





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


lowercase    : Any    	=  logging.get_logger(__name__)

lowercase    : Any    	=  {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "switch_transformers"
     __A	  =       ["past_key_values"]
     __A	  =       {"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,       ):





            """simple docstring"""

            _UpperCamelCase           =						vocab_size
            _UpperCamelCase           =						d_model
            _UpperCamelCase           =						d_kv
            _UpperCamelCase           =						d_ff

            _UpperCamelCase           =						num_sparse_encoder_layers

            _UpperCamelCase           =						num_layers
            _UpperCamelCase           =						(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            _UpperCamelCase           =						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:
                   _UpperCamelCase           =						self.num_layers // self.num_sparse_encoder_layers
            else:
                   _UpperCamelCase           =						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:
                   _UpperCamelCase           =						self.num_decoder_layers // self.num_sparse_decoder_layers
            else:
                   _UpperCamelCase           =						self.num_decoder_layers  # HACK: this will create 0 sparse layers

            _UpperCamelCase           =						num_heads
            _UpperCamelCase           =						num_experts
            _UpperCamelCase           =						expert_capacity
            _UpperCamelCase           =						router_bias
            _UpperCamelCase           =						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}'''      )
            _UpperCamelCase           =						router_dtype

            _UpperCamelCase           =						router_ignore_padding_tokens
            _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           =						add_router_probs

            _UpperCamelCase           =						router_z_loss_coef
            _UpperCamelCase           =						router_aux_loss_coef

            _UpperCamelCase           =						self.feed_forward_proj.split('''-'''      )
            _UpperCamelCase           =						act_info[-1]
            _UpperCamelCase           =						act_info[0] == '''gated'''

            if len(__UpperCamelCase      ) > 1 and act_info[0] != "gated" or len(__UpperCamelCase      ) > 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":
                   _UpperCamelCase           =						'''gelu_new'''

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













from __future__ import annotations

from collections.abc import Iterator

class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						value
            _UpperCamelCase              =						None
            _UpperCamelCase              =						None






class  __A:

     def __init__(      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						tree

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
            )



     def __iter__(      self      ):





            """simple docstring"""

            yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






