






"""simple docstring"""





from typing import Mapping

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


A__					: Tuple=	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    __lowerCamelCase    (  _a  ):
   a      :				Dict 						="""umt5"""
   a      :				List[Any] 						=["""past_key_values"""]

   def __init__(					self ,				snake_case_=25_0112 ,				snake_case_=512 ,				snake_case_=64 ,				snake_case_=1024 ,				snake_case_=8 ,				snake_case_=None ,				snake_case_=6 ,				snake_case_=32 ,				snake_case_=128 ,				snake_case_=0.1 ,				snake_case_=1E-6 ,				snake_case_=1.0 ,				snake_case_="gated-gelu" ,				snake_case_=True ,				snake_case_=True ,				snake_case_="T5Tokenizer" ,				snake_case_=True ,				snake_case_=0 ,				snake_case_=1 ,				snake_case_=0 ,				**snake_case_ ,				)		->       Optional[int]:
       super().__init__(
           is_encoder_decoder=snake_case_ ,				tokenizer_class=snake_case_ ,				tie_word_embeddings=snake_case_ ,				pad_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				decoder_start_token_id=snake_case_ ,				**snake_case_ ,				)
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    d_model
       UpperCamelCase__         =    d_kv
       UpperCamelCase__         =    d_ff
       UpperCamelCase__         =    num_layers
       UpperCamelCase__         =    (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       UpperCamelCase__         =    num_heads
       UpperCamelCase__         =    relative_attention_num_buckets
       UpperCamelCase__         =    relative_attention_max_distance
       UpperCamelCase__         =    dropout_rate
       UpperCamelCase__         =    layer_norm_epsilon
       UpperCamelCase__         =    initializer_factor
       UpperCamelCase__         =    feed_forward_proj
       UpperCamelCase__         =    use_cache

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

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

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       return self.d_model

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       return self.num_heads







   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       return self.num_layers







class    __lowerCamelCase    (  _a  ):

   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       UpperCamelCase__         =    {
           'input_ids': {0: 'batch', 1: 'encoder_sequence'},
           'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
       }
       if self.use_past:
           UpperCamelCase__         =    'past_encoder_sequence + sequence'
           UpperCamelCase__         =    {0: 'batch'}
           UpperCamelCase__         =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
       else:
           UpperCamelCase__         =    {0: 'batch', 1: 'decoder_sequence'}
           UpperCamelCase__         =    {0: 'batch', 1: 'decoder_sequence'}

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

       return common_inputs

   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       return 13







   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       return 5E-4












"""simple docstring"""





from copy import deepcopy
class    __lowerCamelCase    :

   def __init__(					self ,				snake_case_ = None ,				snake_case_ = None      )		->       None:

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    len(snake_case_      )
       UpperCamelCase__         =    deepcopy(snake_case_      )
       for i in range(1 ,				self.size      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               self.tree[j] += self.tree[i]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       list[int]:
       UpperCamelCase__         =    self.tree[:]
       for i in range(self.size - 1 ,				0 ,				-1      ):
           UpperCamelCase__         =    self.next_(snake_case_      )
           if j < self.size:
               arr[j] -= arr[i]
       return arr

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index + (index & (-index))

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       int:
       return index - (index & (-index))

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       if index == 0:
           self.tree[0] += value
           return
       while index < self.size:
           self.tree[index] += value
           UpperCamelCase__         =    self.next_(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       self.add(snake_case_ ,				value - self.get(snake_case_      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       if right == 0:
           return 0
       UpperCamelCase__         =    self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
           result += self.tree[right]
           UpperCamelCase__         =    self.prev(snake_case_      )
       return result

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       return self.prefix(snake_case_      ) - self.prefix(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       return self.query(snake_case_ ,				index + 1      )







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       int:
       value -= self.tree[0]
       if value < 0:
           return -1

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

       UpperCamelCase__         =    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()




