"""simple docstring"""





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	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    ) ->      Optional[Any]:
             lowercase__    :	str     					=			size if size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	int     					=			parent
             lowercase__    :	List[Any]     					=			batch_size
             lowercase__    :	Optional[int]     					=			num_channels
             lowercase__    :	Union[str, Any]     					=			image_size
             lowercase__    :	List[Any]     					=			min_resolution
             lowercase__    :	Any     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	Union[str, Any]     					=			apply_ocr


      def 	_UpperCAmelCase (     self							) ->      Any:
             return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						LayoutLMvaImageProcessor if is_pytesseract_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Dict     					=			LayoutLMvaImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a	,    'do_resize'							)							)
             self.assertTrue(hasattr(a	,    'size'							)							)
             self.assertTrue(hasattr(a	,    'apply_ocr'							)							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Dict     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'height': 1_8, 'width': 1_8}							)

             lowercase__    :	str     					=			self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2							)
             self.assertEqual(image_processor.size	,    {'height': 4_2, 'width': 4_2}							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             pass


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             # Initialize image_processing
             lowercase__    :	Tuple     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	str     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Dict     					=			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	,    a							)
             self.assertIsInstance(encoding.boxes	,    a							)

             # Test batched
             lowercase__    :	Optional[int]     					=			image_processing(a	,    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 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	int     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	int     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    np.ndarray							)

             # Test not batched input
             lowercase__    :	Dict     					=			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
             lowercase__    :	Optional[int]     					=			image_processing(a	,    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 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	Tuple     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[int]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a	,    torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    torch.Tensor							)

             # Test not batched input
             lowercase__    :	List[str]     					=			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
             lowercase__    :	List[str]     					=			image_processing(a	,    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 	_UpperCAmelCase (     self							) ->      List[str]:
             # with apply_OCR = True
             lowercase__    :	List[str]     					=			LayoutLMvaImageProcessor()

             from datasets import load_dataset

             lowercase__    :	Optional[Any]     					=			load_dataset('hf-internal-testing/fixtures_docvqa'	,    split='test'							)

             lowercase__    :	List[Any]     					=			Image.open(ds[0]['file']							).convert('RGB'							)

             lowercase__    :	Dict     					=			image_processing(a	,    return_tensors='pt'							)

             self.assertEqual(encoding.pixel_values.shape	,    (1, 3, 2_2_4, 2_2_4)							)
             self.assertEqual(len(encoding.words							)	,    len(encoding.boxes							)							)

             # fmt: off
             # the words and boxes were obtained with Tesseract 4.1.1
             lowercase__    :	str     					=			[['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
             lowercase__    :	Any     					=			[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
             # fmt: on

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

             # with apply_OCR = False
             lowercase__    :	List[str]     					=			LayoutLMvaImageProcessor(apply_ocr=a							)

             lowercase__    :	Dict     					=			image_processing(a	,    return_tensors='pt'							)

             self.assertEqual(encoding.pixel_values.shape	,    (1, 3, 2_2_4, 2_2_4)							)


"""simple docstring"""





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MaskFormerSwinBackbone
       from transformers.models.maskformer import MaskFormerSwinModel


class   UpperCAmelCase_			:


      def __init__(     self	,    a	,    a=1_3	,    a=3_2	,    a=2	,    a=3	,    a=1_6	,    a=[1, 2, 1]	,    a=[2, 2, 4]	,    a=2	,    a=2.0	,    a=True	,    a=0.0	,    a=0.0	,    a=0.1	,    a="gelu"	,    a=False	,    a=True	,    a=0.02	,    a=1e-5	,    a=True	,    a=None	,    a=True	,    a=1_0	,    a=8	,    a=["stage1", "stage2", "stage3"]	,    a=[1, 2, 3]	,    ) ->      int:
             lowercase__    :	int     					=			parent
             lowercase__    :	Union[str, Any]     					=			batch_size
             lowercase__    :	Dict     					=			image_size
             lowercase__    :	str     					=			patch_size
             lowercase__    :	Optional[Any]     					=			num_channels
             lowercase__    :	List[str]     					=			embed_dim
             lowercase__    :	Any     					=			depths
             lowercase__    :	Dict     					=			num_heads
             lowercase__    :	List[str]     					=			window_size
             lowercase__    :	int     					=			mlp_ratio
             lowercase__    :	Tuple     					=			qkv_bias
             lowercase__    :	Union[str, Any]     					=			hidden_dropout_prob
             lowercase__    :	str     					=			attention_probs_dropout_prob
             lowercase__    :	Tuple     					=			drop_path_rate
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Optional[Any]     					=			use_absolute_embeddings
             lowercase__    :	Optional[Any]     					=			patch_norm
             lowercase__    :	Any     					=			layer_norm_eps
             lowercase__    :	List[Any]     					=			initializer_range
             lowercase__    :	List[str]     					=			is_training
             lowercase__    :	int     					=			scope
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	List[Any]     					=			type_sequence_label_size
             lowercase__    :	List[str]     					=			encoder_stride
             lowercase__    :	Optional[Any]     					=			out_features
             lowercase__    :	Dict     					=			out_indices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Any     					=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__    :	Optional[Any]     					=			None
             if self.use_labels:
                    lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size]	,    self.type_sequence_label_size							)

             lowercase__    :	Tuple     					=			self.get_config()

             return config, pixel_values, labels


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             return MaskFormerSwinConfig(
                 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	,    out_features=self.out_features	,    out_indices=self.out_indices	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Tuple     					=			MaskFormerSwinModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	str     					=			model(a							)

             lowercase__    :	str     					=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
             lowercase__    :	Dict     					=			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 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	List[Any]     					=			MaskFormerSwinBackbone(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	int     					=			model(a							)

             # verify feature maps
             self.parent.assertEqual(len(result.feature_maps							)	,    len(config.out_features							)							)
             self.parent.assertListEqual(list(result.feature_maps[0].shape							)	,    [1_3, 1_6, 1_6, 1_6]							)

             # verify channels
             self.parent.assertEqual(len(model.channels							)	,    len(config.out_features							)							)
             self.parent.assertListEqual(model.channels	,    [1_6, 3_2, 6_4]							)

             # verify ValueError
             with self.parent.assertRaises(a							):
                    lowercase__    :	Dict     					=			['stem']
                    lowercase__    :	List[str]     					=			MaskFormerSwinBackbone(config=a							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	int     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[int]										=						(
          (
              MaskFormerSwinModel,
              MaskFormerSwinBackbone,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase__	:					List[str]										=						{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
      lowerCamelCase__	:					str										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					Any										=						False
      lowerCamelCase__	:					Dict										=						False
      lowerCamelCase__	:					int										=						False


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	str     					=			MaskFormerSwinModelTester(self							)
             lowercase__    :	Tuple     					=			ConfigTester(self	,    config_class=a	,    embed_dim=3_7							)


      @require_torch_multi_gpu
      @unittest.skip(
          reason=(
              '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
              ' `nn.DataParallel`'
          )							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             self.create_and_test_config_common_properties()
             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 	_UpperCAmelCase (     self							) ->      str:
             return


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_backbone(*a							)


      @unittest.skip('Swin does not use inputs_embeds'							)
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      @unittest.skip('Swin does not support feedforward chunking'							)
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__    :	List[str]     					=			model_class(a							)
                    self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)							)
                    lowercase__    :	Union[str, Any]     					=			model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(a	,    nn.Linear							)							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__    :	List[Any]     					=			['pixel_values']
                    self.assertListEqual(arg_names[:1]	,    a							)


      @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'							)
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             pass


      @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'							)
      def 	_UpperCAmelCase (     self							) ->      int:
             pass


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a							) ->      Tuple:
             lowercase__    :	Dict     					=			model_class(a							)
             model.to(a							)
             model.eval()

             with torch.no_grad():
                    lowercase__    :	str     					=			model(**self._prepare_for_class(a	,    a							)							)

             lowercase__    :	List[Any]     					=			outputs.hidden_states

             lowercase__    :	str     					=			getattr(
                 self.model_tester	,    'expected_num_hidden_layers'	,    len(self.model_tester.depths							) + 1							)
             self.assertEqual(len(a							)	,    a							)

             # Swin has a different seq_length
             lowercase__    :	Dict     					=			(
                 config.patch_size
                 if isinstance(config.patch_size	,    collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__    :	Tuple     					=			(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]	,    )


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__    :	List[Any]     					=			(
                 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:
                    lowercase__    :	List[str]     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	List[str]     					=			True

                    self.check_hidden_states_output(a	,    a	,    a	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	Union[str, Any]     					=			3

             lowercase__    :	str     					=			(
                 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)
             )
             lowercase__    :	Tuple     					=			(
                 config.patch_size
                 if isinstance(config.patch_size	,    collections.abc.Iterable							)
                 else (config.patch_size, config.patch_size)
             )

             lowercase__    :	Optional[int]     					=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
             lowercase__    :	List[str]     					=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

             for model_class in self.all_model_classes:
                    lowercase__    :	List[str]     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    (padded_height, padded_width)							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__    :	int     					=			True
                    self.check_hidden_states_output(a	,    a	,    a	,    (padded_height, padded_width)							)


      @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             pass


      @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             pass


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs_for_common()

             def set_nan_tensor_to_zero(a							):
                    lowercase__    :	Union[str, Any]     					=			0
                    return t

             def check_equivalence(a	,    a	,    a	,    a={}							):
                    with torch.no_grad():
                           lowercase__    :	Optional[Any]     					=			model(**a	,    return_dict=a	,    **a							)
                           lowercase__    :	Optional[int]     					=			model(**a	,    return_dict=a	,    **a							).to_tuple()

                           def recursive_check(a	,    a							):
                                  if isinstance(a	,    (List, Tuple)							):
                                         for tuple_iterable_value, dict_iterable_value in zip(a	,    a							):
                                                recursive_check(a	,    a							)
                                  elif isinstance(a	,    a							):
                                         for tuple_iterable_value, dict_iterable_value in zip(
                                             tuple_object.values()	,    dict_object.values()							):
                                                recursive_check(a	,    a							)
                                  elif tuple_object is None:
                                         return
                                  else:
                                         self.assertTrue(
                                             torch.allclose(
                                                 set_nan_tensor_to_zero(a							)	,    set_nan_tensor_to_zero(a							)	,    atol=1e-5							)	,    msg=(
                                                 'Tuple and dict output are not equal. Difference:'
                                                 f""" {torch.max(torch.abs(tuple_object - dict_object							)							)}. Tuple has `nan`:"""
                                                 f""" {torch.isnan(a							).any()} and `inf`: {torch.isinf(a							)}. Dict has"""
                                                 f""" `nan`: {torch.isnan(a							).any()} and `inf`: {torch.isinf(a							)}."""
                                             )	,    )

                           recursive_check(a	,    a							)

             for model_class in self.all_model_classes:
                    lowercase__    :	Any     					=			model_class(a							)
                    model.to(a							)
                    model.eval()

                    lowercase__    :	Tuple     					=			self._prepare_for_class(a	,    a							)
                    lowercase__    :	Optional[Any]     					=			self._prepare_for_class(a	,    a							)
                    check_equivalence(a	,    a	,    a							)

                    lowercase__    :	Any     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    lowercase__    :	List[Any]     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    check_equivalence(a	,    a	,    a							)

                    lowercase__    :	Any     					=			self._prepare_for_class(a	,    a							)
                    lowercase__    :	int     					=			self._prepare_for_class(a	,    a							)
                    check_equivalence(a	,    a	,    a	,    {'output_hidden_states': True}							)

                    lowercase__    :	Dict     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    lowercase__    :	Optional[int]     					=			self._prepare_for_class(a	,    a	,    return_labels=a							)
                    check_equivalence(a	,    a	,    a	,    {'output_hidden_states': True}							)





@require_torch
class   UpperCAmelCase_			(					unittest.TestCase				,							_a):
      lowerCamelCase__	:					Dict										=						(MaskFormerSwinBackbone,) if is_torch_available() else ()
      lowerCamelCase__	:					Optional[int]										=						MaskFormerSwinConfig


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Optional[int]     					=			MaskFormerSwinModelTester(self							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__					,   lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs_for_common()
             lowercase__    :	int     					=			inputs_dict['pixel_values'].shape[0]

             for backbone_class in self.all_model_classes:
                    lowercase__    :	Optional[Any]     					=			backbone_class(a							)
                    backbone.to(a							)
                    backbone.eval()

                    lowercase__    :	Union[str, Any]     					=			backbone(**a							)

                    # Test default outputs and verify feature maps
                    self.assertIsInstance(outputs.feature_maps	,    a							)
                    self.assertTrue(len(outputs.feature_maps							) == len(backbone.channels							)							)
                    for feature_map, n_channels in zip(outputs.feature_maps	,    backbone.channels							):
                           self.assertTrue(feature_map.shape[:2]	,    (batch_size, n_channels)							)
                    self.assertIsNone(outputs.hidden_states							)
                    self.assertIsNone(outputs.attentions							)

                    # Test output_hidden_states=True
                    lowercase__    :	List[str]     					=			backbone(**a	,    output_hidden_states=a							)
                    self.assertIsNotNone(outputs.hidden_states							)
                    self.assertTrue(len(outputs.hidden_states							)	,    len(backbone.stage_names							)							)
                    # We skip the stem layer
                    for hidden_states, n_channels in zip(outputs.hidden_states[1:]	,    backbone.channels							):
                           for hidden_state in hidden_states:
                                  # Hidden states are in the format (batch_size, (height * width), n_channels)
                                  lowercase__					,   lowercase__					,   lowercase__    :	int     					=			hidden_state.shape
                                  self.assertTrue((h_batch_size, h_n_channels)	,    (batch_size, n_channels)							)

            # Test output_attentions=True
                    if self.has_attentions:
                           lowercase__    :	List[Any]     					=			backbone(**a	,    output_attentions=a							)
                           self.assertIsNotNone(outputs.attentions							)

