

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 A__    ( unittest.TestCase ):



 """simple docstring"""







 def __init__(		self :	Union[str, Any]     ,						A_ :	Dict     ,						A_ :	Optional[Any]=1_3     ,						A_ :	List[Any]=7     ,						A_ :	Optional[Any]=True     ,						A_ :	List[str]=True     ,						A_ :	str=True     ,						A_ :	Dict=True     ,						A_ :	Dict=9_9     ,						A_ :	str=3_2     ,						A_ :	List[str]=5     ,						A_ :	Union[str, Any]=4     ,						A_ :	Dict=3_7     ,						A_ :	List[str]="gelu"     ,						A_ :	List[str]=0.1     ,						A_ :	List[str]=0.1     ,						A_ :	Optional[Any]=5_1_2     ,						A_ :	int=1_6     ,						A_ :	Tuple=2     ,						A_ :	Tuple=0.02     ,						A_ :	List[str]=4     ,						):






      '''simple docstring'''




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

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






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size       )

      _lowerCAmelCase   :					Tuple        =    None
      if self.use_attention_mask:
           _lowerCAmelCase   :					List[Any]        =    random_attention_mask([self.batch_size, self.seq_length]       )

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

      _lowerCAmelCase   :					Union[str, Any]        =    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=UpperCamelCase_     ,						initializer_range=self.initializer_range     ,						)

      return config, input_ids, token_type_ids, attention_mask

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    self.prepare_config_and_inputs()
      _lowerCAmelCase   :					Tuple        =    config_and_inputs
      _lowerCAmelCase   :					Dict        =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
      return config, inputs_dict


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.prepare_config_and_inputs()
      _lowerCAmelCase   :					Optional[Any]        =    config_and_inputs

      _lowerCAmelCase   :					Optional[int]        =    True
      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      _lowerCAmelCase   :					int        =    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 A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Optional[Any]      =True

 _lowercase     :					Any      =(
     (
         FlaxBertModel,
         FlaxBertForPreTraining,
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForQuestionAnswering,
         FlaxBertForNextSentencePrediction,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    FlaxBertModelTester(self       )


 @slow
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    FlaxBertModel.from_pretrained("bert-base-cased"       )
      _lowerCAmelCase   :					str        =    model(np.ones((1, 1)       )       )
      self.assertIsNotNone(UpperCamelCase_       )









# 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

__UpperCAmelCase    				=     TypeVar('T')





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	bool = True       ):






      '''simple docstring'''




      _lowerCAmelCase   :					dict[T, list[T]]        =    {}  # dictionary of lists
      _lowerCAmelCase   :					List[Any]        =    directed

 def    __magic_name__       (		self :	Any     ,						A_ :	T     ,						A_ :	T       ):






      '''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(A_       )
                self.adj_list[destination_vertex].append(A_       )
           # 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(A_       )
                _lowerCAmelCase   :					List[Any]        =    [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(A_       )
                _lowerCAmelCase   :					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:
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
                _lowerCAmelCase   :					Dict        =    [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(A_       )
           # 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(A_       )
                _lowerCAmelCase   :					Tuple        =    []
           # 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:
                _lowerCAmelCase   :					Dict        =    [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:
                _lowerCAmelCase   :					Any        =    [destination_vertex]
                _lowerCAmelCase   :					Union[str, Any]        =    []

      return self


 def __repr__(		self :	List[str]       ):






      '''simple docstring'''




      return pformat(self.adj_list       )






