

import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_pytesseract_available():
						from PIL import Image

						from transformers import LayoutLMvaImageProcessor

class 				__UpperCamelCase				(					unittest.TestCase			):







	def __init__(   self: int  , __UpperCamelCase: List[str]  , __UpperCamelCase: Optional[Any]=7  , __UpperCamelCase: Union[str, Any]=3  , __UpperCamelCase: Dict=18  , __UpperCamelCase: Any=30  , __UpperCamelCase: Optional[int]=4_00  , __UpperCamelCase: List[Any]=True  , __UpperCamelCase: Tuple=None  , __UpperCamelCase: Union[str, Any]=True  , ):

						'''simple docstring'''





						__magic_name__							=    size if size is not None else {'height': 18, 'width': 18}
						__magic_name__							=    parent
						__magic_name__							=    batch_size
						__magic_name__							=    num_channels
						__magic_name__							=    image_size
						__magic_name__							=    min_resolution
						__magic_name__							=    max_resolution
						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    apply_ocr







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}




@require_torch
@require_pytesseract
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Optional[int]       				=  LayoutLMvaImageProcessor if is_pytesseract_available() else None







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    LayoutLMvaImageProcessingTester(self      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_resize'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'size'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'apply_ocr'      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class.from_dict(self.image_processor_dict      )
						self.assertEqual(image_processor.size  , {'height': 18, 'width': 18}      )

						__magic_name__							=    self.image_processing_class.from_dict(self.image_processor_dict  , size=42      )
						self.assertEqual(image_processor.size  , {'height': 42, 'width': 42}      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						pass







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , Image.Image      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , return_tensors='pt'      )
						self.assertEqual(
						    encoding.pixel_values.shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )  , )

						self.assertIsInstance(encoding.words  , __UpperCamelCase      )
						self.assertIsInstance(encoding.boxes  , __UpperCamelCase      )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , numpify=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , np.ndarray      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , torchify=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , torch.Tensor      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      ).pixel_values
						self.assertEqual(
						    encoded_images.shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.size['height'],
						        self.image_processor_tester.size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    LayoutLMvaImageProcessor()

						from datasets import load_dataset

						__magic_name__							=    load_dataset('hf-internal-testing/fixtures_docvqa'  , split='test'      )

						__magic_name__							=    Image.open(ds[0]['file']      ).convert('RGB'      )

						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      )

						self.assertEqual(encoding.pixel_values.shape  , (1, 3, 2_24, 2_24)      )
						self.assertEqual(len(encoding.words      )  , len(encoding.boxes      )      )

						# fmt: off
						# the words and boxes were obtained with Tesseract 4.1.1
						__magic_name__							=    [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
						__magic_name__							=    [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
						# fmt: on

						self.assertListEqual(encoding.words  , __UpperCamelCase      )
						self.assertListEqual(encoding.boxes  , __UpperCamelCase      )

						# with apply_OCR = False
						__magic_name__							=    LayoutLMvaImageProcessor(apply_ocr=__UpperCamelCase      )

						__magic_name__							=    image_processing(__UpperCamelCase  , return_tensors='pt'      )

						self.assertEqual(encoding.pixel_values.shape  , (1, 3, 2_24, 2_24)      )







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType


A__						=							logging.get_logger(__name__)

A__						=							{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "layoutlmv3"







	def __init__(   self: Tuple  , __UpperCamelCase: List[Any]=5_02_65  , __UpperCamelCase: Union[str, Any]=7_68  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=12  , __UpperCamelCase: List[Any]=30_72  , __UpperCamelCase: Union[str, Any]="gelu"  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: List[Any]=5_12  , __UpperCamelCase: int=2  , __UpperCamelCase: Union[str, Any]=0.02  , __UpperCamelCase: Dict=1E-5  , __UpperCamelCase: int=1  , __UpperCamelCase: Dict=0  , __UpperCamelCase: Tuple=2  , __UpperCamelCase: Any=10_24  , __UpperCamelCase: Union[str, Any]=1_28  , __UpperCamelCase: Dict=1_28  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=32  , __UpperCamelCase: int=1_28  , __UpperCamelCase: List[str]=64  , __UpperCamelCase: Union[str, Any]=2_56  , __UpperCamelCase: int=True  , __UpperCamelCase: Optional[int]=True  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Union[str, Any]=2_24  , __UpperCamelCase: Tuple=3  , __UpperCamelCase: Any=16  , __UpperCamelCase: List[Any]=None  , **__UpperCamelCase: str  , ):

						'''simple docstring'''





						super().__init__(
						    vocab_size=__UpperCamelCase  , hidden_size=__UpperCamelCase  , num_hidden_layers=__UpperCamelCase  , num_attention_heads=__UpperCamelCase  , intermediate_size=__UpperCamelCase  , hidden_act=__UpperCamelCase  , hidden_dropout_prob=__UpperCamelCase  , attention_probs_dropout_prob=__UpperCamelCase  , max_position_embeddings=__UpperCamelCase  , type_vocab_size=__UpperCamelCase  , initializer_range=__UpperCamelCase  , layer_norm_eps=__UpperCamelCase  , pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , **__UpperCamelCase  , )
						__magic_name__							=    max_ad_position_embeddings
						__magic_name__							=    coordinate_size
						__magic_name__							=    shape_size
						__magic_name__							=    has_relative_attention_bias
						__magic_name__							=    rel_pos_bins
						__magic_name__							=    max_rel_pos
						__magic_name__							=    has_spatial_attention_bias
						__magic_name__							=    rel_ad_pos_bins
						__magic_name__							=    max_rel_ad_pos
						__magic_name__							=    text_embed
						__magic_name__							=    visual_embed
						__magic_name__							=    input_size
						__magic_name__							=    num_channels
						__magic_name__							=    patch_size
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Any       				=  version.parse("1.12"			)







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]      )
						else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return 1E-5







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return 12







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: "ProcessorMixin"  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional["TensorType"] = None  , __UpperCamelCase: int = 3  , __UpperCamelCase: int = 40  , __UpperCamelCase: int = 40  , ):

						'''simple docstring'''





						setattr(processor.image_processor  , 'apply_ocr'  , __UpperCamelCase      )

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_batch  , num_token_to_add=0      )
						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__magic_name__							=    processor.tokenizer.num_special_tokens_to_add(__UpperCamelCase      )
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_sequence  , num_token_to_add=__UpperCamelCase      )
						# Generate dummy inputs according to compute batch and sequence
						__magic_name__							=    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						__magic_name__							=    [[[48, 84, 73, 1_28]]] * batch_size

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
						__magic_name__							=    self._generate_dummy_images(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						__magic_name__							=    dict(
						    processor(
						        __UpperCamelCase  , text=__UpperCamelCase  , boxes=__UpperCamelCase  , return_tensors=__UpperCamelCase  , )      )

						return inputs




