






import warnings

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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Dict       						=  '''xlnet'''
		_snake_case    :    Optional[int]       						=  ['''mems''']
		_snake_case    :    List[str]       						=  {
		    '''n_token''': '''vocab_size''',  # Backward compatibility
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}



		def __init__(							self				,		_UpperCamelCase=3_2_0_0_0				,		_UpperCamelCase=1_0_2_4				,		_UpperCamelCase=2_4				,		_UpperCamelCase=1_6				,		_UpperCamelCase=4_0_9_6				,		_UpperCamelCase="gelu"				,		_UpperCamelCase=True				,		_UpperCamelCase="bi"				,		_UpperCamelCase=0.02				,		_UpperCamelCase=1E-12				,		_UpperCamelCase=0.1				,		_UpperCamelCase=5_1_2				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		_UpperCamelCase=False				,		_UpperCamelCase=False				,		_UpperCamelCase=-1				,		_UpperCamelCase=False				,		_UpperCamelCase="last"				,		_UpperCamelCase=True				,		_UpperCamelCase="tanh"				,		_UpperCamelCase=0.1				,		_UpperCamelCase=5				,		_UpperCamelCase=5				,		_UpperCamelCase=5				,		_UpperCamelCase=1				,		_UpperCamelCase=2				,		**_UpperCamelCase				,		)	->							Any:
							UpperCAmelCase_    :      int            =						vocab_size
							UpperCAmelCase_    :      int            =						d_model
							UpperCAmelCase_    :      Any            =						n_layer
							UpperCAmelCase_    :      Tuple            =						n_head
							if d_model % n_head != 0:
												raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0"   )
							if "d_head" in kwargs:
												if kwargs["d_head"] != d_model // n_head:
																	raise ValueError(
																	    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"   )
							UpperCAmelCase_    :      Dict            =						d_model // n_head
							UpperCAmelCase_    :      int            =						ff_activation
							UpperCAmelCase_    :      Tuple            =						d_inner
							UpperCAmelCase_    :      Any            =						untie_r
							UpperCAmelCase_    :      Optional[int]            =						attn_type

							UpperCAmelCase_    :      Optional[int]            =						initializer_range
							UpperCAmelCase_    :      Union[str, Any]            =						layer_norm_eps

							UpperCAmelCase_    :      int            =						dropout
							UpperCAmelCase_    :      Optional[int]            =						mem_len
							UpperCAmelCase_    :      str            =						reuse_len
							UpperCAmelCase_    :      List[Any]            =						bi_data
							UpperCAmelCase_    :      Tuple            =						clamp_len
							UpperCAmelCase_    :      Dict            =						same_length

							UpperCAmelCase_    :      int            =						summary_type
							UpperCAmelCase_    :      Optional[Any]            =						summary_use_proj
							UpperCAmelCase_    :      List[str]            =						summary_activation
							UpperCAmelCase_    :      Dict            =						summary_last_dropout
							UpperCAmelCase_    :      str            =						start_n_top
							UpperCAmelCase_    :      str            =						end_n_top

							UpperCAmelCase_    :      Any            =						bos_token_id
							UpperCAmelCase_    :      Tuple            =						pad_token_id
							UpperCAmelCase_    :      str            =						eos_token_id

							if "use_cache" in kwargs:
												warnings.warn(
												    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
												    ' instead.'				,		_UpperCamelCase				,		)
												UpperCAmelCase_    :      Any            =						kwargs['use_cache']

							UpperCAmelCase_    :      Union[str, Any]            =						use_mems_eval
							UpperCAmelCase_    :      Union[str, Any]            =						use_mems_train
							super().__init__(pad_token_id=_UpperCamelCase				,		bos_token_id=_UpperCamelCase				,		eos_token_id=_UpperCamelCase				,		**_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."   )
							return -1



		@max_position_embeddings.setter
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    f"The model {self.model_type} is one of the few models that has no sequence length limit."   )














"""simple docstring"""


from __future__ import annotations


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     __A									= data
     __A									= None






   def __repr__(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= []
     __A									= self
     while temp:
       string_rep.append(f'{temp.data}'  )
       __A									= temp.next
     return "->".join(_lowerCamelCase  )





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not elements_list:
    raise Exception('''The Elements List is empty''' )

  __A									= __A									= Node(elements_list[0] )
  for i in range(1		,       len(__UpperCamelCase ) ):
    __A									= Node(elements_list[i] )
    __A									= current.next
  return head



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if head_node is not None and isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    print_reverse(head_node.next )
    print(head_node.data )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  from doctest import testmod

  testmod()

  __A									= make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
  print('''Linked List:''' )
  print(__UpperCamelCase )
  print('''Elements in Reverse:''' )
  print_reverse(__UpperCamelCase )


if __name__ == "__main__":
       main()




