

"""simple docstring"""





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

					if is_vision_available():
										from transformers import MaskaFormerImageProcessor

if is_vision_available():
					from PIL import Image



class    _lowerCAmelCase    :





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=2							,    _lowercase=True							,    _lowercase=False							,    _lowercase=1_0							,    _lowercase=3							,    _lowercase=3_2 * 8							,    _lowercase=3_2 * 8							,    _lowercase=4							,    _lowercase=6_4							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : int						=			parent
								snake_case_   : Optional[int]						=			batch_size
								snake_case_   : List[str]						=			is_training
								snake_case_   : int						=			use_auxiliary_loss
								snake_case_   : List[str]						=			num_queries
								snake_case_   : Tuple						=			num_channels
								snake_case_   : Any						=			min_size
								snake_case_   : List[Any]						=			max_size
								snake_case_   : List[str]						=			num_labels
								snake_case_   : Optional[Any]						=			hidden_dim
								snake_case_   : Any						=			hidden_dim





		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : Any						=			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]   ).to(
								    _lowercase   )

								snake_case_   : Union[str, Any]						=			torch.ones([self.batch_size, self.min_size, self.max_size]							,    device=_lowercase   )

								snake_case_   : int						=			(
								    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,    device=_lowercase   ) > 0.5
								).float()
								snake_case_   : Any						=			(torch.rand((self.batch_size, self.num_labels)							,    device=_lowercase   ) > 0.5).long()

								snake_case_   : Optional[int]						=			self.get_config()
								return config, pixel_values, pixel_mask, mask_labels, class_labels





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			MaskaFormerConfig(
								    hidden_size=self.hidden_dim							,    )
								snake_case_   : List[Any]						=			self.num_queries
								snake_case_   : Union[str, Any]						=			self.num_labels

								snake_case_   : Union[str, Any]						=			[1, 1, 1, 1]
								snake_case_   : str						=			self.num_channels

								snake_case_   : Any						=			6_4
								snake_case_   : int						=			1_2_8
								snake_case_   : Optional[Any]						=			self.hidden_dim
								snake_case_   : Any						=			self.hidden_dim
								snake_case_   : str						=			self.hidden_dim
								return config





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.prepare_config_and_inputs()
								snake_case_   : Any						=			{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
								return config, inputs_dict





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						Optional[Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			output.encoder_hidden_states
								snake_case_   : Dict						=			output.pixel_decoder_hidden_states
								snake_case_   : List[str]						=			output.transformer_decoder_hidden_states

								self.parent.assertTrue(len(_lowercase   )							,    len(config.backbone_config.depths   )   )
								self.parent.assertTrue(len(_lowercase   )							,    len(config.backbone_config.depths   )   )
								self.parent.assertTrue(len(_lowercase   )							,    config.decoder_layers   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase=False   ) ->						int:







								'''simple docstring'''

								with torch.no_grad():
														snake_case_   : Dict						=			MaskaFormerModel(config=_lowercase   )
														model.to(_lowercase   )
														model.eval()

														snake_case_   : Optional[int]						=			model(pixel_values=_lowercase							,    pixel_mask=_lowercase   )
														snake_case_   : Optional[Any]						=			model(_lowercase							,    output_hidden_states=_lowercase   )

								self.parent.assertEqual(
								    output.transformer_decoder_last_hidden_state.shape							,    (self.batch_size, self.num_queries, self.hidden_dim)							,    )
								# let's ensure the other two hidden state exists
								self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None   )
								self.parent.assertTrue(output.encoder_last_hidden_state is not None   )

								if output_hidden_states:
														self.check_output_hidden_state(_lowercase							,    _lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						str:







								'''simple docstring'''

								snake_case_   : List[str]						=			MaskaFormerForUniversalSegmentation(config=_lowercase   )
								model.to(_lowercase   )
								model.eval()

								def comm_check_on_output(_lowercase   ):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None   )
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None   )
														self.parent.assertTrue(result.encoder_last_hidden_state is not None   )
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape							,    (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)							,    )
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape							,    (self.batch_size, self.num_queries, self.num_labels + 1)   )

								with torch.no_grad():
														snake_case_   : List[str]						=			model(pixel_values=_lowercase							,    pixel_mask=_lowercase   )
														snake_case_   : List[Any]						=			model(_lowercase   )

														comm_check_on_output(_lowercase   )

														snake_case_   : Optional[Any]						=			model(
														    pixel_values=_lowercase							,    pixel_mask=_lowercase							,    mask_labels=_lowercase							,    class_labels=_lowercase   )

								comm_check_on_output(_lowercase   )

								self.parent.assertTrue(result.loss is not None   )
								self.parent.assertEqual(result.loss.shape							,    torch.Size([1]   )   )




@require_torch
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
		_lowerCamelCase			  =       {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			MaskaFormerModelTester(self   )
								snake_case_   : List[Any]						=			ConfigTester(self							,    config_class=_lowercase							,    has_text_modality=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								self.config_tester.run_common_tests()





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(_lowercase							,    **_lowercase							,    output_hidden_states=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Any						=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowercase   )





		@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""   )
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""   )
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""Mask2Former is not a generative model"""   )
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""Mask2Former does not use token embeddings"""   )
		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								pass





		@require_torch_multi_gpu
		@unittest.skip(
		    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""   )
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								pass





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														snake_case_   : Any						=			model_class(_lowercase   )
														snake_case_   : Any						=			inspect.signature(model.forward   )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														snake_case_   : str						=			[*signature.parameters.keys()]

														snake_case_   : Tuple						=			["""pixel_values"""]
														self.assertListEqual(arg_names[:1]							,    _lowercase   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
														snake_case_   : Tuple						=			MaskaFormerModel.from_pretrained(_lowercase   )
														self.assertIsNotNone(_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			(self.model_tester.min_size,) * 2
								snake_case_   : Union[str, Any]						=			{
								    """pixel_values""": torch.randn((2, 3, *size)							,    device=_lowercase   ),
								    """mask_labels""": torch.randn((2, 1_0, *size)							,    device=_lowercase   ),
								    """class_labels""": torch.zeros(2							,    1_0							,    device=_lowercase   ).long(),
								}
								snake_case_   : int						=			self.model_tester.get_config()

								snake_case_   : int						=			MaskaFormerForUniversalSegmentation(_lowercase   ).to(_lowercase   )
								snake_case_   : List[str]						=			model(**_lowercase   )
								self.assertTrue(outputs.loss is not None   )





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.create_and_check_maskaformer_model(_lowercase							,    **_lowercase							,    output_hidden_states=_lowercase   )





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : int						=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														snake_case_   : str						=			model_class(_lowercase   ).to(_lowercase   )
														snake_case_   : Optional[int]						=			model(**_lowercase							,    output_attentions=_lowercase   )
														self.assertTrue(outputs.attentions is not None   )





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								if not self.model_tester.is_training:
														return

								snake_case_   : int						=			self.all_model_classes[1]
								snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs()

								snake_case_   : Optional[Any]						=			model_class(_lowercase   )
								model.to(_lowercase   )
								model.train()

								snake_case_   : Optional[Any]						=			model(_lowercase							,    mask_labels=_lowercase							,    class_labels=_lowercase   ).loss
								loss.backward()





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : str						=			self.all_model_classes[1]
								snake_case_   : Dict						=			self.model_tester.prepare_config_and_inputs()
								snake_case_   : str						=			True
								snake_case_   : List[str]						=			True

								snake_case_   : str						=			model_class(_lowercase   ).to(_lowercase   )
								model.train()

								snake_case_   : Dict						=			model(_lowercase							,    mask_labels=_lowercase							,    class_labels=_lowercase   )

								snake_case_   : Optional[Any]						=			outputs.encoder_hidden_states[0]
								encoder_hidden_states.retain_grad()

								snake_case_   : int						=			outputs.pixel_decoder_hidden_states[0]
								pixel_decoder_hidden_states.retain_grad()

								snake_case_   : Optional[int]						=			outputs.transformer_decoder_hidden_states[0]
								transformer_decoder_hidden_states.retain_grad()

								snake_case_   : Optional[int]						=			outputs.attentions[0]
								attentions.retain_grad()

								outputs.loss.backward(retain_graph=_lowercase   )

								self.assertIsNotNone(encoder_hidden_states.grad   )
								self.assertIsNotNone(pixel_decoder_hidden_states.grad   )
								self.assertIsNotNone(transformer_decoder_hidden_states.grad   )
								self.assertIsNotNone(attentions.grad   )




__lowerCAmelCase       :					List[str]      					=					1e-4
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : str						=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
						return image






@require_vision
@slow
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@cached_property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								return "facebook/mask2former-swin-small-coco-instance"





		@cached_property
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints   ) if is_vision_available() else None





		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			MaskaFormerModel.from_pretrained(self.model_checkpoints   ).to(_lowercase   )
								snake_case_   : List[str]						=			self.default_image_processor
								snake_case_   : List[str]						=			prepare_img()
								snake_case_   : Any						=			image_processor(_lowercase							,    return_tensors="""pt"""   ).to(_lowercase   )
								snake_case_   : List[Any]						=			inputs["""pixel_values"""].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
								# check size
								self.assertEqual(_lowercase							,    (1, 3, 3_8_4, 3_8_4)   )

								with torch.no_grad():
														snake_case_   : Union[str, Any]						=			model(**_lowercase   )

								snake_case_   : Optional[int]						=			torch.tensor(
								    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]   ).to(_lowercase   )
								self.assertTrue(
								    torch.allclose(
								        outputs.encoder_last_hidden_state[0, 0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )

								snake_case_   : List[str]						=			torch.tensor(
								    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]   ).to(_lowercase   )
								self.assertTrue(
								    torch.allclose(
								        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )

								snake_case_   : List[str]						=			torch.tensor(
								    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]   ).to(_lowercase   )
								self.assertTrue(
								    torch.allclose(
								        outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(_lowercase   ).eval()
								snake_case_   : str						=			self.default_image_processor
								snake_case_   : Any						=			prepare_img()
								snake_case_   : str						=			image_processor(_lowercase							,    return_tensors="""pt"""   ).to(_lowercase   )
								snake_case_   : int						=			inputs["""pixel_values"""].shape
								# check size is divisible by 32
								self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0   )
								# check size
								self.assertEqual(_lowercase							,    (1, 3, 3_8_4, 3_8_4)   )

								with torch.no_grad():
														snake_case_   : List[str]						=			model(**_lowercase   )
								# masks_queries_logits
								snake_case_   : Tuple						=			outputs.masks_queries_logits
								self.assertEqual(
								    masks_queries_logits.shape							,    (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   )
								snake_case_   : Union[str, Any]						=			[
								    [-8.7839, -9.0056, -8.8121],
								    [-7.4104, -7.0313, -6.5401],
								    [-6.6105, -6.3427, -6.4675],
								]
								snake_case_   : int						=			torch.tensor(_lowercase   ).to(_lowercase   )
								self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )
								# class_queries_logits
								snake_case_   : Dict						=			outputs.class_queries_logits
								self.assertEqual(class_queries_logits.shape							,    (1, model.config.num_queries, model.config.num_labels + 1)   )
								snake_case_   : int						=			torch.tensor(
								    [
								        [1.8324, -8.0835, -4.1922],
								        [0.8450, -9.0050, -3.6053],
								        [0.3045, -7.7293, -3.0275],
								    ]   ).to(_lowercase   )
								self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,    _lowercase							,    atol=_lowercase   )   )





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints   ).to(_lowercase   ).eval()
								snake_case_   : Union[str, Any]						=			self.default_image_processor

								snake_case_   : List[Any]						=			image_processor(
								    [np.zeros((3, 8_0_0, 1_3_3_3)   ), np.zeros((3, 8_0_0, 1_3_3_3)   )]							,    segmentation_maps=[np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   ), np.zeros((3_8_4, 3_8_4)   ).astype(np.floataa   )]							,    return_tensors="""pt"""							,    )

								snake_case_   : List[Any]						=			inputs["""pixel_values"""].to(_lowercase   )
								snake_case_   : Optional[int]						=			[el.to(_lowercase   ) for el in inputs["""mask_labels"""]]
								snake_case_   : Any						=			[el.to(_lowercase   ) for el in inputs["""class_labels"""]]

								with torch.no_grad():
														snake_case_   : Optional[Any]						=			model(**_lowercase   )

								self.assertTrue(outputs.loss is not None   )






"""simple docstring"""





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
					import jax.numpy as jnp

					from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
					    FlaxRobertaPreLayerNormForCausalLM,
					    FlaxRobertaPreLayerNormForMaskedLM,
					    FlaxRobertaPreLayerNormForMultipleChoice,
					    FlaxRobertaPreLayerNormForQuestionAnswering,
					    FlaxRobertaPreLayerNormForSequenceClassification,
					    FlaxRobertaPreLayerNormForTokenClassification,
					    FlaxRobertaPreLayerNormModel,
					)



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase=1_3							,    _lowercase=7							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=True							,    _lowercase=9_9							,    _lowercase=3_2							,    _lowercase=5							,    _lowercase=4							,    _lowercase=3_7							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=1_6							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=4							,    ) ->						Union[str, Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

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

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

								snake_case_   : List[str]						=			None
								if self.use_token_type_ids:
														snake_case_   : int						=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size   )

								snake_case_   : Tuple						=			RobertaPreLayerNormConfig(
								    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    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								snake_case_   : Any						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			config_and_inputs
								snake_case_   : int						=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : Tuple						=			self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : Any						=			config_and_inputs

								snake_case_   : Union[str, Any]						=			True
								snake_case_   : List[Any]						=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
								snake_case_   : Optional[int]						=			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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       True

		_lowerCamelCase			  =       (
		    (
		        FlaxRobertaPreLayerNormModel,
		        FlaxRobertaPreLayerNormForCausalLM,
		        FlaxRobertaPreLayerNormForMaskedLM,
		        FlaxRobertaPreLayerNormForSequenceClassification,
		        FlaxRobertaPreLayerNormForTokenClassification,
		        FlaxRobertaPreLayerNormForMultipleChoice,
		        FlaxRobertaPreLayerNormForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Any						=			FlaxRobertaPreLayerNormModelTester(self   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								for model_class_name in self.all_model_classes:
														snake_case_   : Tuple						=			model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,    from_pt=_lowercase   )
														snake_case_   : Tuple						=			model(np.ones((1, 1)   )   )
														self.assertIsNotNone(_lowercase   )




@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								snake_case_   : Union[str, Any]						=			FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,    from_pt=_lowercase   )

								snake_case_   : List[str]						=			np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							,    dtype=jnp.intaa   )
								snake_case_   : str						=			model(_lowercase   )[0]
								snake_case_   : int						=			[1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape   )							,    _lowercase   )
								# compare the actual values for a slice.
								snake_case_   : Tuple						=			np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]							,    dtype=np.floataa   )
								self.assertTrue(np.allclose(output[:, :3, :3]							,    _lowercase							,    atol=1E-4   )   )





		@slow
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : Any						=			FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""							,    from_pt=_lowercase   )

								snake_case_   : Dict						=			np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]							,    dtype=jnp.intaa   )
								snake_case_   : Any						=			model(_lowercase   )[0]
								# compare the actual values for a slice.
								snake_case_   : Optional[Any]						=			np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]							,    dtype=np.floataa   )
								self.assertTrue(np.allclose(output[:, :3, :3]							,    _lowercase							,    atol=1E-4   )   )

