
"""simple docstring"""
# 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

lowercase_							:    Tuple         =						TypeVar('''T''')
class       UpperCamelCase   (				Generic[T]	):
     def __init__(  self	,  snake_case__ = True					):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE :					dict[T, list[T]]				     =			{}  # dictionary of lists
       _SCREAMING_SNAKE_CASE :					Optional[int]				     =			directed
     def 					__SCREAMING_SNAKE_CASE				(  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(_lowercase					)
           self.adj_list[destination_vertex].append(_lowercase					)
         # 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(_lowercase					)
           _SCREAMING_SNAKE_CASE :					Tuple				     =			[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(_lowercase					)
           _SCREAMING_SNAKE_CASE :					List[str]				     =			[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:
           _SCREAMING_SNAKE_CASE :					str				     =			[destination_vertex]
           _SCREAMING_SNAKE_CASE :					Optional[int]				     =			[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(_lowercase					)
         # 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(_lowercase					)
           _SCREAMING_SNAKE_CASE :					Dict				     =			[]
         # 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:
           _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			[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:
           _SCREAMING_SNAKE_CASE :					Optional[Any]				     =			[destination_vertex]
           _SCREAMING_SNAKE_CASE :					List[Any]				     =			[]

       return self




     def __repr__(  self					):




       """simple docstring"""


       return pformat(self.adj_list					)




import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.bert.modeling_flax_bert import (
      FlaxBertForMaskedLM,
      FlaxBertForMultipleChoice,
      FlaxBertForNextSentencePrediction,
      FlaxBertForPreTraining,
      FlaxBertForQuestionAnswering,
      FlaxBertForSequenceClassification,
      FlaxBertForTokenClassification,
      FlaxBertModel,
  )




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def __init__(						self       :	Tuple		,	_lowercase       :	List[str]		,	_lowercase       :	Dict=13		,	_lowercase       :	Any=7		,	_lowercase       :	List[str]=True		,	_lowercase       :	Any=True		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	int=True		,	_lowercase       :	str=99		,	_lowercase       :	Union[str, Any]=32		,	_lowercase       :	Any=5		,	_lowercase       :	Dict=4		,	_lowercase       :	Tuple=37		,	_lowercase       :	Optional[Any]="gelu"		,	_lowercase       :	Optional[int]=0.1		,	_lowercase       :	Any=0.1		,	_lowercase       :	Union[str, Any]=512		,	_lowercase       :	Dict=16		,	_lowercase       :	int=2		,	_lowercase       :	Union[str, Any]=0.02		,	_lowercase       :	Dict=4		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			parent
    _UpperCamelCase:  Optional[int]    						=			batch_size
    _UpperCamelCase:  Any    						=			seq_length
    _UpperCamelCase:  List[str]    						=			is_training
    _UpperCamelCase:  Union[str, Any]    						=			use_attention_mask
    _UpperCamelCase:  Union[str, Any]    						=			use_token_type_ids
    _UpperCamelCase:  str    						=			use_labels
    _UpperCamelCase:  List[str]    						=			vocab_size
    _UpperCamelCase:  Dict    						=			hidden_size
    _UpperCamelCase:  List[Any]    						=			num_hidden_layers
    _UpperCamelCase:  List[Any]    						=			num_attention_heads
    _UpperCamelCase:  Tuple    						=			intermediate_size
    _UpperCamelCase:  Optional[Any]    						=			hidden_act
    _UpperCamelCase:  Any    						=			hidden_dropout_prob
    _UpperCamelCase:  int    						=			attention_probs_dropout_prob
    _UpperCamelCase:  Optional[int]    						=			max_position_embeddings
    _UpperCamelCase:  List[Any]    						=			type_vocab_size
    _UpperCamelCase:  List[Any]    						=			type_sequence_label_size
    _UpperCamelCase:  List[Any]    						=			initializer_range
    _UpperCamelCase:  Optional[int]    						=			num_choices



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_attention_mask:
     _UpperCamelCase:  List[str]    						=			random_attention_mask([self.batch_size, self.seq_length]				)

    _UpperCamelCase:  Optional[int]    						=			None
    if self.use_token_type_ids:
     _UpperCamelCase:  Tuple    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size				)

    _UpperCamelCase:  str    						=			BertConfig(
        vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	is_decoder=_lowercase		,	initializer_range=self.initializer_range		,	)

    return config, input_ids, token_type_ids, attention_mask



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Any    						=			config_and_inputs
    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.prepare_config_and_inputs()
    _UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  str    						=			config_and_inputs

    _UpperCamelCase:  int    						=			True
    _UpperCamelCase:  Optional[Any]    						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
    _UpperCamelCase:  Optional[Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2				)

    return (
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask,
    )




@require_flax
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = True

   lowerCAmelCase   :     List[Any]	      = (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModelTester(self				)



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			FlaxBertModel.from_pretrained('''bert-base-cased'''				)
    _UpperCamelCase:  int    						=			model(np.ones((1, 1)				)				)
    self.assertIsNotNone(_lowercase				)