

'''simple docstring'''






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.albert.modeling_flax_albert import (
          FlaxAlbertForMaskedLM,
          FlaxAlbertForMultipleChoice,
          FlaxAlbertForPreTraining,
          FlaxAlbertForQuestionAnswering,
          FlaxAlbertForSequenceClassification,
          FlaxAlbertForTokenClassification,
          FlaxAlbertModel,
      )




class 			UpperCAmelCase					(      unittest.TestCase       ):




      def __init__(      self      :			str      ,      __snake_case      :			List[Any]      ,      __snake_case      :			Union[str, Any]=13      ,      __snake_case      :			Optional[Any]=7      ,      __snake_case      :			Tuple=True      ,      __snake_case      :			Union[str, Any]=True      ,      __snake_case      :			int=True      ,      __snake_case      :			Union[str, Any]=True      ,      __snake_case      :			Dict=99      ,      __snake_case      :			Tuple=32      ,      __snake_case      :			Union[str, Any]=5      ,      __snake_case      :			Any=4      ,      __snake_case      :			int=37      ,      __snake_case      :			List[Any]="gelu"      ,      __snake_case      :			Optional[Any]=0.1      ,      __snake_case      :			List[Any]=0.1      ,      __snake_case      :			Dict=5_12      ,      __snake_case      :			Any=16      ,      __snake_case      :			List[Any]=2      ,      __snake_case      :			Dict=0.02      ,      __snake_case      :			List[Any]=4      ,      )    ->				Union[str, Any]:
            _lowerCAmelCase												=							parent
            _lowerCAmelCase												=							batch_size
            _lowerCAmelCase												=							seq_length
            _lowerCAmelCase												=							is_training
            _lowerCAmelCase												=							use_attention_mask
            _lowerCAmelCase												=							use_token_type_ids
            _lowerCAmelCase												=							use_labels
            _lowerCAmelCase												=							vocab_size
            _lowerCAmelCase												=							hidden_size
            _lowerCAmelCase												=							num_hidden_layers
            _lowerCAmelCase												=							num_attention_heads
            _lowerCAmelCase												=							intermediate_size
            _lowerCAmelCase												=							hidden_act
            _lowerCAmelCase												=							hidden_dropout_prob
            _lowerCAmelCase												=							attention_probs_dropout_prob
            _lowerCAmelCase												=							max_position_embeddings
            _lowerCAmelCase												=							type_vocab_size
            _lowerCAmelCase												=							type_sequence_label_size
            _lowerCAmelCase												=							initializer_range
            _lowerCAmelCase												=							num_choices




      def     lowercase__   (      self      :			Tuple  )    ->				Optional[Any]:
            _lowerCAmelCase												=							ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size  )

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

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

            _lowerCAmelCase												=							AlbertConfig(
                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     lowercase__   (      self      :			Tuple  )    ->				str:
            _lowerCAmelCase												=							self.prepare_config_and_inputs()
            _lowerCAmelCase					,							_lowerCAmelCase					,							_lowerCAmelCase					,							_lowerCAmelCase												=							config_and_inputs
            _lowerCAmelCase												=							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict




@require_flax
class 			UpperCAmelCase					(      snake_case_							,		unittest.TestCase       ):
      _lowercase:      Optional[Any]											=       (
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




      def     lowercase__   (      self      :			Dict  )    ->				Optional[int]:
            _lowerCAmelCase												=							FlaxAlbertModelTester(self  )




      @slow
      def     lowercase__   (      self      :			Any  )    ->				str:
            for model_class_name in self.all_model_classes:
                  _lowerCAmelCase												=							model_class_name.from_pretrained("""albert-base-v2"""  )
                  _lowerCAmelCase												=							model(np.ones((1, 1)  )  )
                  self.assertIsNotNone(_lowercase  )




@require_flax
class 			UpperCAmelCase					(      unittest.TestCase       ):




      @slow
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				List[str]:
            _lowerCAmelCase												=							FlaxAlbertModel.from_pretrained("""albert-base-v2"""  )
            _lowerCAmelCase												=							np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]  )
            _lowerCAmelCase												=							np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
            _lowerCAmelCase												=							model(_lowercase      ,      attention_mask=_lowercase  )[0]
            _lowerCAmelCase												=							(1, 11, 7_68)
            self.assertEqual(output.shape      ,      _lowercase  )
            _lowerCAmelCase												=							np.array(
                [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]  )

            self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,      _lowercase      ,      atol=1E-4  )  )













'''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

A__    :       List[str]              =TypeVar('''T''')




class 			UpperCAmelCase					(      Generic[T]       ):




      def __init__(      self      :			Tuple      ,      __snake_case      :			bool = True  )    ->				None:
            _lowerCAmelCase												=							{}  # dictionary of lists
            _lowerCAmelCase												=							directed




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			T      ,      __snake_case      :			T  )    ->				GraphAdjacencyList[T]:

            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  )
                        _lowerCAmelCase												=							[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  )
                        _lowerCAmelCase												=							[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												=							[destination_vertex]
                        _lowerCAmelCase												=							[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  )
                        _lowerCAmelCase												=							[]
                  # 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												=							[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												=							[destination_vertex]
                        _lowerCAmelCase												=							[]

            return self




      def __repr__(      self      :			int  )    ->				str:
            return pformat(self.adj_list  )






