





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_     :						Optional[int]						=					logging.get_logger(__name__)

lowerCamelCase_     :						List[Any]						=					{
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   """switch_transformers"""
      lowercase_      : str	    =   ["""past_key_values"""]
      lowercase_      : Dict	    =   {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

      def __init__(     self					,      snake_case_=3_2_1_2_8					,      snake_case_=7_6_8					,      snake_case_=6_4					,      snake_case_=2_0_4_8					,      snake_case_=6_4					,      snake_case_=1_2					,      snake_case_=3					,      snake_case_=1_2					,      snake_case_=3					,      snake_case_=1_2					,      snake_case_=8					,      snake_case_=False					,      snake_case_=0.01					,      snake_case_="float32"					,      snake_case_=False					,      snake_case_=3_2					,      snake_case_=1_2_8					,      snake_case_=0.1					,      snake_case_=1E-6					,      snake_case_=0.0_01					,      snake_case_=0.0_01					,      snake_case_=1.0					,      snake_case_="relu"					,      snake_case_=True					,      snake_case_=False					,      snake_case_=True					,      snake_case_=0					,      snake_case_=1					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  vocab_size
        A_  :       Tuple										=  d_model
        A_  :       Tuple										=  d_kv
        A_  :       str										=  d_ff

        A_  :       Tuple										=  num_sparse_encoder_layers

        A_  :       int										=  num_layers
        A_  :       List[Any]										=  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_  :       List[str]										=  num_sparse_decoder_layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_encoder_layers > 0:
          A_  :       List[str]										=  self.num_layers // self.num_sparse_encoder_layers
        else:
          A_  :       str										=  self.num_layers  # HACK: this will create 0 sparse layers

        # This tells us, each how many encoder layer we'll have to set a sparse layer.
        if self.num_sparse_decoder_layers > 0:
          A_  :       int										=  self.num_decoder_layers // self.num_sparse_decoder_layers
        else:
          A_  :       str										=  self.num_decoder_layers  # HACK: this will create 0 sparse layers

        A_  :       Optional[Any]										=  num_heads
        A_  :       Tuple										=  num_experts
        A_  :       List[str]										=  expert_capacity
        A_  :       List[Any]										=  router_bias
        A_  :       Optional[int]										=  router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
          raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""  )
        A_  :       Dict										=  router_dtype

        A_  :       int										=  router_ignore_padding_tokens
        A_  :       Union[str, Any]										=  relative_attention_num_buckets
        A_  :       List[str]										=  relative_attention_max_distance

        A_  :       str										=  dropout_rate
        A_  :       Tuple										=  layer_norm_epsilon
        A_  :       Optional[Any]										=  initializer_factor
        A_  :       str										=  feed_forward_proj
        A_  :       List[Any]										=  use_cache
        A_  :       Optional[Any]										=  add_router_probs

        A_  :       Optional[Any]										=  router_z_loss_coef
        A_  :       Union[str, Any]										=  router_aux_loss_coef

        A_  :       str										=  self.feed_forward_proj.split('-'  )
        A_  :       List[Any]										=  act_info[-1]
        A_  :       Tuple										=  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\''  )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
          A_  :       List[Any]										=  'gelu_new'

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






"""simple docstring"""




from copy import deepcopy

class  _UpperCAmelCase  :



      '''simple docstring'''

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

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  len(snake_case_  )
        A_  :       Optional[int]										=  deepcopy(snake_case_  )
        for i in range(1					,      self.size  ):
          A_  :       Optional[Any]										=  self.next_(snake_case_  )
          if j < self.size:
            self.tree[j] += self.tree[i]

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index + (index & (-index))

      @staticmethod
      def 					lowerCamelCase_			(     snake_case_  ):

        """simple docstring"""

        return index - (index & (-index))

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if index == 0:
          self.tree[0] += value
          return
        while index < self.size:
          self.tree[index] += value
          A_  :       List[str]										=  self.next_(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if right == 0:
          return 0
        A_  :       Any										=  self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
          result += self.tree[right]
          A_  :       Tuple										=  self.prev(snake_case_  )
        return result

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        return self.prefix(snake_case_  ) - self.prefix(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        return self.query(snake_case_					,      index + 1  )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

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

        A_  :       List[Any]										=  1  # Largest power of 2 <= size
        while j * 2 < self.size:
          j *= 2

        A_  :       Tuple										=  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()