'''simple docstring'''





from typing import Mapping

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


SCREAMING_SNAKE_CASE_:     int  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={
    '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        __A      ( UpperCamelCase__	):
    a__       :						Any          =  """umt5"""
    a__       :						Optional[int]          =  ["""past_key_values"""]



    def __init__(self    :    Optional[Any]   ,			__a    :    List[str]=250112   ,			__a    :    Union[str, Any]=512   ,			__a    :    str=64   ,			__a    :    Tuple=1024   ,			__a    :    Tuple=8   ,			__a    :    int=None   ,			__a    :    List[Any]=6   ,			__a    :    Any=32   ,			__a    :    Optional[int]=128   ,			__a    :    List[str]=0.1   ,			__a    :    List[Any]=1E-6   ,			__a    :    List[Any]=1.0   ,			__a    :    str="gated-gelu"   ,			__a    :    List[Any]=True   ,			__a    :    List[Any]=True   ,			__a    :    List[str]="T5Tokenizer"   ,			__a    :    Union[str, Any]=True   ,			__a    :    List[str]=0   ,			__a    :    Optional[Any]=1   ,			__a    :    List[str]=0   ,			**__a    :    List[Any]   ,			):
         super().__init__(
             is_encoder_decoder=__a   ,			tokenizer_class=__a   ,			tie_word_embeddings=__a   ,			pad_token_id=__a   ,			eos_token_id=__a   ,			decoder_start_token_id=__a   ,			**__a   ,			)
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   d_model
         UpperCAmelCase_           =   d_kv
         UpperCAmelCase_           =   d_ff
         UpperCAmelCase_           =   num_layers
         UpperCAmelCase_           =   (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCAmelCase_           =   num_heads
         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_           =   self.feed_forward_proj.split("-" )
         UpperCAmelCase_           =   act_info[-1]
         UpperCAmelCase_           =   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'" )

         if feed_forward_proj == "gated-gelu":
              UpperCAmelCase_           =   "gelu_new"



    @property
    def 					_lowercase						(self    :    int ):
         return self.d_model



    @property
    def 					_lowercase						(self    :    Optional[int] ):
         return self.num_heads






    @property
    def 					_lowercase						(self    :    List[str] ):
         return self.num_layers




class        __A      ( UpperCamelCase__	):



    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   {
             "input_ids": {0: "batch", 1: "encoder_sequence"},
             "attention_mask": {0: "batch", 1: "encoder_sequence"},
         }
         if self.use_past:
              UpperCAmelCase_           =   "past_encoder_sequence + sequence"
              UpperCAmelCase_           =   {0: "batch"}
              UpperCAmelCase_           =   {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
              UpperCAmelCase_           =   {0: "batch", 1: "decoder_sequence"}
              UpperCAmelCase_           =   {0: "batch", 1: "decoder_sequence"}

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

         return common_inputs



    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def 					_lowercase						(self    :    Optional[int] ):
         return 13






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








from __future__ import annotations

from typing import Any

def       lowercase_		(		_A			:    list[Any]						):

      """simple docstring"""




      create_state_space_tree(_A    ,					[]    ,					0						)

def       lowercase_		(		_A			:    list[Any]    ,					_A			:    list[Any]    ,					_A			:    int						):

      """simple docstring"""




      if index == len(_A						):
            print(_A						)
            return

      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.append(sequence[index]						)
      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.pop()


if __name__ == "__main__":
    A      :    list[Any]		=					[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["A", "B", "C"])
    generate_all_subsequences(seq)


