


'''simple docstring'''


from typing import Mapping

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


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    '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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""t5"""
  a_  					=							["""past_key_values"""]
  a_  					=							{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






  def __init__(							self :       List[Any]	,_a :       List[str]=32128	,_a :       Any=512	,_a :       Dict=64	,_a :       List[Any]=2048	,_a :       str=6	,_a :       List[Any]=None	,_a :       Optional[Any]=8	,_a :       List[str]=32	,_a :       Dict=128	,_a :       Optional[Any]=0.1	,_a :       str=1e-6	,_a :       Optional[Any]=1.0	,_a :       List[Any]="relu"	,_a :       Optional[Any]=True	,_a :       Optional[int]=True	,_a :       Optional[Any]=0	,_a :       Optional[int]=1	,**_a :       List[str]	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				vocab_size
        A_						:  Dict          =				d_model
        A_						:  List[Any]          =				d_kv
        A_						:  Tuple          =				d_ff
        A_						:  Any          =				num_layers
        A_						:  Optional[Any]          =				(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_						:  List[Any]          =				num_heads
        A_						:  Optional[Any]          =				relative_attention_num_buckets
        A_						:  Optional[Any]          =				relative_attention_max_distance
        A_						:  int          =				dropout_rate
        A_						:  List[Any]          =				layer_norm_epsilon
        A_						:  int          =				initializer_factor
        A_						:  Any          =				feed_forward_proj
        A_						:  List[str]          =				use_cache

        A_						:  Optional[Any]          =				self.feed_forward_proj.split("""-""" )
        A_						:  Optional[int]          =				act_info[-1]
        A_						:  Tuple          =				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":
              A_						:  Dict          =				"""gelu_new"""

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







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				{
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
              A_						:  Union[str, Any]          =				"""past_encoder_sequence + sequence"""
              A_						:  int          =				{0: """batch"""}
              A_						:  int          =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
              A_						:  Union[str, Any]          =				{0: """batch""", 1: """decoder_sequence"""}
              A_						:  Union[str, Any]          =				{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 :       List[str] ):

        '''simple docstring'''




        return 13




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]):
                  if adj not in explored:
                        stack.append(lowerCamelCase)
      return explored


__magic_name__			=      {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, 'A'))
