




from typing import Mapping

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


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    '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__(					__lowerCamelCase):
      UpperCAmelCase__      :	List[str]    		=   'umt5'
      UpperCAmelCase__      :	Optional[int]    		=   ['past_key_values']
      def __init__(				self: str    ,       UpperCamelCase_: Optional[Any]=25_01_12    ,       UpperCamelCase_: Tuple=5_12    ,       UpperCamelCase_: int=64    ,       UpperCamelCase_: Union[str, Any]=10_24    ,       UpperCamelCase_: Tuple=8    ,       UpperCamelCase_: List[Any]=None    ,       UpperCamelCase_: int=6    ,       UpperCamelCase_: Dict=32    ,       UpperCamelCase_: List[Any]=1_28    ,       UpperCamelCase_: str=0.1    ,       UpperCamelCase_: List[Any]=1E-6    ,       UpperCamelCase_: str=1.0    ,       UpperCamelCase_: int="gated-gelu"    ,       UpperCamelCase_: Optional[int]=True    ,       UpperCamelCase_: Tuple=True    ,       UpperCamelCase_: List[str]="T5Tokenizer"    ,       UpperCamelCase_: Dict=True    ,       UpperCamelCase_: Any=0    ,       UpperCamelCase_: int=1    ,       UpperCamelCase_: Optional[int]=0    ,       **UpperCamelCase_: List[Any]    ,       ):
             super().__init__(
                 is_encoder_decoder=UpperCamelCase_    ,       tokenizer_class=UpperCamelCase_    ,       tie_word_embeddings=UpperCamelCase_    ,       pad_token_id=UpperCamelCase_    ,       eos_token_id=UpperCamelCase_    ,       decoder_start_token_id=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						d_model
             __lowerCamelCase   =						d_kv
             __lowerCamelCase   =						d_ff
             __lowerCamelCase   =						num_layers
             __lowerCamelCase   =						(
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             __lowerCamelCase   =						num_heads
             __lowerCamelCase   =						relative_attention_num_buckets
             __lowerCamelCase   =						relative_attention_max_distance
             __lowerCamelCase   =						dropout_rate
             __lowerCamelCase   =						layer_norm_epsilon
             __lowerCamelCase   =						initializer_factor
             __lowerCamelCase   =						feed_forward_proj
             __lowerCamelCase   =						use_cache

             __lowerCamelCase   =						self.feed_forward_proj.split("""-"""   )
             __lowerCamelCase   =						act_info[-1]
             __lowerCamelCase   =						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'"""   )

             if feed_forward_proj == "gated-gelu":
                    __lowerCamelCase   =						"""gelu_new"""
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             return self.d_model
      @property
      def 			lowerCAmelCase__						(				self: int   ):
             return self.num_heads




      @property
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             return self.num_layers





class     lowerCamelCase__(					__lowerCamelCase):
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						{
                 """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
                 """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
             }
             if self.use_past:
                    __lowerCamelCase   =						"""past_encoder_sequence + sequence"""
                    __lowerCamelCase   =						{0: """batch"""}
                    __lowerCamelCase   =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
             else:
                    __lowerCamelCase   =						{0: """batch""", 1: """decoder_sequence"""}
                    __lowerCamelCase   =						{0: """batch""", 1: """decoder_sequence"""}

             if self.use_past:
                    self.fill_with_past_key_values_(UpperCamelCase_    ,       direction="""inputs"""   )

             return common_inputs
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             return 13




      @property
      def 			lowerCAmelCase__						(				self: int   ):
             return 5E-4







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	int
      UpperCAmelCase__      :	TreeNode | None    		=   None
      UpperCAmelCase__      :	TreeNode | None    		=   None



UpperCAmelCase_									=	namedtuple('CoinsDistribResult', 'moves excess')




def 					lowerCamelCase__       (						A__       :					TreeNode | None	):





       '''simple docstring'''



       if root is None:
              return 0

       # Validation
       def count_nodes(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left	) + count_nodes(node.right	) + 1

       def count_coins(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_coins(node.left	) + count_coins(node.right	) + node.data

       if count_nodes(A__	) != count_coins(A__	):
              raise ValueError("""The nodes number should be same as the number of coins"""	)

       # Main calculation
       def get_distrib(A__       :					TreeNode | None	) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0  ,			1	)

              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.left	)
              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.right	)

              __lowerCamelCase   =						1 - left_distrib_excess
              __lowerCamelCase   =						1 - right_distrib_excess

              __lowerCamelCase   =						(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A__	)
                  + abs(A__	)
              )
              __lowerCamelCase   =						node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A__  ,			A__	)

       return get_distrib(A__	)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

