



'''simple docstring'''

import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
							    FlaxDistilBertForMaskedLM,
							    FlaxDistilBertForMultipleChoice,
							    FlaxDistilBertForQuestionAnswering,
							    FlaxDistilBertForSequenceClassification,
							    FlaxDistilBertForTokenClassification,
							    FlaxDistilBertModel,
							)

class       __magic_name__		(       unittest.TestCase  ):







							def __init__(				self   :			Tuple      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			List[Any]=13      ,_UpperCAmelCase   :			Union[str, Any]=7      ,_UpperCAmelCase   :			List[str]=True      ,_UpperCAmelCase   :			Optional[int]=True      ,_UpperCAmelCase   :			Dict=True      ,_UpperCAmelCase   :			Optional[Any]=True      ,_UpperCAmelCase   :			Union[str, Any]=99      ,_UpperCAmelCase   :			Optional[Any]=32      ,_UpperCAmelCase   :			str=5      ,_UpperCAmelCase   :			Dict=4      ,_UpperCAmelCase   :			Optional[Any]=37      ,_UpperCAmelCase   :			Union[str, Any]="gelu"      ,_UpperCAmelCase   :			Tuple=0.1      ,_UpperCAmelCase   :			str=0.1      ,_UpperCAmelCase   :			int=512      ,_UpperCAmelCase   :			Tuple=16      ,_UpperCAmelCase   :			Optional[int]=2      ,_UpperCAmelCase   :			Any=0.02      ,_UpperCAmelCase   :			Union[str, Any]=4      ,):
									_a	: Tuple								=				parent
									_a	: List[Any]								=				batch_size
									_a	: Optional[Any]								=				seq_length
									_a	: Tuple								=				is_training
									_a	: List[Any]								=				use_attention_mask
									_a	: List[str]								=				use_token_type_ids
									_a	: Union[str, Any]								=				use_labels
									_a	: Optional[Any]								=				vocab_size
									_a	: Tuple								=				hidden_size
									_a	: str								=				num_hidden_layers
									_a	: List[Any]								=				num_attention_heads
									_a	: List[str]								=				intermediate_size
									_a	: Dict								=				hidden_act
									_a	: Optional[int]								=				hidden_dropout_prob
									_a	: Optional[Any]								=				attention_probs_dropout_prob
									_a	: Any								=				max_position_embeddings
									_a	: Optional[int]								=				type_vocab_size
									_a	: List[str]								=				type_sequence_label_size
									_a	: str								=				initializer_range
									_a	: Optional[int]								=				num_choices







							def 				__lowercase     (				self   :			str						):
									_a	: Optional[Any]								=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size						)

									_a	: Optional[Any]								=				None
									if self.use_attention_mask:
											_a	: int								=				random_attention_mask([self.batch_size, self.seq_length]						)

									_a	: List[str]								=				DistilBertConfig(
									    vocab_size=self.vocab_size      ,dim=self.hidden_size      ,n_layers=self.num_hidden_layers      ,n_heads=self.num_attention_heads      ,hidden_dim=self.intermediate_size      ,hidden_act=self.hidden_act      ,dropout=self.hidden_dropout_prob      ,attention_dropout=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,tie_weights_=_UpperCAmelCase      ,)

									return config, input_ids, attention_mask







							def 				__lowercase     (				self   :			Any						):
									_a	: Optional[Any]								=				self.prepare_config_and_inputs()
									_a			,   _a			,   _a	: Optional[Any]								=				config_and_inputs
									_a	: Optional[int]								=				{'input_ids': input_ids, 'attention_mask': attention_mask}
									return config, inputs_dict

@require_flax
class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Tuple  							=   (
							    (
							        FlaxDistilBertModel,
							        FlaxDistilBertForMaskedLM,
							        FlaxDistilBertForMultipleChoice,
							        FlaxDistilBertForQuestionAnswering,
							        FlaxDistilBertForSequenceClassification,
							        FlaxDistilBertForTokenClassification,
							        FlaxDistilBertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Optional[int]								=				FlaxDistilBertModelTester(self						)







							@slow
							def 				__lowercase     (				self   :			Optional[Any]						):
									for model_class_name in self.all_model_classes:
											_a	: List[str]								=				model_class_name.from_pretrained('distilbert-base-uncased'						)
											_a	: Any								=				model(np.ones((1, 1)						)						)
											self.assertIsNotNone(_UpperCAmelCase						)

@require_flax
class       __magic_name__		(       unittest.TestCase  ):







							@slow
							def 				__lowercase     (				self   :			int						):
									_a	: Optional[Any]								=				FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'						)
									_a	: int								=				np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
									_a	: List[Any]								=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
									_a	: int								=				model(_UpperCAmelCase      ,attention_mask=_UpperCAmelCase						)[0]
									_a	: List[str]								=				(1, 11, 768)
									self.assertEqual(output.shape      ,_UpperCAmelCase						)
									_a	: Optional[Any]								=				np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]						)

									self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,_UpperCAmelCase      ,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

__lowerCAmelCase		     =  TypeVar('''T''')

class       __magic_name__		(       Generic[T]  ):







							def __init__(				self   :			int      ,_UpperCAmelCase   :			bool = True						):
									_a	: dict[T, list[T]]								=				{}  # dictionary of lists
									_a	: Tuple								=				directed







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			T      ,_UpperCAmelCase   :			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(_UpperCAmelCase						)
													self.adj_list[destination_vertex].append(_UpperCAmelCase						)
											# 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(_UpperCAmelCase						)
													_a	: Optional[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(_UpperCAmelCase						)
													_a	: Union[str, 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 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	: Union[str, Any]								=				[destination_vertex]
													_a	: 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(_UpperCAmelCase						)
											# 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(_UpperCAmelCase						)
													_a	: int								=				[]
											# 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	: Tuple								=				[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	: Tuple								=				[destination_vertex]
													_a	: str								=				[]

									return self







							def __repr__(				self   :			int						):
									return pformat(self.adj_list						)




