



from copy import deepcopy



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			str			,				lowerCAmelCase  :			list[int] | None = None			,				lowerCAmelCase  :			int | None = None)	->				None:




         """simple docstring"""

         if arr is None and size is not None:
               lowercase__     =  size
               lowercase__     =  [0] * size
         elif arr is not None:
               self.init(lowerCAmelCase)
         else:
               raise ValueError('Either arr or size must be specified')


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			list[int])	->				None:




         """simple docstring"""

         lowercase__     =  len(lowerCAmelCase)
         lowercase__     =  deepcopy(lowerCAmelCase)
         for i in range(1			,				self.size):
               lowercase__     =  self.next_(lowerCAmelCase)
               if j < self.size:
                     self.tree[j] += self.tree[i]


   def 			UpperCAmelCase		(						self  :			Tuple)	->				list[int]:




         """simple docstring"""

         lowercase__     =  self.tree[:]
         for i in range(self.size - 1			,				0			,				-1):
               lowercase__     =  self.next_(lowerCAmelCase)
               if j < self.size:
                     arr[j] -= arr[i]
         return arr


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

         return index + (index & (-index))


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

         return index - (index & (-index))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			int)	->				None:




         """simple docstring"""

         if index == 0:
               self.tree[0] += value
               return
         while index < self.size:
               self.tree[index] += value
               lowercase__     =  self.next_(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			int)	->				None:




         """simple docstring"""

         self.add(lowerCAmelCase			,				value - self.get(lowerCAmelCase))


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

         if right == 0:
               return 0
         lowercase__     =  self.tree[0]
         right -= 1  # make right inclusive
         while right > 0:
               result += self.tree[right]
               lowercase__     =  self.prev(lowerCAmelCase)
         return result


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

         return self.prefix(lowerCAmelCase) - self.prefix(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

         return self.query(lowerCAmelCase			,				index + 1)



   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			int)	->				int:




         """simple docstring"""

         value -= self.tree[0]
         if value < 0:
               return -1

         lowercase__     =  1  # Largest power of 2 <= size
         while j * 2 < self.size:
               j *= 2

         lowercase__     =  0

         while j > 0:
               if i + j < self.size and self.tree[i + j] <= value:
                     value -= self.tree[i + j]
                     i += j
               j //= 2
         return i


if __name__ == "__main__":
       import doctest

       doctest.testmod()





from typing import Mapping

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


a__      :				Union[str, Any]   	=			logging.get_logger(__name__)

a__      :				Optional[Any]   	=			{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Union[str, Any]       				=    "umt5"
   A    :  List[str]       				=    ["past_key_values"]


   def __init__(						self  :			List[Any]			,				lowerCAmelCase  :			Optional[int]=25_01_12			,				lowerCAmelCase  :			str=5_12			,				lowerCAmelCase  :			List[Any]=64			,				lowerCAmelCase  :			Optional[int]=10_24			,				lowerCAmelCase  :			Union[str, Any]=8			,				lowerCAmelCase  :			Tuple=None			,				lowerCAmelCase  :			Optional[Any]=6			,				lowerCAmelCase  :			int=32			,				lowerCAmelCase  :			int=1_28			,				lowerCAmelCase  :			List[str]=0.1			,				lowerCAmelCase  :			List[str]=1E-6			,				lowerCAmelCase  :			Optional[int]=1.0			,				lowerCAmelCase  :			Optional[Any]="gated-gelu"			,				lowerCAmelCase  :			List[Any]=True			,				lowerCAmelCase  :			List[str]=True			,				lowerCAmelCase  :			List[Any]="T5Tokenizer"			,				lowerCAmelCase  :			str=True			,				lowerCAmelCase  :			Optional[int]=0			,				lowerCAmelCase  :			Tuple=1			,				lowerCAmelCase  :			Any=0			,				**lowerCAmelCase  :			int			,				)	->				str:




         """simple docstring"""

         super().__init__(
             is_encoder_decoder=lowerCAmelCase			,				tokenizer_class=lowerCAmelCase			,				tie_word_embeddings=lowerCAmelCase			,				pad_token_id=lowerCAmelCase			,				eos_token_id=lowerCAmelCase			,				decoder_start_token_id=lowerCAmelCase			,				**lowerCAmelCase			,				)
         lowercase__     =  vocab_size
         lowercase__     =  d_model
         lowercase__     =  d_kv
         lowercase__     =  d_ff
         lowercase__     =  num_layers
         lowercase__     =  (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__     =  num_heads
         lowercase__     =  relative_attention_num_buckets
         lowercase__     =  relative_attention_max_distance
         lowercase__     =  dropout_rate
         lowercase__     =  layer_norm_epsilon
         lowercase__     =  initializer_factor
         lowercase__     =  feed_forward_proj
         lowercase__     =  use_cache

         lowercase__     =  self.feed_forward_proj.split('-')
         lowercase__     =  act_info[-1]
         lowercase__     =  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\'')

         if feed_forward_proj == "gated-gelu":
               lowercase__     =  'gelu_new'


   @property
   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         return self.d_model


   @property
   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         return self.num_heads



   @property
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         return self.num_layers



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Mapping[str, Mapping[int, str]]:




         """simple docstring"""

         lowercase__     =  {
             'input_ids': {0: 'batch', 1: 'encoder_sequence'},
             'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
         }
         if self.use_past:
               lowercase__     =  'past_encoder_sequence + sequence'
               lowercase__     =  {0: 'batch'}
               lowercase__     =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
         else:
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}
               lowercase__     =  {0: 'batch', 1: 'decoder_sequence'}

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

         return common_inputs


   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         return 13



   @property
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				float:




         """simple docstring"""

         return 5E-4
