
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__ :       Any						     =			{"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Union[str, Any]						     =			[
					    "OPT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "OPTForCausalLM",
					    "OPTModel",
					    "OPTPreTrainedModel",
					    "OPTForSequenceClassification",
					    "OPTForQuestionAnswering",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Optional[Any]						     =			["TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Optional[Any]						     =			[
					    "FlaxOPTForCausalLM",
					    "FlaxOPTModel",
					    "FlaxOPTPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCamelCase__ :       List[str]						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







"""simple docstring"""






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Tuple		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Any=3		, lowerCamelCase_    :Union[str, Any]=4		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :str=7		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :int=True		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Optional[Any]=99		, lowerCamelCase_    :Any=36		, lowerCamelCase_    :Any=3		, lowerCamelCase_    :str=4		, lowerCamelCase_    :Tuple=37		, lowerCamelCase_    :Optional[int]="gelu"		, lowerCamelCase_    :str=0.1		, lowerCamelCase_    :Optional[int]=0.1		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :Optional[Any]=16		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :Optional[int]=0.0_2		, lowerCamelCase_    :int=6		, lowerCamelCase_    :str=6		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :Union[str, Any]=4		, lowerCamelCase_    :List[Any]=None		, lowerCamelCase_    :Tuple=10_00		, )							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= parent
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     List[str]				= num_channels
			SCREAMING_SNAKE_CASE       :     str				= image_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= patch_size
			SCREAMING_SNAKE_CASE       :     Tuple				= text_seq_length
			SCREAMING_SNAKE_CASE       :     Optional[int]				= is_training
			SCREAMING_SNAKE_CASE       :     Dict				= use_input_mask
			SCREAMING_SNAKE_CASE       :     Any				= use_token_type_ids
			SCREAMING_SNAKE_CASE       :     List[Any]				= use_labels
			SCREAMING_SNAKE_CASE       :     List[Any]				= vocab_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_size
			SCREAMING_SNAKE_CASE       :     Tuple				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[int]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     List[str]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Any				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     int				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= type_sequence_label_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= initializer_range
			SCREAMING_SNAKE_CASE       :     Dict				= coordinate_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= shape_size
			SCREAMING_SNAKE_CASE       :     Dict				= num_labels
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_choices
			SCREAMING_SNAKE_CASE       :     List[str]				= scope
			SCREAMING_SNAKE_CASE       :     Optional[int]				= range_bbox

			# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
			SCREAMING_SNAKE_CASE       :     str				= text_seq_length
			SCREAMING_SNAKE_CASE       :     int				= (image_size // patch_size) ** 2 + 1
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.text_seq_length + self.image_seq_length





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= ids_tensor([self.batch_size, self.text_seq_length]		, self.vocab_size					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= ids_tensor([self.batch_size, self.text_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]:
									SCREAMING_SNAKE_CASE       :     str				= bbox[i, j, 3]
									SCREAMING_SNAKE_CASE       :     List[str]				= bbox[i, j, 1]
									SCREAMING_SNAKE_CASE       :     Any				= t
							if bbox[i, j, 2] < bbox[i, j, 0]:
									SCREAMING_SNAKE_CASE       :     Any				= bbox[i, j, 2]
									SCREAMING_SNAKE_CASE       :     Any				= bbox[i, j, 0]
									SCREAMING_SNAKE_CASE       :     Optional[Any]				= t

			SCREAMING_SNAKE_CASE       :     Dict				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= None
			if self.use_input_mask:
					SCREAMING_SNAKE_CASE       :     Dict				= random_attention_mask([self.batch_size, self.text_seq_length]					)

			SCREAMING_SNAKE_CASE       :     Any				= None
			if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE       :     Tuple				= ids_tensor([self.batch_size, self.text_seq_length]		, self.type_vocab_size					)

			SCREAMING_SNAKE_CASE       :     Tuple				= None
			SCREAMING_SNAKE_CASE       :     Optional[int]				= None
			if self.use_labels:
					SCREAMING_SNAKE_CASE       :     List[str]				= ids_tensor([self.batch_size]		, self.type_sequence_label_size					)
					SCREAMING_SNAKE_CASE       :     Dict				= ids_tensor([self.batch_size, self.text_seq_length]		, self.num_labels					)

			SCREAMING_SNAKE_CASE       :     List[str]				= LayoutLMvaConfig(
			    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		, coordinate_size=self.coordinate_size		, shape_size=self.shape_size		, input_size=self.image_size		, patch_size=self.patch_size		, )

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





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :str		, lowerCamelCase_    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Union[str, Any]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= LayoutLMvaModel(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			# text + image
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, pixel_values=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, token_type_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_					)

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

			# text only
			SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCamelCase_					)
			self.parent.assertEqual(
			    result.last_hidden_state.shape		, (self.batch_size, self.text_seq_length, self.hidden_size)					)

			# image only
			SCREAMING_SNAKE_CASE       :     List[str]				= model(pixel_values=lowerCamelCase_					)
			self.parent.assertEqual(
			    result.last_hidden_state.shape		, (self.batch_size, self.image_seq_length, self.hidden_size)					)





	def   __lowerCAmelCase (						self    :str		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Any		, lowerCamelCase_    :int		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :List[str]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.num_labels
			SCREAMING_SNAKE_CASE       :     Dict				= LayoutLMvaForSequenceClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     int				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_		, )
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)					)





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Any		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :int		, lowerCamelCase_    :str		, lowerCamelCase_    :Any					)							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.num_labels
			SCREAMING_SNAKE_CASE       :     int				= LayoutLMvaForTokenClassification(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_		, )
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.text_seq_length, self.num_labels)					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= LayoutLMvaForQuestionAnswering(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, start_positions=lowerCamelCase_		, end_positions=lowerCamelCase_		, )
			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    :Optional[int]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.prepare_config_and_inputs()
			(
			    (
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					
			)       :     Tuple				= config_and_inputs
			SCREAMING_SNAKE_CASE       :     Dict				= {
			    '''input_ids''': input_ids,
			    '''bbox''': bbox,
			    '''pixel_values''': pixel_values,
			    '''token_type_ids''': token_type_ids,
			    '''attention_mask''': input_mask,
			}
			return config, inputs_dict





@require_torch
class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False

	UpperCamelCase				=  (
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  (
	    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :str		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :List[str]					)							->			Union[str, Any]:







			'''simple docstring'''




			return True





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= LayoutLMvaModelTester(self					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= ConfigTester(self		, config_class=lowerCamelCase_		, hidden_size=37					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Dict		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :str=False					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= copy.deepcopy(lowerCamelCase_					)
			if model_class in get_values(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= {
					    k: v.unsqueeze(1					).expand(-1		, self.model_tester.num_choices		, -1					).contiguous()
					    if isinstance(lowerCamelCase_		, torch.Tensor					) and v.ndim > 1
					    else v
					    for k, v in inputs_dict.items()
					}
			if return_labels:
					if model_class in get_values(lowerCamelCase_					):
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.ones(self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in get_values(lowerCamelCase_					):
							SCREAMING_SNAKE_CASE       :     List[Any]				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Any				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in [
					    *get_values(lowerCamelCase_					),
					]:
							SCREAMING_SNAKE_CASE       :     str				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in [
					    *get_values(lowerCamelCase_					),
					]:
							SCREAMING_SNAKE_CASE       :     Dict				= torch.zeros(
							    (self.model_tester.batch_size, self.model_tester.text_seq_length)		, dtype=torch.long		, device=lowerCamelCase_		, )

			return inputs_dict





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







			'''simple docstring'''




			self.config_tester.run_common_tests()





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					SCREAMING_SNAKE_CASE       :     str				= type
					self.model_tester.create_and_check_model(*lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_					)







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







			'''simple docstring'''




			for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= LayoutLMvaModel.from_pretrained(lowerCamelCase_					)
					self.assertIsNotNone(lowerCamelCase_					)





def __A						(   )->							Optional[int]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image




@require_torch
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@cached_property
	def   __lowerCAmelCase (						self    :str					)							->			int:







			'''simple docstring'''




			return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_					) if is_vision_available() else None







	@slow
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''					).to(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Any				= self.default_image_processor
			SCREAMING_SNAKE_CASE       :     List[Any]				= prepare_img()
			SCREAMING_SNAKE_CASE       :     Tuple				= image_processor(images=lowerCamelCase_		, return_tensors='''pt'''					).pixel_values.to(lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor([[1, 2]]					)
			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]					).unsqueeze(0					)

			# forward pass
			SCREAMING_SNAKE_CASE       :     Tuple				= model(
			    input_ids=input_ids.to(lowerCamelCase_					)		, bbox=bbox.to(lowerCamelCase_					)		, pixel_values=pixel_values.to(lowerCamelCase_					)		, )

			# verify the logits
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.Size((1, 1_99, 7_68)					)
			self.assertEqual(outputs.last_hidden_state.shape		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor(
			    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]					).to(lowerCamelCase_					)

			self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		, lowerCamelCase_		, atol=1E-4					)					)





