       ,    **__SCREAMING_SNAKE_CASE       ,    ):




              """simple docstring"""




              UpperCamelCase			:		Tuple      		=						vocab_size
              UpperCamelCase			:		Dict      		=						d_model
              UpperCamelCase			:		Any      		=						d_kv
              UpperCamelCase			:		Optional[Any]      		=						d_ff
              UpperCamelCase			:		Any      		=						num_layers
              # default = symmetry
              UpperCamelCase			:		Tuple      		=						num_decoder_layers if num_decoder_layers is not None else self.num_layers
              UpperCamelCase			:		Union[str, Any]      		=						num_heads
              UpperCamelCase			:		Any      		=						local_radius
              UpperCamelCase			:		int      		=						global_block_size
              UpperCamelCase			:		Optional[Any]      		=						relative_attention_num_buckets
              UpperCamelCase			:		Optional[int]      		=						relative_attention_max_distance
              UpperCamelCase			:		Union[str, Any]      		=						dropout_rate
              UpperCamelCase			:		List[Any]      		=						layer_norm_epsilon
              UpperCamelCase			:		str      		=						initializer_factor
              UpperCamelCase			:		int      		=						feed_forward_proj
              UpperCamelCase			:		Any      		=						encoder_attention_type
              UpperCamelCase			:		str      		=						use_cache

              UpperCamelCase			:		Tuple      		=						self.feed_forward_proj.split('''-'''  )
              UpperCamelCase			:		Optional[int]      		=						act_info[-1]
              UpperCamelCase			:		List[Any]      		=						act_info[0] == '''gated'''

              if len(_snake_case  ) > 1 and act_info[0] != "gated" or len(_snake_case  ) > 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			:		Optional[Any]      		=						'''gelu_new'''

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




class       UpperCAmelCase_		(							a__):
       '''simple docstring'''






       @property
       def        _lowercase      (   self  ):




              """simple docstring"""




              UpperCamelCase			:		Dict      		=						{
                  '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
                  '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
              }
              if self.use_past:
                     UpperCamelCase			:		Union[str, Any]      		=						'''past_encoder_sequence + sequence'''
                     UpperCamelCase			:		List[Any]      		=						{0: '''batch'''}
                     UpperCamelCase			:		Any      		=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
              else:
                     UpperCamelCase			:		Optional[int]      		=						{0: '''batch''', 1: '''decoder_sequence'''}
                     UpperCamelCase			:		int      		=						{0: '''batch''', 1: '''decoder_sequence'''}

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

              return common_inputs






       @property
       def        _lowercase      (   self  ):




              """simple docstring"""




              return 13








"""simple docstring"""
__UpperCamelCase			:		Tuple		    =   10**8

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  int       ):
 if n % 10 == 0:
  return False
 lowerCAmelCase  							=      str(_UpperCAmelCase       )
 return s == s[::-1]

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      set()
 lowerCAmelCase  							=      1
 lowerCAmelCase  							=      5
 while sum_squares < LIMIT:
  lowerCAmelCase  							=      first_square + 1
  while sum_squares < LIMIT:
   if is_palindrome(_UpperCAmelCase       ):
    answer.add(_UpperCAmelCase       )
   last_square += 1
   sum_squares += last_square**2
  first_square += 1
  lowerCAmelCase  							=      first_square**2 + (first_square + 1) ** 2

 return sum(_UpperCAmelCase       )


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





