

'''simple docstring'''

import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput




class 	A_			(    _snake_case	,       _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  1


     @register_to_config
     def __init__(		self			:					Any					,      lowercase_			:					int = 1_000					,      lowercase_			:					Optional[Union[np.ndarray, List[float]]] = None	)						->     str:
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(lowercase_	)

      # standard deviation of the initial noise distribution
      UpperCAmelCase	:     Optional[Any]						 =			1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      UpperCAmelCase	:     List[str]						 =			4

      # running values
      UpperCAmelCase	:     Optional[Any]						 =			[]


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					int					,      lowercase_			:					Union[str, torch.device] = None	)						->     List[str]:
      UpperCAmelCase	:     Optional[int]						 =			num_inference_steps
      UpperCAmelCase	:     List[Any]						 =			torch.linspace(1					,      0					,      num_inference_steps + 1	)[:-1]
      UpperCAmelCase	:     str						 =			torch.cat([steps, torch.tensor([0.0]	)]	)

      if self.config.trained_betas is not None:
       UpperCAmelCase	:     str						 =			torch.tensor(self.config.trained_betas					,      dtype=torch.floataa	)
      else:
       UpperCAmelCase	:     List[Any]						 =			torch.sin(steps * math.pi / 2	) ** 2

      UpperCAmelCase	:     Union[str, Any]						 =			(1.0 - self.betas**2) ** 0.5

      UpperCAmelCase	:     List[str]						 =			(torch.atana(self.betas					,      self.alphas	) / math.pi * 2)[:-1]
      UpperCAmelCase	:     Any						 =			timesteps.to(lowercase_	)

      UpperCAmelCase	:     Tuple						 =			[]


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					torch.FloatTensor					,      lowercase_			:					int					,      lowercase_			:					torch.FloatTensor					,      lowercase_			:					bool = True					,      )						->     Union[SchedulerOutput, Tuple]:
      if self.num_inference_steps is None:
       raise ValueError(
           'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'	)

      UpperCAmelCase	:     Optional[int]						 =			(self.timesteps == timestep).nonzero().item()
      UpperCAmelCase	:     Dict						 =			timestep_index + 1

      UpperCAmelCase	:     Tuple						 =			sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(lowercase_	)

      if len(self.ets	) == 1:
       UpperCAmelCase	:     Tuple						 =			self.ets[-1]
      elif len(self.ets	) == 2:
       UpperCAmelCase	:     Optional[Any]						 =			(3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets	) == 3:
       UpperCAmelCase	:     str						 =			(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
       UpperCAmelCase	:     Dict						 =			(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      UpperCAmelCase	:     Dict						 =			self._get_prev_sample(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_	)

      if not return_dict:
       return (prev_sample,)

      return SchedulerOutput(prev_sample=lowercase_	)


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					torch.FloatTensor					,      *lowercase_			:					Union[str, Any]					,      **lowercase_			:					Optional[Any]	)						->     torch.FloatTensor:
      return sample


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					str					,      lowercase_			:					List[str]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Dict	)						->     Dict:
      UpperCAmelCase	:     List[str]						 =			self.alphas[timestep_index]
      UpperCAmelCase	:     str						 =			self.betas[timestep_index]

      UpperCAmelCase	:     str						 =			self.alphas[prev_timestep_index]
      UpperCAmelCase	:     List[str]						 =			self.betas[prev_timestep_index]

      UpperCAmelCase	:     Dict						 =			(sample - sigma * ets) / max(lowercase_					,      1E-8	)
      UpperCAmelCase	:     Optional[int]						 =			next_alpha * pred + ets * next_sigma

      return prev_sample




     def __len__(		self			:					List[Any]	)						->     Tuple:
      return self.config.num_train_timesteps







'''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 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]=2					,      lowercase_			:					Optional[Any]=3					,      lowercase_			:					List[Any]=4					,      lowercase_			:					Union[str, Any]=2					,      lowercase_			:					List[Any]=7					,      lowercase_			:					Any=True					,      lowercase_			:					Tuple=True					,      lowercase_			:					List[str]=True					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					str=99					,      lowercase_			:					str=36					,      lowercase_			:					int=3					,      lowercase_			:					int=4					,      lowercase_			:					Any=37					,      lowercase_			:					str="gelu"					,      lowercase_			:					List[str]=0.1					,      lowercase_			:					Optional[int]=0.1					,      lowercase_			:					int=512					,      lowercase_			:					int=16					,      lowercase_			:					Dict=2					,      lowercase_			:					Dict=0.02					,      lowercase_			:					Optional[int]=6					,      lowercase_			:					Tuple=6					,      lowercase_			:					Any=3					,      lowercase_			:					Dict=4					,      lowercase_			:					Any=None					,      lowercase_			:					Tuple=1_000					,      )						->     Tuple:
      UpperCAmelCase	:     List[Any]						 =			parent
      UpperCAmelCase	:     List[Any]						 =			batch_size
      UpperCAmelCase	:     List[Any]						 =			num_channels
      UpperCAmelCase	:     Optional[int]						 =			image_size
      UpperCAmelCase	:     int						 =			patch_size
      UpperCAmelCase	:     Tuple						 =			text_seq_length
      UpperCAmelCase	:     int						 =			is_training
      UpperCAmelCase	:     Any						 =			use_input_mask
      UpperCAmelCase	:     Optional[int]						 =			use_token_type_ids
      UpperCAmelCase	:     int						 =			use_labels
      UpperCAmelCase	:     Dict						 =			vocab_size
      UpperCAmelCase	:     List[str]						 =			hidden_size
      UpperCAmelCase	:     Any						 =			num_hidden_layers
      UpperCAmelCase	:     str						 =			num_attention_heads
      UpperCAmelCase	:     Tuple						 =			intermediate_size
      UpperCAmelCase	:     Optional[int]						 =			hidden_act
      UpperCAmelCase	:     str						 =			hidden_dropout_prob
      UpperCAmelCase	:     Any						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Tuple						 =			max_position_embeddings
      UpperCAmelCase	:     List[str]						 =			type_vocab_size
      UpperCAmelCase	:     List[str]						 =			type_sequence_label_size
      UpperCAmelCase	:     int						 =			initializer_range
      UpperCAmelCase	:     Optional[int]						 =			coordinate_size
      UpperCAmelCase	:     Optional[int]						 =			shape_size
      UpperCAmelCase	:     str						 =			num_labels
      UpperCAmelCase	:     str						 =			num_choices
      UpperCAmelCase	:     int						 =			scope
      UpperCAmelCase	:     Tuple						 =			range_bbox

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


     def        UpperCAmelCase_	(		self			:					int	)						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[int]						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.vocab_size	)

      UpperCAmelCase	:     Union[str, 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]:
         UpperCAmelCase	:     int						 =			bbox[i, j, 3]
         UpperCAmelCase	:     List[Any]						 =			bbox[i, j, 1]
         UpperCAmelCase	:     Union[str, Any]						 =			t
        if bbox[i, j, 2] < bbox[i, j, 0]:
         UpperCAmelCase	:     Tuple						 =			bbox[i, j, 2]
         UpperCAmelCase	:     List[str]						 =			bbox[i, j, 0]
         UpperCAmelCase	:     List[str]						 =			t

      UpperCAmelCase	:     List[str]						 =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      UpperCAmelCase	:     int						 =			None
      if self.use_input_mask:
       UpperCAmelCase	:     Union[str, Any]						 =			random_attention_mask([self.batch_size, self.text_seq_length]	)

      UpperCAmelCase	:     int						 =			None
      if self.use_token_type_ids:
       UpperCAmelCase	:     List[Any]						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.type_vocab_size	)

      UpperCAmelCase	:     Dict						 =			None
      UpperCAmelCase	:     Dict						 =			None
      if self.use_labels:
       UpperCAmelCase	:     Dict						 =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size	)
       UpperCAmelCase	:     int						 =			ids_tensor([self.batch_size, self.text_seq_length]					,      self.num_labels	)

      UpperCAmelCase	:     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        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					str					,      lowercase_			:					Tuple					,      lowercase_			:					Any					,      lowercase_			:					Optional[int]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					str					,      lowercase_			:					Optional[Any]	)						->     Any:
      UpperCAmelCase	:     Dict						 =			LayoutLMvaModel(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()

      # text + image
      UpperCAmelCase	:     Optional[Any]						 =			model(lowercase_					,      pixel_values=lowercase_	)
      UpperCAmelCase	:     str						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_	)
      UpperCAmelCase	:     Any						 =			model(lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      token_type_ids=lowercase_	)
      UpperCAmelCase	:     str						 =			model(lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_	)

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

      # text only
      UpperCAmelCase	:     List[Any]						 =			model(lowercase_	)
      self.parent.assertEqual(
          result.last_hidden_state.shape					,      (self.batch_size, self.text_seq_length, self.hidden_size)	)

      # image only
      UpperCAmelCase	:     Dict						 =			model(pixel_values=lowercase_	)
      self.parent.assertEqual(
          result.last_hidden_state.shape					,      (self.batch_size, self.image_seq_length, self.hidden_size)	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					int					,      lowercase_			:					Tuple					,      lowercase_			:					List[Any]					,      lowercase_			:					Tuple					,      lowercase_			:					Tuple					,      lowercase_			:					str					,      lowercase_			:					Any					,      lowercase_			:					Union[str, Any]	)						->     Union[str, Any]:
      UpperCAmelCase	:     List[Any]						 =			self.num_labels
      UpperCAmelCase	:     Union[str, Any]						 =			LayoutLMvaForSequenceClassification(lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     int						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_					,      )
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int					,      lowercase_			:					str					,      lowercase_			:					Any					,      lowercase_			:					int					,      lowercase_			:					Any					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					int	)						->     Any:
      UpperCAmelCase	:     Optional[int]						 =			self.num_labels
      UpperCAmelCase	:     int						 =			LayoutLMvaForTokenClassification(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Optional[Any]						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_					,      )
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.text_seq_length, self.num_labels)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Tuple					,      lowercase_			:					Tuple					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					List[str]					,      lowercase_			:					Tuple					,      lowercase_			:					Any					,      lowercase_			:					Optional[int]	)						->     Optional[int]:
      UpperCAmelCase	:     Union[str, Any]						 =			LayoutLMvaForQuestionAnswering(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     List[str]						 =			model(
          lowercase_					,      bbox=lowercase_					,      pixel_values=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      start_positions=lowercase_					,      end_positions=lowercase_					,      )
      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        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     List[str]						 =			self.prepare_config_and_inputs()
      (
          (
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			
      )	:     Optional[int]						 =			config_and_inputs
      UpperCAmelCase	:     Optional[Any]						 =			{
          '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 	A_			(    _snake_case	,       _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  False
     UpperCAmelCase_						:     Dict  		=  False
     UpperCAmelCase_						:     List[Any]  		=  False

     UpperCAmelCase_						:     List[str]  		=  (
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase_						:     List[str]  		=  (
         {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
         if is_torch_available()
         else {}
     )


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int					,      lowercase_			:					List[Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					Optional[int]					,      lowercase_			:					Optional[Any]	)						->     Union[str, Any]:
      # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
      # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
      # the sequence dimension of the text embedding only.
      # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
      return True


     def        UpperCAmelCase_	(		self			:					str	)						->     Any:
      UpperCAmelCase	:     Union[str, Any]						 =			LayoutLMvaModelTester(self	)
      UpperCAmelCase	:     Optional[Any]						 =			ConfigTester(self					,      config_class=lowercase_					,      hidden_size=37	)


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

      return inputs_dict


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Union[str, Any]:
      self.config_tester.run_common_tests()


     def        UpperCAmelCase_	(		self			:					Any	)						->     Any:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     int:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       UpperCAmelCase	:     Optional[int]						 =			type
       self.model_tester.create_and_check_model(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     str:
      UpperCAmelCase	:     List[str]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Any:
      UpperCAmelCase	:     Dict						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Any:
      UpperCAmelCase	:     Tuple						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowercase_	)




     @slow
     def        UpperCAmelCase_	(		self			:					Any	)						->     Optional[Any]:
      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:     int						 =			LayoutLMvaModel.from_pretrained(lowercase_	)
       self.assertIsNotNone(lowercase_	)




def 				UpperCamelCase(					):
 UpperCAmelCase	:     List[str]						 =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
 return image




@require_torch
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     @cached_property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Dict:
      return LayoutLMvaImageProcessor(apply_ocr=lowercase_	) if is_vision_available() else None




     @slow
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      UpperCAmelCase	:     int						 =			LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	).to(lowercase_	)

      UpperCAmelCase	:     Dict						 =			self.default_image_processor
      UpperCAmelCase	:     str						 =			prepare_img()
      UpperCAmelCase	:     Optional[Any]						 =			image_processor(images=lowercase_					,      return_tensors='pt'	).pixel_values.to(lowercase_	)

      UpperCAmelCase	:     int						 =			torch.tensor([[1, 2]]	)
      UpperCAmelCase	:     Tuple						 =			torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]	).unsqueeze(0	)

      # forward pass
      UpperCAmelCase	:     Dict						 =			model(
          input_ids=input_ids.to(lowercase_	)					,      bbox=bbox.to(lowercase_	)					,      pixel_values=pixel_values.to(lowercase_	)					,      )

      # verify the logits
      UpperCAmelCase	:     Optional[Any]						 =			torch.Size((1, 199, 768)	)
      self.assertEqual(outputs.last_hidden_state.shape					,      lowercase_	)

      UpperCAmelCase	:     List[str]						 =			torch.tensor(
          [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]	).to(lowercase_	)

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




