



"""simple docstring"""






from __future__ import annotations

from typing import Any






def 	snake_case_  (				A_ :		list[Any]						):


       '''simple docstring'''





       create_state_space_tree(A_,    [],    0						)






def 	snake_case_  (				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__":
   lowerCAmelCase__      =    [3, 1, 2, 4]
   generate_all_subsequences(seq)

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







"""simple docstring"""






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


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       List[Any]        =						"xglm"
  snake_case__    :       Dict        =						["past_key_values"]

  snake_case__    :       str        =						{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						List[Any]=2_5_6_0_0_8						, __lowerCAmelCase					:						int=2_0_4_8						, __lowerCAmelCase					:						Dict=1_0_2_4						, __lowerCAmelCase					:						List[str]=4_0_9_6						, __lowerCAmelCase					:						Tuple=2_4						, __lowerCAmelCase					:						Dict=1_6						, __lowerCAmelCase					:						Tuple="gelu"						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Optional[Any]=0.0						, __lowerCAmelCase					:						List[Any]=0.0						, __lowerCAmelCase					:						int=0.02						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Tuple=True						, __lowerCAmelCase					:						str=2						, __lowerCAmelCase					:						Dict=1						, __lowerCAmelCase					:						Dict=0						, __lowerCAmelCase					:						List[Any]=2						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			vocab_size
         _lowerCamelCase						:    List[Any]         =			max_position_embeddings
         _lowerCamelCase						:    int         =			d_model
         _lowerCamelCase						:    Optional[Any]         =			ffn_dim
         _lowerCamelCase						:    Any         =			num_layers
         _lowerCamelCase						:    Union[str, Any]         =			attention_heads
         _lowerCamelCase						:    List[str]         =			activation_function
         _lowerCamelCase						:    Union[str, Any]         =			dropout
         _lowerCamelCase						:    int         =			attention_dropout
         _lowerCamelCase						:    Optional[int]         =			activation_dropout
         _lowerCamelCase						:    Any         =			layerdrop
         _lowerCamelCase						:    List[str]         =			init_std
         _lowerCamelCase						:    Union[str, Any]         =			scale_embedding  # scale factor will be sqrt(d_model) if True
         _lowerCamelCase						:    str         =			use_cache

         super().__init__(
             pad_token_id=__lowerCAmelCase						, bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, decoder_start_token_id=__lowerCAmelCase						, **__lowerCAmelCase						, )


