





"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
						    FlaxRobertaForCausalLM,
						    FlaxRobertaForMaskedLM,
						    FlaxRobertaForMultipleChoice,
						    FlaxRobertaForQuestionAnswering,
						    FlaxRobertaForSequenceClassification,
						    FlaxRobertaForTokenClassification,
						    FlaxRobertaModel,
						)





class        _lowerCamelCase      (       unittest.TestCase ):


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


						def     snake_case_   (self		) ->   Any:
								UpperCamelCase   =	ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

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

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

								UpperCamelCase   =	RobertaConfig(
								    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=__a   ,							initializer_range=self.initializer_range   ,							)

								return config, input_ids, token_type_ids, attention_mask


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	config_and_inputs
								UpperCamelCase   =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict




						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	self.prepare_config_and_inputs()
								UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	config_and_inputs

								UpperCamelCase   =	True
								UpperCamelCase   =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								UpperCamelCase   =	ids_tensor([self.batch_size, self.seq_length]   ,							vocab_size=2		)

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





@require_flax
class        _lowerCamelCase      (       _lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			True

						UpperCAmelCase_ =			(
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	FlaxRobertaModelTester(self		)




						@slow
						def     snake_case_   (self		) ->   Any:
								for model_class_name in self.all_model_classes:
										UpperCamelCase   =	model_class_name.from_pretrained("roberta-base"   ,							from_pt=__a		)
										UpperCamelCase   =	model(np.ones((1, 1)		)		)
										self.assertIsNotNone(__a		)









"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCAmelCase__     			=			logging.get_logger(__name__)





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"upernet"


						def __init__(self   ,							__a=None   ,							__a=5_12   ,							__a=0.02   ,							__a=[1, 2, 3, 6]   ,							__a=True   ,							__a=0.4   ,							__a=3_84   ,							__a=2_56   ,							__a=1   ,							__a=False   ,							__a=2_55   ,							**__a   ,							) ->   Tuple:
								super().__init__(**__a		)

								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
										UpperCamelCase   =	CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]		)
								elif isinstance(__a   ,							__a		):
										UpperCamelCase   =	backbone_config.get("model_type"		)
										UpperCamelCase   =	CONFIG_MAPPING[backbone_model_type]
										UpperCamelCase   =	config_class.from_dict(__a		)

								UpperCamelCase   =	backbone_config
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	pool_scales
								UpperCamelCase   =	use_auxiliary_head
								UpperCamelCase   =	auxiliary_loss_weight
								UpperCamelCase   =	auxiliary_in_channels
								UpperCamelCase   =	auxiliary_channels
								UpperCamelCase   =	auxiliary_num_convs
								UpperCamelCase   =	auxiliary_concat_input
								UpperCamelCase   =	loss_ignore_index




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	copy.deepcopy(self.__dict__		)
								UpperCamelCase   =	self.backbone_config.to_dict()
								UpperCamelCase   =	self.__class__.model_type
								return output






