from collections import OrderedDict
from typing import Mapping

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


a_					      = logging.get_logger(__name__)

a_					      = {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}





class 	_lowercase		(      SCREAMING_SNAKE_CASE__ ):
 lowercase         =     'plbart'
 lowercase         =     ['past_key_values']
 lowercase         =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

 def __init__(				self			:							Any     ,   snake_case			:							Union[str, Any]=5_0_0_0_5     ,   snake_case			:							Tuple=1_0_2_4     ,   snake_case			:							Dict=6     ,   snake_case			:							Any=3_0_7_2     ,   snake_case			:							Optional[int]=1_2     ,   snake_case			:							Dict=6     ,   snake_case			:							List[str]=3_0_7_2     ,   snake_case			:							Union[str, Any]=1_2     ,   snake_case			:							Optional[Any]=0.0     ,   snake_case			:							Any=0.0     ,   snake_case			:							List[Any]=True     ,   snake_case			:							Union[str, Any]=True     ,   snake_case			:							Any="gelu"     ,   snake_case			:							Dict=7_6_8     ,   snake_case			:							Any=0.1     ,   snake_case			:							Optional[int]=0.1     ,   snake_case			:							Dict=0.0     ,   snake_case			:							Dict=0.02     ,   snake_case			:							Optional[Any]=0.0     ,   snake_case			:							Dict=True     ,   snake_case			:							Optional[Any]=1     ,   snake_case			:							int=0     ,   snake_case			:							Dict=2     ,   snake_case			:							Tuple=2     ,   **snake_case			:							List[str]     ,   )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      vocab_size
  UpperCamelCase_      :		Union[str, Any]				=      max_position_embeddings
  UpperCamelCase_      :		Optional[int]				=      d_model
  UpperCamelCase_      :		Union[str, Any]				=      encoder_ffn_dim
  UpperCamelCase_      :		Tuple				=      encoder_layers
  UpperCamelCase_      :		List[Any]				=      encoder_attention_heads
  UpperCamelCase_      :		str				=      decoder_ffn_dim
  UpperCamelCase_      :		Union[str, Any]				=      decoder_layers
  UpperCamelCase_      :		Tuple				=      decoder_attention_heads
  UpperCamelCase_      :		List[Any]				=      dropout
  UpperCamelCase_      :		List[str]				=      attention_dropout
  UpperCamelCase_      :		Any				=      activation_dropout
  UpperCamelCase_      :		Optional[Any]				=      activation_function
  UpperCamelCase_      :		Optional[int]				=      init_std
  UpperCamelCase_      :		Tuple				=      encoder_layerdrop
  UpperCamelCase_      :		Union[str, Any]				=      decoder_layerdrop
  UpperCamelCase_      :		List[str]				=      classifier_dropout
  UpperCamelCase_      :		str				=      use_cache
  UpperCamelCase_      :		Optional[Any]				=      encoder_layers
  UpperCamelCase_      :		Any				=      scale_embedding  # scale factor will be sqrt(d_model) if True
  super().__init__(
      pad_token_id=snake_case     ,   bos_token_id=snake_case     ,   eos_token_id=snake_case     ,   is_encoder_decoder=snake_case     ,   forced_eos_token_id=snake_case     ,   **snake_case     ,   )





class 	_lowercase		(      SCREAMING_SNAKE_CASE__ ):

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Mapping[str, Mapping[int, str]]:

  """simple docstring"""





  return OrderedDict(
      [
          ('input_ids', {0: 'batch', 1: 'sequence'}),
          ('attention_mask', {0: 'batch', 1: 'sequence'}),
      ]    )


 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Mapping[str, Mapping[int, str]]:

  """simple docstring"""





  if self.use_past:
   return OrderedDict(
       [
           ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
           ('past_keys', {0: 'batch', 2: 'sequence'}),
           ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
       ]    )
  else:
   return OrderedDict(
       [
           ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
           ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
       ]    )




def      __lowercase  (		lowerCamelCase      :  int = 998001			):
 UpperCamelCase_      :		int				=      0
 for i in range(999						,						99						,						-1			):  # 3 digit numbers range from 999 down to 100
  for j in range(999						,						99						,						-1			):
   UpperCamelCase_      :		str				=      str(i * j			)
   if product_string == product_string[::-1] and i * j < n:
    UpperCamelCase_      :		str				=      max(lowerCamelCase						,						i * j			)
 return answer


if __name__ == "__main__":
   print(F"""{solution() = }""")



