


"""simple docstring"""
import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
				from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[Any]=13		,       UpperCAmelCase_							:      Any=32		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      Optional[Any]=3		,       UpperCAmelCase_							:      List[str]=16		,       UpperCAmelCase_							:      str=[1, 2, 1]		,       UpperCAmelCase_							:      str=[2, 2, 4]		,       UpperCAmelCase_							:      str=2		,       UpperCAmelCase_							:      Union[str, Any]=2.0		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      List[str]=0.0		,       UpperCAmelCase_							:      int=0.0		,       UpperCAmelCase_							:      int=0.1		,       UpperCAmelCase_							:      List[Any]="gelu"		,       UpperCAmelCase_							:      List[Any]=False		,       UpperCAmelCase_							:      str=True		,       UpperCAmelCase_							:      int=0.02		,       UpperCAmelCase_							:      Dict=1E-5		,       UpperCAmelCase_							:      str=True		,       UpperCAmelCase_							:      int=None		,       UpperCAmelCase_							:      Dict=True		,       UpperCAmelCase_							:      List[Any]=10		,       UpperCAmelCase_							:      List[Any]=8		,       )					->int:



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		image_size
														snake_case_           =		patch_size
														snake_case_           =		num_channels
														snake_case_           =		embed_dim
														snake_case_           =		depths
														snake_case_           =		num_heads
														snake_case_           =		window_size
														snake_case_           =		mlp_ratio
														snake_case_           =		qkv_bias
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		drop_path_rate
														snake_case_           =		hidden_act
														snake_case_           =		use_absolute_embeddings
														snake_case_           =		patch_norm
														snake_case_           =		layer_norm_eps
														snake_case_           =		initializer_range
														snake_case_           =		is_training
														snake_case_           =		scope
														snake_case_           =		use_labels
														snake_case_           =		type_sequence_label_size
														snake_case_           =		encoder_stride



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

														snake_case_           =		None
														if self.use_labels:
																					snake_case_           =		ids_tensor([self.batch_size]		,       self.type_sequence_label_size       )

														snake_case_           =		self.get_config()

														return config, pixel_values, labels



							def 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



														return SwinvaConfig(
														    image_size=self.image_size		,       patch_size=self.patch_size		,       num_channels=self.num_channels		,       embed_dim=self.embed_dim		,       depths=self.depths		,       num_heads=self.num_heads		,       window_size=self.window_size		,       mlp_ratio=self.mlp_ratio		,       qkv_bias=self.qkv_bias		,       hidden_dropout_prob=self.hidden_dropout_prob		,       attention_probs_dropout_prob=self.attention_probs_dropout_prob		,       drop_path_rate=self.drop_path_rate		,       hidden_act=self.hidden_act		,       use_absolute_embeddings=self.use_absolute_embeddings		,       path_norm=self.patch_norm		,       layer_norm_eps=self.layer_norm_eps		,       initializer_range=self.initializer_range		,       encoder_stride=self.encoder_stride		,       )



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      str       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		SwinvaModel(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )

														snake_case_           =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
														snake_case_           =		int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

														self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, expected_seq_len, expected_dim)       )



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		SwinvaForMaskedImageModeling(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_       )
														self.parent.assertEqual(
														    result.logits.shape		,       (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

														# test greyscale images
														snake_case_           =		1
														snake_case_           =		SwinvaForMaskedImageModeling(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()

														snake_case_           =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
														snake_case_           =		model(UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, 1, self.image_size, self.image_size)       )



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.type_sequence_label_size
														snake_case_           =		SwinvaForImageClassification(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_		,       labels=UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



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





@require_torch
class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Any							      =    (
							    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
							)
							__lowercase:		Dict							      =    (
							    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		Optional[Any]							      =    False
							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		str							      =    False



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		SwinvaModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       embed_dim=37       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Any:



														"""simple docstring"""



														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Any:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""       )
							def 		lowerCAmelCase       (				self							:      Any       )					->Optional[Any]:



														"""simple docstring"""



														pass



							@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""       )
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      int       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					self.assertIsInstance(model.get_input_embeddings()		,       (nn.Module)       )
																					snake_case_           =		model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCAmelCase_		,       nn.Linear       )       )



							def 		lowerCAmelCase       (				self							:      int       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

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

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



							def 		lowerCAmelCase       (				self							:      str       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_           =		True

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					snake_case_           =		False
																					snake_case_           =		True
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()
																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )
																					snake_case_           =		outputs.attentions
																					snake_case_           =		len(self.model_tester.depths       )
																					self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

																					# check that output_attentions also work using config
																					del inputs_dict["output_attentions"]
																					snake_case_           =		True
																					snake_case_           =		config.window_size**2
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()
																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )
																					snake_case_           =		outputs.attentions
																					self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

																					self.assertListEqual(
																					    list(attentions[0].shape[-3:]       )		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )
																					snake_case_           =		len(UpperCAmelCase_       )

																					# Check attention is always last and order is fine
																					snake_case_           =		True
																					snake_case_           =		True
																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.eval()
																					with torch.no_grad():
																												snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )

																					if hasattr(self.model_tester		,       """num_hidden_states_types"""       ):
																												snake_case_           =		self.model_tester.num_hidden_states_types
																					else:
																												# also another +1 for reshaped_hidden_states
																												snake_case_           =		2
																					self.assertEqual(out_len + added_hidden_states		,       len(UpperCAmelCase_       )       )

																					snake_case_           =		outputs.attentions

																					self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

																					self.assertListEqual(
																					    list(self_attentions[0].shape[-3:]       )		,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,       )



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Dict       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		model_class(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()

														with torch.no_grad():
																					snake_case_           =		model(**self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_       )       )

														snake_case_           =		outputs.hidden_states

														snake_case_           =		getattr(
														    self.model_tester		,       """expected_num_hidden_layers"""		,       len(self.model_tester.depths       ) + 1       )
														self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

														# Swinv2 has a different seq_length
														snake_case_           =		(
														    config.patch_size
														    if isinstance(config.patch_size		,       collections.abc.Iterable       )
														    else (config.patch_size, config.patch_size)
														)

														snake_case_           =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]       )		,       [num_patches, self.model_tester.embed_dim]		,       )

														snake_case_           =		outputs.reshaped_hidden_states
														self.assertEqual(len(UpperCAmelCase_       )		,       UpperCAmelCase_       )

														snake_case_       ,						snake_case_       ,						snake_case_       ,						snake_case_           =		reshaped_hidden_states[0].shape
														snake_case_           =		(
														    reshaped_hidden_states[0].view(UpperCAmelCase_		,       UpperCAmelCase_		,       height * width       ).permute(0		,       2		,       1       )
														)
														self.assertListEqual(
														    list(reshaped_hidden_states.shape[-2:]       )		,       [num_patches, self.model_tester.embed_dim]		,       )



							def 		lowerCAmelCase       (				self							:      Any       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														snake_case_           =		(
														    self.model_tester.image_size
														    if isinstance(self.model_tester.image_size		,       collections.abc.Iterable       )
														    else (self.model_tester.image_size, self.model_tester.image_size)
														)

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					snake_case_           =		True

																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_           =		3

														snake_case_           =		(
														    self.model_tester.image_size
														    if isinstance(self.model_tester.image_size		,       collections.abc.Iterable       )
														    else (self.model_tester.image_size, self.model_tester.image_size)
														)
														snake_case_           =		(
														    config.patch_size
														    if isinstance(config.patch_size		,       collections.abc.Iterable       )
														    else (config.patch_size, config.patch_size)
														)

														snake_case_           =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
														snake_case_           =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

														for model_class in self.all_model_classes:
																					snake_case_           =		True
																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       (padded_height, padded_width)       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					snake_case_           =		True
																					self.check_hidden_states_output(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       (padded_height, padded_width)       )



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



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_       )



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					snake_case_           =		SwinvaModel.from_pretrained(UpperCAmelCase_       )
																					self.assertIsNotNone(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Tuple:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

														snake_case_           =		_config_zero_init(UpperCAmelCase_       )
														for model_class in self.all_model_classes:
																					snake_case_           =		model_class(config=UpperCAmelCase_       )
																					for name, param in model.named_parameters():
																												if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																																			self.assertIn(
																																			    ((param.data.mean() * 1E9).round() / 1E9).item()		,       [0.0, 1.0]		,       msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,       )





@require_vision
@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							@cached_property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Tuple:



														"""simple docstring"""



														return (
														    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       )
														    if is_vision_available()
														    else None
														)



							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""       ).to(
														    UpperCAmelCase_       )
														snake_case_           =		self.default_image_processor

														snake_case_           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
														snake_case_           =		image_processor(images=UpperCAmelCase_		,       return_tensors="""pt"""       ).to(UpperCAmelCase_       )

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(**UpperCAmelCase_       )

														# verify the logits
														snake_case_           =		torch.Size((1, 1_000)       )
														self.assertEqual(outputs.logits.shape		,       UpperCAmelCase_       )
														snake_case_           =		torch.tensor([-0.3_947, -0.4_306, 0.0_026]       ).to(UpperCAmelCase_       )
														self.assertTrue(torch.allclose(outputs.logits[0, :3]		,       UpperCAmelCase_		,       atol=1E-4       )       )







"""simple docstring"""
import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				)
				from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      Optional[int]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]=13		,       UpperCAmelCase_							:      Optional[int]=7		,       UpperCAmelCase_							:      List[str]=True		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      List[Any]=True		,       UpperCAmelCase_							:      Optional[int]=True		,       UpperCAmelCase_							:      List[str]=99		,       UpperCAmelCase_							:      Dict=24		,       UpperCAmelCase_							:      List[str]=2		,       UpperCAmelCase_							:      Optional[Any]=6		,       UpperCAmelCase_							:      int=37		,       UpperCAmelCase_							:      Optional[Any]="gelu"		,       UpperCAmelCase_							:      Optional[Any]=0.1		,       UpperCAmelCase_							:      List[Any]=0.1		,       UpperCAmelCase_							:      Any=512		,       UpperCAmelCase_							:      str=16		,       UpperCAmelCase_							:      List[str]=2		,       UpperCAmelCase_							:      Optional[int]=0.02		,       UpperCAmelCase_							:      Tuple=3		,       UpperCAmelCase_							:      Union[str, Any]=None		,       UpperCAmelCase_							:      Any=1_000		,       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		seq_length
														snake_case_           =		is_training
														snake_case_           =		use_input_mask
														snake_case_           =		use_token_type_ids
														snake_case_           =		use_labels
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		num_attention_heads
														snake_case_           =		intermediate_size
														snake_case_           =		hidden_act
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		max_position_embeddings
														snake_case_           =		type_vocab_size
														snake_case_           =		type_sequence_label_size
														snake_case_           =		initializer_range
														snake_case_           =		num_labels
														snake_case_           =		scope
														snake_case_           =		range_bbox



							def 		lowerCAmelCase       (				self							:      Tuple       )					->int:



														"""simple docstring"""



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

														snake_case_           =		ids_tensor([self.batch_size, self.seq_length, 4]		,       self.range_bbox       )
														# Ensure that bbox is legal
														for i in range(bbox.shape[0]       ):
																					for j in range(bbox.shape[1]       ):
																												if bbox[i, j, 3] < bbox[i, j, 1]:
																																			snake_case_           =		bbox[i, j, 3]
																																			snake_case_           =		bbox[i, j, 1]
																																			snake_case_           =		t
																												if bbox[i, j, 2] < bbox[i, j, 0]:
																																			snake_case_           =		bbox[i, j, 2]
																																			snake_case_           =		bbox[i, j, 0]
																																			snake_case_           =		t

														snake_case_           =		None
														if self.use_input_mask:
																					snake_case_           =		ids_tensor([self.batch_size, self.seq_length]		,       vocab_size=2       )

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

														snake_case_           =		None
														snake_case_           =		None
														if self.use_labels:
																					snake_case_           =		ids_tensor([self.batch_size]		,       self.type_sequence_label_size       )
																					snake_case_           =		ids_tensor([self.batch_size, self.seq_length]		,       self.num_labels       )

														snake_case_           =		self.get_config()

														return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels



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



														"""simple docstring"""



														return LiltConfig(
														    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		,       initializer_range=self.initializer_range		,       )



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[int]		,       )					->str:



														"""simple docstring"""



														snake_case_           =		LiltModel(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_       )
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_       )
														snake_case_           =		model(UpperCAmelCase_		,       bbox=UpperCAmelCase_       )

														self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size)       )
														self.parent.assertEqual(result.pooler_output.shape		,       (self.batch_size, self.hidden_size)       )



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Any		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]		,       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.num_labels
														snake_case_           =		LiltForTokenClassification(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(
														    UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_		,       labels=UpperCAmelCase_       )
														self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.num_labels)       )



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      List[str]		,       )					->Dict:



														"""simple docstring"""



														snake_case_           =		LiltForQuestionAnswering(config=UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														snake_case_           =		model(
														    UpperCAmelCase_		,       bbox=UpperCAmelCase_		,       attention_mask=UpperCAmelCase_		,       token_type_ids=UpperCAmelCase_		,       start_positions=UpperCAmelCase_		,       end_positions=UpperCAmelCase_		,       )
														self.parent.assertEqual(result.start_logits.shape		,       (self.batch_size, self.seq_length)       )
														self.parent.assertEqual(result.end_logits.shape		,       (self.batch_size, self.seq_length)       )



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



														"""simple docstring"""



														snake_case_           =		self.prepare_config_and_inputs()
														(
														    (
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						(
														    snake_case_
														)       ,						
														)           =		config_and_inputs
														snake_case_           =		{
														    """input_ids""": input_ids,
														    """bbox""": bbox,
														    """token_type_ids""": token_type_ids,
														    """attention_mask""": input_mask,
														}
														return config, inputs_dict





@require_torch
class      __A     (snake_case__						,		snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Optional[int]							      =    (
							    (
							        LiltModel,
							        LiltForSequenceClassification,
							        LiltForTokenClassification,
							        LiltForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowercase:		Optional[Any]							      =    (
							    {
							        """feature-extraction""": LiltModel,
							        """question-answering""": LiltForQuestionAnswering,
							        """text-classification""": LiltForSequenceClassification,
							        """token-classification""": LiltForTokenClassification,
							        """zero-shot""": LiltForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		List[str]							      =    False



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														return True



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



														"""simple docstring"""



														snake_case_           =		LiltModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       hidden_size=37       )



							def 		lowerCAmelCase       (				self							:      str       )					->List[Any]:



														"""simple docstring"""



														self.config_tester.run_common_tests()



							def 		lowerCAmelCase       (				self							:      List[str]       )					->int:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



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



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					snake_case_           =		type
																					self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_       )



							@slow
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					snake_case_           =		LiltModel.from_pretrained(UpperCAmelCase_       )
																					self.assertIsNotNone(UpperCAmelCase_       )





@require_torch
@slow
class      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base"""       ).to(UpperCAmelCase_       )

														snake_case_           =		torch.tensor([[1, 2]]		,       device=UpperCAmelCase_       )
														snake_case_           =		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]		,       device=UpperCAmelCase_       )

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(input_ids=UpperCAmelCase_		,       bbox=UpperCAmelCase_       )

														snake_case_           =		torch.Size([1, 2, 768]       )
														snake_case_           =		torch.tensor(
														    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]		,       device=UpperCAmelCase_		,       )

														self.assertTrue(outputs.last_hidden_state.shape		,       UpperCAmelCase_       )
														self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]		,       UpperCAmelCase_		,       atol=1E-3       )       )



