





from typing import Mapping

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


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				Optional[Any]							=    't5'
      A_						:				str							=    ['past_key_values']
      A_						:				Optional[Any]							=    {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


      def __init__(self       :      Optional[int]				,			a__       :      int=3_2128				,			a__       :      Dict=512				,			a__       :      Optional[Any]=64				,			a__       :      Dict=2048				,			a__       :      Optional[Any]=6				,			a__       :      int=None				,			a__       :      List[Any]=8				,			a__       :      int=32				,			a__       :      Union[str, Any]=128				,			a__       :      str=0.1				,			a__       :      str=1E-6				,			a__       :      str=1.0				,			a__       :      Union[str, Any]="relu"				,			a__       :      Any=True				,			a__       :      Optional[int]=True				,			a__       :      Any=0				,			a__       :      str=1				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         __snake_case							       =   vocab_size
         __snake_case							       =   d_model
         __snake_case							       =   d_kv
         __snake_case							       =   d_ff
         __snake_case							       =   num_layers
         __snake_case							       =   (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         __snake_case							       =   num_heads
         __snake_case							       =   relative_attention_num_buckets
         __snake_case							       =   relative_attention_max_distance
         __snake_case							       =   dropout_rate
         __snake_case							       =   layer_norm_epsilon
         __snake_case							       =   initializer_factor
         __snake_case							       =   feed_forward_proj
         __snake_case							       =   use_cache

         __snake_case							       =   self.feed_forward_proj.split('''-'''      )
         __snake_case							       =   act_info[-1]
         __snake_case							       =   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":
            __snake_case							       =   '''gelu_new'''

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






class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   {
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
            __snake_case							       =   '''past_encoder_sequence + sequence'''
            __snake_case							       =   {0: '''batch'''}
            __snake_case							       =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
            __snake_case							       =   {0: '''batch''', 1: '''decoder_sequence'''}
            __snake_case							       =   {0: '''batch''', 1: '''decoder_sequence'''}

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

         return common_inputs


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         return 13








from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       )      ->				list[list[int]]:
   __snake_case							       =   []
   __snake_case							       =   []
   __snake_case							       =   0
   __snake_case							       =   sum(snake_case_       )
   create_state_space_tree(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
   return result






def lowerCamelCase__    (							snake_case_	:  list[int]       ,      snake_case_	:  int       ,      snake_case_	:  int       ,      snake_case_	:  list[int]       ,      snake_case_	:  list[list[int]]       ,      snake_case_	:  int       ,      )      ->				None:
   if sum(snake_case_       ) > max_sum or (remaining_nums_sum + sum(snake_case_       )) < max_sum:
      return
   if sum(snake_case_       ) == max_sum:
      result.append(snake_case_       )
      return
   for index in range(snake_case_       ,      len(snake_case_       )       ):
      create_state_space_tree(
          snake_case_       ,      snake_case_       ,      index + 1       ,      [*path, nums[index]]       ,      snake_case_       ,      remaining_nums_sum - nums[index]       ,      )
snake_case_				=			[3, 34, 4, 12, 5, 2]
snake_case_				=			9
snake_case_				=			generate_sum_of_subsets_soln(nums, max_sum)







print(*result)

