




"""simple docstring"""


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


A_      =    logging.get_logger(__name__)

A_      =    {
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = "trocr"
 lowercase__			 = ["past_key_values"]
 lowercase__			 = {
     "num_attention_heads": "decoder_attention_heads",
     "hidden_size": "d_model",
     "num_hidden_layers": "decoder_layers",
 }


 def __init__(    self:		List[str],				a_:		List[str]=50_265,				a_:		Optional[Any]=1_024,				a_:		Any=12,				a_:		List[Any]=16,				a_:		List[str]=4_096,				a_:		Optional[int]="gelu",				a_:		Optional[int]=512,				a_:		Optional[int]=0.1,				a_:		List[str]=0.0,				a_:		Any=0.0,				a_:		str=2,				a_:		List[str]=0.02,				a_:		str=0.0,				a_:		Tuple=True,				a_:		List[Any]=False,				a_:		str=True,				a_:		str=True,				a_:		List[Any]=1,				a_:		int=0,				a_:		Any=2,				**a_:		Optional[int],				):




   '''simple docstring'''



   _snake_case							:		str								=  vocab_size
   _snake_case							:		Dict								=  d_model
   _snake_case							:		Tuple								=  decoder_layers
   _snake_case							:		Dict								=  decoder_attention_heads
   _snake_case							:		int								=  decoder_ffn_dim
   _snake_case							:		List[Any]								=  activation_function
   _snake_case							:		str								=  max_position_embeddings
   _snake_case							:		str								=  dropout
   _snake_case							:		List[Any]								=  attention_dropout
   _snake_case							:		Dict								=  activation_dropout
   _snake_case							:		Tuple								=  init_std
   _snake_case							:		int								=  decoder_layerdrop
   _snake_case							:		Optional[int]								=  use_cache
   _snake_case							:		List[Any]								=  scale_embedding
   _snake_case							:		Optional[Any]								=  use_learned_position_embeddings
   _snake_case							:		str								=  layernorm_embedding

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








"""simple docstring"""


from typing import Any






class  lowercase:







 '''simple docstring'''





 def __init__(    self:		Dict,				a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  data
   _snake_case							:		Optional[Any]								=  None







class  lowercase:







 '''simple docstring'''





 def __init__(    self:		str		):




   '''simple docstring'''



   _snake_case							:		Any								=  None


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self.head
   while temp is not None:
     print(temp.data,				end=""" """		)
     _snake_case							:		int								=  temp.next
   print()


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  Node(a_		)
   _snake_case							:		Union[str, Any]								=  self.head
   _snake_case							:		List[Any]								=  new_node


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		List[str],				a_:		Union[str, Any]		):




   '''simple docstring'''



   if node_data_a == node_data_a:
     return
   else:
     _snake_case							:		int								=  self.head
     while node_a is not None and node_a.data != node_data_a:
       _snake_case							:		List[Any]								=  node_a.next

     _snake_case							:		List[Any]								=  self.head
     while node_a is not None and node_a.data != node_data_a:
       _snake_case							:		List[Any]								=  node_a.next

     if node_a is None or node_a is None:
       return

     _snake_case		, _snake_case							:		int								=  node_a.data, node_a.data


if __name__ == "__main__":
     A_      =    LinkedList()
     for i in range(5, 0, -1):
          ll.push(i)

     ll.print_list()

     ll.swap_nodes(1, 4)
     print('''After swapping''')
     ll.print_list()


