
'''simple docstring'''
from typing import Mapping

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


A__ :		List[Any]					=logging.get_logger(__name__)

A__ :		List[Any]					={
    '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 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''longt5'''
 lowerCamelCase												=['''past_key_values''']
 lowerCamelCase												={'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
 def __init__( self      :     Tuple					, lowerCamelCase      :     Dict=3_21_28					, lowerCamelCase      :     Union[str, Any]=5_12					, lowerCamelCase      :     List[str]=64					, lowerCamelCase      :     Union[str, Any]=20_48					, lowerCamelCase      :     List[Any]=6					, lowerCamelCase      :     int=None					, lowerCamelCase      :     List[Any]=8					, lowerCamelCase      :     List[Any]=1_27					, lowerCamelCase      :     str=16					, lowerCamelCase      :     Any=32					, lowerCamelCase      :     Tuple=1_28					, lowerCamelCase      :     List[Any]=0.1					, lowerCamelCase      :     List[Any]=1e-6					, lowerCamelCase      :     str=1.0					, lowerCamelCase      :     str="relu"					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Dict="local"					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Optional[int]=0					, lowerCamelCase      :     Optional[int]=1					, **lowerCamelCase      :     Any					, ):



   """simple docstring"""





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

   __A		:  int       				=		self.feed_forward_proj.split("""-"""							)
   __A		:  Tuple       				=		act_info[-1]
   __A		:  Optional[Any]       				=		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":
     __A		:  Any       				=		"""gelu_new"""

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







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 @property
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





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

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

   return common_inputs






 @property
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   return 13





'''simple docstring'''
from __future__ import annotations







def 							A_  (    __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  __SCREAMING_SNAKE_CASE    :	float | int      ,  )					->							list[float | int]:





  """simple docstring"""

  if not all((nth_term, start_term_a, common_ratio_r)			):
    return []
  __A		:  list[float | int]       				=		[]
  __A		:  Union[str, Any]       				=		1
  __A		:  List[str]       				=		common_ratio_r
  for _ in range(int(__SCREAMING_SNAKE_CASE			)			):
    if not series:
      series.append(__SCREAMING_SNAKE_CASE			)
    else:
      power += 1
      series.append(float(start_term_a * multiple			)			)
      __A		:  List[str]       				=		pow(float(__SCREAMING_SNAKE_CASE			)      ,  __SCREAMING_SNAKE_CASE			)
  return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A__ :		Optional[int]					=float(input('Enter the last number (n term) of the Geometric Series'))
     A__ :		List[Any]					=float(input('Enter the starting term (a) of the Geometric Series'))
     A__ :		List[Any]					=float(
         input('Enter the common ratio between two terms (r) of the Geometric Series')
     )
     print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))



