





from typing import Callable, Optional, Union

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


a__      :     Optional[Any]         =		logging.get_logger(__name__)

a__      :     List[Any]         =		{
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						Union[str, Any]							=   """xlm-prophetnet"""
  __lowerCAmelCase   :						List[Any]							=   ["""past_key_values"""]
  __lowerCAmelCase   :						Dict							=   {
      """num_attention_heads""": """num_encoder_attention_heads""",
  }


  def __init__(							self ,__snake_case = 0.1 ,__snake_case = "gelu" ,__snake_case = 3_0_5_2_2 ,__snake_case = 1_0_2_4 ,__snake_case = 4_0_9_6 ,__snake_case = 1_2 ,__snake_case = 1_6 ,__snake_case = 4_0_9_6 ,__snake_case = 1_2 ,__snake_case = 1_6 ,__snake_case = 0.1 ,__snake_case = 0.1 ,__snake_case = 5_1_2 ,__snake_case = 0.02 ,__snake_case = True ,__snake_case = True ,__snake_case = 0 ,__snake_case = 2 ,__snake_case = 3_2 ,__snake_case = 1_2_8 ,__snake_case = False ,__snake_case = 0.0 ,__snake_case = True ,__snake_case = 0 ,__snake_case = 1 ,__snake_case = 2 ,**__snake_case ,):



       """simple docstring"""





       A_          =       vocab_size
       A_          =       hidden_size
       A_          =       encoder_ffn_dim
       A_          =       num_encoder_layers
       A_          =       num_encoder_attention_heads
       A_          =       decoder_ffn_dim
       A_          =       num_decoder_layers
       A_          =       num_decoder_attention_heads
       A_          =       max_position_embeddings
       A_          =       init_std  # Normal(0, this parameter)
       A_          =       activation_function

       # parameters for xlmprophetnet
       A_          =       ngram
       A_          =       num_buckets
       A_          =       relative_max_distance
       A_          =       disable_ngram_loss
       A_          =       eps

       # 3 Types of Dropout
       A_          =       attention_dropout
       A_          =       activation_dropout
       A_          =       dropout

       A_          =       use_cache

       super().__init__(
           pad_token_id=__snake_case ,bos_token_id=__snake_case ,eos_token_id=__snake_case ,is_encoder_decoder=__snake_case ,add_cross_attention=__snake_case ,decoder_start_token_id=__snake_case ,**__snake_case ,)


  @property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 						__UpperCAmelCase     (							self ,__snake_case ):



       """simple docstring"""





       raise NotImplementedError(
           '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
           ''' `num_decoder_layers`.''' )










# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

a__      :     Optional[int]         =		TypeVar('T')





class UpperCAmelCase_				( Generic[T] ):


  def __init__(							self ,__snake_case = True ):



       """simple docstring"""





       A_          =       {}  # dictionary of lists
       A_          =       directed


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       if not self.directed:  # For undirected graphs
            # if both source vertex and destination vertex are both present in the
            # adjacency list, add destination vertex to source vertex list of adjacent
            # vertices and add source vertex to destination vertex list of adjacent
            # vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 self.adj_list[destination_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination vertex
            # to source vertex list of adjacent vertices, then create a new vertex with
            # destination vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       [source_vertex]
            # if only destination vertex is present in adjacency list, add source vertex
            # to destination vertex list of adjacent vertices, then create a new vertex
            # with source vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif destination_vertex in self.adj_list:
                 self.adj_list[destination_vertex].append(__snake_case )
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and assign a list
            # containing the destination vertex as it's first adjacent vertex also
            # create a new vertex with destination vertex as key and assign a list
            # containing the source vertex as it's first adjacent vertex.
            else:
                 A_          =       [destination_vertex]
                 A_          =       [source_vertex]
       else:  # For directed graphs
            # if both source vertex and destination vertex are present in adjacency
            # list, add destination vertex to source vertex list of adjacent vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination
            # vertex to source vertex list of adjacent vertices and create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       []
            # if only destination vertex is present in adjacency list, create a new
            # vertex with source vertex as key and assign a list containing destination
            # vertex as first adjacent vertex
            elif destination_vertex in self.adj_list:
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and a list containing
            # destination vertex as it's first adjacent vertex. Then create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            else:
                 A_          =       [destination_vertex]
                 A_          =       []

       return self




  def __repr__(							self ):



       """simple docstring"""





       return pformat(self.adj_list )



