code: 
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					{
      "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 			a_					(    SCREAMING_SNAKE_CASE__    ):
   A						       =   '''plbart'''
   A						       =   ['''past_key_values''']
   A						       =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  
   def __init__(       self			,    SCREAMING_SNAKE_CASE=5_0005			,    SCREAMING_SNAKE_CASE=1024			,    SCREAMING_SNAKE_CASE=6			,    SCREAMING_SNAKE_CASE=3072			,    SCREAMING_SNAKE_CASE=12			,    SCREAMING_SNAKE_CASE=6			,    SCREAMING_SNAKE_CASE=3072			,    SCREAMING_SNAKE_CASE=12			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE="gelu"			,    SCREAMING_SNAKE_CASE=768			,    SCREAMING_SNAKE_CASE=0.1			,    SCREAMING_SNAKE_CASE=0.1			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=0.0_2			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=1			,    SCREAMING_SNAKE_CASE=0			,    SCREAMING_SNAKE_CASE=2			,    SCREAMING_SNAKE_CASE=2			,    **SCREAMING_SNAKE_CASE			,    )     ->				List[str]:
  
  
  
  
      """simple docstring"""
  
  
  
      SCREAMING_SNAKE_CASE_			=	vocab_size
      SCREAMING_SNAKE_CASE_			=	max_position_embeddings
      SCREAMING_SNAKE_CASE_			=	d_model
      SCREAMING_SNAKE_CASE_			=	encoder_ffn_dim
      SCREAMING_SNAKE_CASE_			=	encoder_layers
      SCREAMING_SNAKE_CASE_			=	encoder_attention_heads
      SCREAMING_SNAKE_CASE_			=	decoder_ffn_dim
      SCREAMING_SNAKE_CASE_			=	decoder_layers
      SCREAMING_SNAKE_CASE_			=	decoder_attention_heads
      SCREAMING_SNAKE_CASE_			=	dropout
      SCREAMING_SNAKE_CASE_			=	attention_dropout
      SCREAMING_SNAKE_CASE_			=	activation_dropout
      SCREAMING_SNAKE_CASE_			=	activation_function
      SCREAMING_SNAKE_CASE_			=	init_std
      SCREAMING_SNAKE_CASE_			=	encoder_layerdrop
      SCREAMING_SNAKE_CASE_			=	decoder_layerdrop
      SCREAMING_SNAKE_CASE_			=	classifier_dropout
      SCREAMING_SNAKE_CASE_			=	use_cache
      SCREAMING_SNAKE_CASE_			=	encoder_layers
      SCREAMING_SNAKE_CASE_			=	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE			,    bos_token_id=SCREAMING_SNAKE_CASE			,    eos_token_id=SCREAMING_SNAKE_CASE			,    is_encoder_decoder=SCREAMING_SNAKE_CASE			,    forced_eos_token_id=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			,    )
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
   @property
   def 	A_(       self			)     ->				Mapping[str, Mapping[int, str]]:
  
  
  
  
      """simple docstring"""
  
  
  
      return OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'sequence'}),
              ('attention_mask', {0: 'batch', 1: 'sequence'}),
          ]			)
  
  
  
  
  
   @property
   def 	A_(       self			)     ->				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'}),
             ]			)
  
style_context: 
  
  
  
  
  from __future__ import annotations
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	m - 1, 0
  			while i >= 0 and j < n:
  						if key == mat[i][j]:
  									print(F'Key {key} found at row- {i + 1} column- {j + 1}'   )
  									return
  						if key < mat[i][j]:
  									i -= 1
  						else:
  									j += 1
  			print(F'Key {key} not found'   )
  
  def lowercase						(     ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  			SCREAMING_SNAKE_CASE_			=	int(input('Enter the element to be searched:'   )   )
  			print(SCREAMING_SNAKE_CASE   )
  			search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE					,						len(SCREAMING_SNAKE_CASE   )					,						len(mat[0]   )					,						SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  				main()
  