




from __future__ import annotations



class 			a     :

     '''simple docstring'''





     def __init__(	self   :			Optional[int]					,    __snake_case   :			int				):
         UpperCAmelCase_							 =     value
         UpperCAmelCase_							 =     None
         UpperCAmelCase_							 =     None



class 			a     :

     '''simple docstring'''





     lowerCAmelCase				:       int
     def __init__(	self   :			int				):
         UpperCAmelCase_							 =     0
     def   lowerCamelCase_   (	self   :			Optional[int]					,    __snake_case   :			Node | None					,    __snake_case   :			int				):
         if node is None:
             return

         if path_sum == self.target:
             self.paths += 1

         if node.left:
             self.depth_first_search(node.left					,    path_sum + node.left.value				)
         if node.right:
             self.depth_first_search(node.right					,    path_sum + node.right.value				)




     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			Node | None					,    __snake_case   :			int | None = None				):
         if node is None:
             return 0
         if target is not None:
             UpperCAmelCase_							 =     target

         self.depth_first_search(__snake_case					,    node.value				)
         self.path_sum(node.left				)
         self.path_sum(node.right				)

         return self.paths


if __name__ == "__main__":
 import doctest

 doctest.testmod()













import copy

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


__A          = logging.get_logger(__name__)

__A          = {}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt_decoder"

							def __init__(     self:      List[Any]						,   __A:      Dict=0						,   __A:      str=0      )			-> Optional[int]:
												super().__init__()
												_A									=       vocab_size
												_A									=       bos_token_id





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt"
							A_       =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(     self:      Optional[Any]						,   __A:      Optional[Any]=["en", "de"]						,   __A:      List[str]=4_20_24						,   __A:      Any=4_20_24						,   __A:      Optional[int]="relu"						,   __A:      List[str]=10_24						,   __A:      str=2_00						,   __A:      Tuple=10_24						,   __A:      List[str]=40_96						,   __A:      List[Any]=12						,   __A:      Union[str, Any]=16						,   __A:      List[str]=0.0						,   __A:      int=40_96						,   __A:      Union[str, Any]=12						,   __A:      List[str]=16						,   __A:      Union[str, Any]=0.0						,   __A:      int=0.0						,   __A:      str=0.1						,   __A:      int=0.0						,   __A:      Union[str, Any]=0.02						,   __A:      Tuple=2						,   __A:      int=True						,   __A:      Dict=True						,   __A:      int=False						,   __A:      Any=5						,   __A:      Tuple=1.0						,   __A:      Optional[int]=False						,   __A:      List[Any]=True						,   __A:      Tuple=1						,   __A:      List[Any]=0						,   __A:      Any=2						,   __A:      Optional[Any]=2						,   **__A:      Optional[Any]						,   )			-> Tuple:
												_A									=       langs
												_A									=       src_vocab_size
												_A									=       tgt_vocab_size
												_A									=       d_model  # encoder_embed_dim and decoder_embed_dim

												_A									=       encoder_ffn_dim
												_A									=       _A									=       encoder_layers
												_A									=       encoder_attention_heads
												_A									=       encoder_layerdrop
												_A									=       decoder_layerdrop
												_A									=       decoder_ffn_dim
												_A									=       decoder_layers
												_A									=       decoder_attention_heads
												_A									=       max_position_embeddings
												_A									=       init_std  # Normal(0, this parameter)
												_A									=       activation_function

												_A									=       DecoderConfig(vocab_size=__A						,   bos_token_id=__A      )
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												_A									=       scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												_A									=       attention_dropout
												_A									=       activation_dropout
												_A									=       dropout

												_A									=       use_cache
												super().__init__(
												    pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   decoder_start_token_id=__A						,   is_encoder_decoder=__A						,   tie_word_embeddings=__A						,   forced_eos_token_id=__A						,   max_length=__A						,   num_beams=__A						,   length_penalty=__A						,   early_stopping=__A						,   **__A						,   )



							def 					__A       (     self:      Optional[int]      )			-> List[Any]:
												_A									=       copy.deepcopy(self.__dict__      )
												_A									=       self.decoder.to_dict()
												_A									=       self.__class__.model_type
												return output



