

from collections.abc import Callable


class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a = None):
         # Stores actual heap items.
         lowercase__    :   list      =       []
         # Stores indexes of each item for supporting updates and deletion.
         lowercase__    :   dict      =       {}
         # Stores current size of heap.
         lowercase__    :   int      =       0
         # Stores function used to evaluate the score of an item on which basis ordering
         # will be done.
         lowercase__    :   Any      =       key or (lambda a: x)





    def       snake_case_				(      self						,   a):
         return int((i - 1) / 2) if i > 0 else None





    def       snake_case_				(      self						,   a):
         lowercase__    :   Tuple      =       int(2 * i + 1)
         return left if 0 < left < self.size else None





    def       snake_case_				(      self						,   a):
         lowercase__    :   Dict      =       int(2 * i + 2)
         return right if 0 < right < self.size else None





    def       snake_case_				(      self						,   a						,   a):
         lowercase__      , lowercase__    :   Union[str, Any]      =       (
             self.pos_map[self.arr[j][0]],
             self.pos_map[self.arr[i][0]],
         )
         # Then swap the items in the list.
         lowercase__      , lowercase__    :   Tuple      =       self.arr[j], self.arr[i]





    def       snake_case_				(      self						,   a						,   a):
         return self.arr[i][1] < self.arr[j][1]





    def       snake_case_				(      self						,   a):
         lowercase__    :   int      =       self._left(a)
         lowercase__    :   int      =       self._right(a)
         lowercase__    :   Tuple      =       i

         if left is not None and not self._cmp(a						,   a):
              lowercase__    :   List[str]      =       left
         if right is not None and not self._cmp(a						,   a):
              lowercase__    :   Union[str, Any]      =       right

         return valid_parent





    def       snake_case_				(      self						,   a):
         lowercase__    :   Tuple      =       self._parent(a)
         while parent is not None and not self._cmp(a						,   a):
              self._swap(a						,   a)
              lowercase__      , lowercase__    :   str      =       parent, self._parent(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Dict      =       self._get_valid_parent(a)
         while valid_parent != index:
              self._swap(a						,   a)
              lowercase__      , lowercase__    :   int      =       valid_parent, self._get_valid_parent(a)





    def       snake_case_				(      self						,   a						,   a):
         if item not in self.pos_map:
              return
         lowercase__    :   Dict      =       self.pos_map[item]
         lowercase__    :   Optional[Any]      =       [item, self.key(a)]
         # Make sure heap is right in both up and down direction.
         # Ideally only one of them will make any change.
         self._heapify_up(a)
         self._heapify_down(a)





    def       snake_case_				(      self						,   a):
         if item not in self.pos_map:
              return
         lowercase__    :   Optional[int]      =       self.pos_map[item]
         del self.pos_map[item]
         lowercase__    :   Any      =       self.arr[self.size - 1]
         lowercase__    :   List[str]      =       index
         self.size -= 1
         # Make sure heap is right in both up and down direction. Ideally only one
         # of them will make any change- so no performance loss in calling both.
         if self.size > index:
              self._heapify_up(a)
              self._heapify_down(a)





    def       snake_case_				(      self						,   a						,   a):
         lowercase__    :   Tuple      =       len(self.arr)
         if arr_len == self.size:
              self.arr.append([item, self.key(a)])
         else:
              lowercase__    :   Optional[int]      =       [item, self.key(a)]
         lowercase__    :   Dict      =       self.size
         self.size += 1
         self._heapify_up(self.size - 1)





    def       snake_case_				(      self):
         return self.arr[0] if self.size else None





    def       snake_case_				(      self):
         lowercase__    :   str      =       self.get_top()
         if top_item_tuple:
              self.delete_item(top_item_tuple[0])
         return top_item_tuple



def 	snake_case__			(    ):



     '''simple docstring'''







if __name__ == "__main__":
       import doctest

       doctest.testmod()




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


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       int														=     """switch_transformers"""
    __lowerCamelCase						:       Optional[Any]														=     ["""past_key_values"""]
    __lowerCamelCase						:       Any														=     {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





    def __init__(      self						,   a=3_2128						,   a=768						,   a=64						,   a=2048						,   a=64						,   a=12						,   a=3						,   a=12						,   a=3						,   a=12						,   a=8						,   a=False						,   a=0.01						,   a="float32"						,   a=False						,   a=32						,   a=128						,   a=0.1						,   a=1e-6						,   a=0.001						,   a=0.001						,   a=1.0						,   a="relu"						,   a=True						,   a=False						,   a=True						,   a=0						,   a=1						,   **a						,   ):
         lowercase__    :   Optional[int]      =       vocab_size
         lowercase__    :   List[Any]      =       d_model
         lowercase__    :   List[Any]      =       d_kv
         lowercase__    :   Any      =       d_ff

         lowercase__    :   Optional[int]      =       num_sparse_encoder_layers

         lowercase__    :   Tuple      =       num_layers
         lowercase__    :   Optional[Any]      =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         lowercase__    :   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:
              lowercase__    :   Optional[Any]      =       self.num_layers // self.num_sparse_encoder_layers
         else:
              lowercase__    :   Optional[int]      =       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:
              lowercase__    :   Union[str, Any]      =       self.num_decoder_layers // self.num_sparse_decoder_layers
         else:
              lowercase__    :   int      =       self.num_decoder_layers  # HACK: this will create 0 sparse layers

         lowercase__    :   List[Any]      =       num_heads
         lowercase__    :   Union[str, Any]      =       num_experts
         lowercase__    :   str      =       expert_capacity
         lowercase__    :   List[Any]      =       router_bias
         lowercase__    :   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}""")
         lowercase__    :   str      =       router_dtype

         lowercase__    :   Optional[int]      =       router_ignore_padding_tokens
         lowercase__    :   int      =       relative_attention_num_buckets
         lowercase__    :   Optional[Any]      =       relative_attention_max_distance

         lowercase__    :   List[str]      =       dropout_rate
         lowercase__    :   str      =       layer_norm_epsilon
         lowercase__    :   int      =       initializer_factor
         lowercase__    :   int      =       feed_forward_proj
         lowercase__    :   Dict      =       use_cache
         lowercase__    :   int      =       add_router_probs

         lowercase__    :   int      =       router_z_loss_coef
         lowercase__    :   List[Any]      =       router_aux_loss_coef

         lowercase__    :   int      =       self.feed_forward_proj.split('-')
         lowercase__    :   Optional[int]      =       act_info[-1]
         lowercase__    :   Dict      =       act_info[0] == 'gated'

         if len(a) > 1 and act_info[0] != "gated" or len(a) > 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":
              lowercase__    :   Optional[int]      =       'gelu_new'

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

