

from typing import Mapping

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


__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      Union[str, Any]										="t5"
  lowerCamelCase   :      int										=["past_key_values"]
  lowerCamelCase   :      str										={"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[Any]=3_21_28	,							lowerCAmelCase			:   Any=5_12	,							lowerCAmelCase			:   Any=64	,							lowerCAmelCase			:   str=20_48	,							lowerCAmelCase			:   List[Any]=6	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   List[Any]=8	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   List[str]=1_28	,							lowerCAmelCase			:   Tuple=0.1	,							lowerCAmelCase			:   List[str]=1e-6	,							lowerCAmelCase			:   str=1.0	,							lowerCAmelCase			:   Tuple="relu"	,							lowerCAmelCase			:   Tuple=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=0	,							lowerCAmelCase			:   Tuple=1	,							**lowerCAmelCase			:   Optional[Any]	,							)							->     Tuple:






     """simple docstring"""







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

     __lowerCAmelCase  :      Union[str, Any]    =						self.feed_forward_proj.split("""-"""   )
     __lowerCAmelCase  :      int    =						act_info[-1]
     __lowerCAmelCase  :      Tuple    =						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":
        __lowerCAmelCase  :      Union[str, Any]    =						"""gelu_new"""

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






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Mapping[str, Mapping[int, str]]:






     """simple docstring"""







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

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

     return common_inputs







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     int:






     """simple docstring"""







     return 13





def      snake_case_				()      ->     List[Any]:
   for n in range(1 ,       1_0_0_0_0_0_0					):
      yield n * (n + 1) // 2





def      snake_case_				(__A					:						Dict					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						1
   __lowerCAmelCase  :      Optional[int]    =						2
   while i * i <= n:
      __lowerCAmelCase  :      Optional[int]    =						0
      while n % i == 0:
         n //= i
         multiplicity += 1
      divisors_count *= multiplicity + 1
      i += 1
   if n > 1:
      divisors_count *= 2
   return divisors_count





def      snake_case_				()      ->     Dict:
   return next(i for i in triangle_number_generator() if count_divisors(__A					) > 5_0_0					)


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


