


from typing import Mapping

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


UpperCAmelCase			=    logging.get_logger(__name__)

UpperCAmelCase			=    {
    '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_						( _A       ):

 '''simple docstring'''

 __snake_case												=  "longt5"
 __snake_case												=  ["past_key_values"]
 __snake_case												=  {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



 def __init__(	self		,      _UpperCAmelCase=3_21_28		,      _UpperCAmelCase=5_12		,      _UpperCAmelCase=64		,      _UpperCAmelCase=20_48		,      _UpperCAmelCase=6		,      _UpperCAmelCase=None		,      _UpperCAmelCase=8		,      _UpperCAmelCase=1_27		,      _UpperCAmelCase=16		,      _UpperCAmelCase=32		,      _UpperCAmelCase=1_28		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1E-6		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase="relu"		,      _UpperCAmelCase=True		,      _UpperCAmelCase="local"		,      _UpperCAmelCase=True		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1		,      **_UpperCAmelCase		,      ):
       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(__SCREAMING_SNAKE_CASE					) > 1 and act_info[0] != "gated" or len(__SCREAMING_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":
             snake_case_       =      '''gelu_new'''

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







class lowerCAmelCase_						( _A       ):

 '''simple docstring'''

 @property
 def 						UpperCamelCase__     (	self					):
       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_(__SCREAMING_SNAKE_CASE		,      direction='''inputs'''					)

       return common_inputs





 @property
 def 						UpperCamelCase__     (	self					):
       return 13


"""simple docstring"""




def    __SCREAMING_SNAKE_CASE						(						lowercase__ = 1_000      ):
					"""simple docstring"""
					return sum(i for i in range(lowercase__      ) if i % 3 == 0 or i % 5 == 0      )


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





