


"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}





class    lowerCamelCase__						(		_a    ):
      a :     Union[str, Any]											=		"""transfo-xl"""
      a :     List[str]											=		["""mems"""]
      a :     str											=		{
          """n_token""": """vocab_size""",
          """hidden_size""": """d_model""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self	:	Union[str, Any]      ,       A_	:	str=2_6_7_7_3_5      ,       A_	:	Dict=[2_0_0_0_0, 4_0_0_0_0, 2_0_0_0_0_0]      ,       A_	:	Union[str, Any]=1_0_2_4      ,       A_	:	List[str]=1_0_2_4      ,       A_	:	Optional[int]=1_6      ,       A_	:	str=6_4      ,       A_	:	Tuple=4_0_9_6      ,       A_	:	Dict=4      ,       A_	:	int=False      ,       A_	:	Optional[int]=1_8      ,       A_	:	int=1_6_0_0      ,       A_	:	List[str]=1_0_0_0      ,       A_	:	Dict=True      ,       A_	:	Union[str, Any]=True      ,       A_	:	Union[str, Any]=0      ,       A_	:	Tuple=-1      ,       A_	:	Dict=True      ,       A_	:	Optional[int]=0.1      ,       A_	:	Tuple=0.0      ,       A_	:	Tuple=True      ,       A_	:	str="normal"      ,       A_	:	Tuple=0.01      ,       A_	:	Optional[int]=0.01      ,       A_	:	str=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Dict=0      ,       **A_	:	List[Any]      ,       ):






       '''simple docstring'''


       __lowercase									=	vocab_size
       __lowercase									=	[]
       self.cutoffs.extend(A_							)
       if proj_share_all_but_first:
        __lowercase									=	[False] + [True] * len(self.cutoffs							)
       else:
        __lowercase									=	[False] + [False] * len(self.cutoffs							)
       __lowercase									=	d_model
       __lowercase									=	d_embed
       __lowercase									=	d_head
       __lowercase									=	d_inner
       __lowercase									=	div_val
       __lowercase									=	pre_lnorm
       __lowercase									=	n_layer
       __lowercase									=	n_head
       __lowercase									=	mem_len
       __lowercase									=	same_length
       __lowercase									=	attn_type
       __lowercase									=	clamp_len
       __lowercase									=	sample_softmax
       __lowercase									=	adaptive
       __lowercase									=	dropout
       __lowercase									=	dropatt
       __lowercase									=	untie_r
       __lowercase									=	init
       __lowercase									=	init_range
       __lowercase									=	proj_init_std
       __lowercase									=	init_std
       __lowercase									=	layer_norm_epsilon
       super().__init__(eos_token_id=A_      ,       **A_							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       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 SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       raise NotImplementedError(
           F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''							)




"""simple docstring"""

import random


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: float				,   UpperCamelCase__	: bool = False ):






 """simple docstring"""







 __lowercase									=	{i: [] for i in range(UpperCamelCase__ )}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(UpperCamelCase__ )
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(UpperCamelCase__ ):
  for j in range(i + 1				,   UpperCamelCase__ ):
   if random.random() < probability:
    graph[i].append(UpperCamelCase__ )
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(UpperCamelCase__ )
 return graph


def lowerCAmelCase_      (				UpperCamelCase__	: int ):






 """simple docstring"""







 return {
     i: [j for j in range(UpperCamelCase__ ) if i != j] for i in range(UpperCamelCase__ )
 }


if __name__ == "__main__":
       import doctest

       doctest.testmod()
