





'''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     :					List[str]     ,     __snake_case     :					str     ,     __snake_case     :					Optional[int]=7     ,     __snake_case     :					List[str]=3     ,     __snake_case     :					Optional[Any]=18     ,     __snake_case     :					Any=30     ,     __snake_case     :					Dict=4_00     ,     __snake_case     :					Any=True     ,     __snake_case     :					Dict=None     ,     __snake_case     :					Optional[int]=True     ,     )							-> Tuple:
            _lowerCAmelCase					=				size if size is not None else {"""height""": 18, """width""": 18}
            _lowerCAmelCase					=				parent
            _lowerCAmelCase					=				batch_size
            _lowerCAmelCase					=				num_channels
            _lowerCAmelCase					=				image_size
            _lowerCAmelCase					=				min_resolution
            _lowerCAmelCase					=				max_resolution
            _lowerCAmelCase					=				do_resize
            _lowerCAmelCase					=				size
            _lowerCAmelCase					=				apply_ocr




      def 							lowercase__       (     self     :					Dict    )							-> Optional[Any]:
            return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}




@require_torch
@require_pytesseract
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[Any]			=  LayoutLMvaImageProcessor if is_pytesseract_available() else None




      def 							lowercase__       (     self     :					Tuple    )							-> Union[str, Any]:
            _lowerCAmelCase					=				LayoutLMvaImageProcessingTester(self    )




      @property
      def 							lowercase__       (     self     :					Dict    )							-> int:
            return self.image_processor_tester.prepare_image_processor_dict()




      def 							lowercase__       (     self     :					List[Any]    )							-> List[Any]:
            _lowerCAmelCase					=				self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(__snake_case     ,     """do_resize"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """size"""    )    )
            self.assertTrue(hasattr(__snake_case     ,     """apply_ocr"""    )    )




      def 							lowercase__       (     self     :					Dict    )							-> List[str]:
            _lowerCAmelCase					=				self.image_processing_class.from_dict(self.image_processor_dict    )
            self.assertEqual(image_processor.size     ,     {"""height""": 18, """width""": 18}    )

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




      def 							lowercase__       (     self     :					Dict    )							-> Any:
            pass




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

            # Test not batched input
            _lowerCAmelCase					=				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     ,     __snake_case    )
            self.assertIsInstance(encoding.boxes     ,     __snake_case    )

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

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

            # Test not batched input
            _lowerCAmelCase					=				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
            _lowerCAmelCase					=				image_processing(__snake_case     ,     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 							lowercase__       (     self     :					Optional[int]    )							-> Dict:
            # with apply_OCR = True
            _lowerCAmelCase					=				LayoutLMvaImageProcessor()

            from datasets import load_dataset

            _lowerCAmelCase					=				load_dataset("""hf-internal-testing/fixtures_docvqa"""     ,     split="""test"""    )

            _lowerCAmelCase					=				Image.open(ds[0]["""file"""]    ).convert("""RGB"""    )

            _lowerCAmelCase					=				image_processing(__snake_case     ,     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
            _lowerCAmelCase					=				[["""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
            _lowerCAmelCase					=				[[[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     ,     __snake_case    )
            self.assertListEqual(encoding.boxes     ,     __snake_case    )

            # with apply_OCR = False
            _lowerCAmelCase					=				LayoutLMvaImageProcessor(apply_ocr=__snake_case    )

            _lowerCAmelCase					=				image_processing(__snake_case     ,     return_tensors="""pt"""    )

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













'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if "cls_token" in name:
            _lowerCAmelCase					=				name.replace("""cls_token"""				,      """vit.embeddings.cls_token"""    )
      if "mask_token" in name:
            _lowerCAmelCase					=				name.replace("""mask_token"""				,      """decoder.mask_token"""    )
      if "decoder_pos_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pos_embed"""				,      """decoder.decoder_pos_embed"""    )
      if "pos_embed" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""pos_embed"""				,      """vit.embeddings.position_embeddings"""    )
      if "patch_embed.proj" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.proj"""				,      """vit.embeddings.patch_embeddings.projection"""    )
      if "patch_embed.norm" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.norm"""				,      """vit.embeddings.norm"""    )
      if "decoder_blocks" in name:
            _lowerCAmelCase					=				name.replace("""decoder_blocks"""				,      """decoder.decoder_layers"""    )
      if "blocks" in name:
            _lowerCAmelCase					=				name.replace("""blocks"""				,      """vit.encoder.layer"""    )
      if "attn.proj" in name:
            _lowerCAmelCase					=				name.replace("""attn.proj"""				,      """attention.output.dense"""    )
      if "attn" in name:
            _lowerCAmelCase					=				name.replace("""attn"""				,      """attention.self"""    )
      if "norm1" in name:
            _lowerCAmelCase					=				name.replace("""norm1"""				,      """layernorm_before"""    )
      if "norm2" in name:
            _lowerCAmelCase					=				name.replace("""norm2"""				,      """layernorm_after"""    )
      if "mlp.fc1" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc1"""				,      """intermediate.dense"""    )
      if "mlp.fc2" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc2"""				,      """output.dense"""    )
      if "decoder_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_embed"""				,      """decoder.decoder_embed"""    )
      if "decoder_norm" in name:
            _lowerCAmelCase					=				name.replace("""decoder_norm"""				,      """decoder.decoder_norm"""    )
      if "decoder_pred" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pred"""				,      """decoder.decoder_pred"""    )
      if "norm.weight" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""norm.weight"""				,      """vit.layernorm.weight"""    )
      if "norm.bias" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""norm.bias"""				,      """vit.layernorm.bias"""    )

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase					=				orig_state_dict.pop(lowerCAmelCase    )

            if "qkv" in key:
                  _lowerCAmelCase					=				key.split("""."""    )
                  _lowerCAmelCase					=				int(key_split[1]    )
                  if "decoder_blocks" in key:
                        _lowerCAmelCase					=				config.decoder_hidden_size
                        _lowerCAmelCase					=				"""decoder.decoder_layers."""
                        if "weight" in key:
                              _lowerCAmelCase					=				val[:dim, :]
                              _lowerCAmelCase					=				val[dim : dim * 2, :]
                              _lowerCAmelCase					=				val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase					=				val[:dim]
                              _lowerCAmelCase					=				val[dim : dim * 2]
                              _lowerCAmelCase					=				val[-dim:]
                  else:
                        _lowerCAmelCase					=				config.hidden_size
                        _lowerCAmelCase					=				"""vit.encoder.layer."""
                        if "weight" in key:
                              _lowerCAmelCase					=				val[:dim, :]
                              _lowerCAmelCase					=				val[dim : dim * 2, :]
                              _lowerCAmelCase					=				val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase					=				val[:dim]
                              _lowerCAmelCase					=				val[dim : dim * 2]
                              _lowerCAmelCase					=				val[-dim:]

            else:
                  _lowerCAmelCase					=				val

      return orig_state_dict
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				ViTMAEConfig()
      if "large" in checkpoint_url:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
      elif "huge" in checkpoint_url:
            _lowerCAmelCase					=				14
            _lowerCAmelCase					=				12_80
            _lowerCAmelCase					=				51_20
            _lowerCAmelCase					=				32
            _lowerCAmelCase					=				16

      _lowerCAmelCase					=				ViTMAEForPreTraining(lowerCAmelCase    )

      _lowerCAmelCase					=				torch.hub.load_state_dict_from_url(lowerCAmelCase				,      map_location="""cpu"""    )["""model"""]

      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )

      _lowerCAmelCase					=				convert_state_dict(lowerCAmelCase				,      lowerCAmelCase    )

      model.load_state_dict(lowerCAmelCase    )
      model.eval()

      _lowerCAmelCase					=				"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )
      _lowerCAmelCase					=				image_processor(images=lowerCAmelCase				,      return_tensors="""pt"""    )

      # forward pass
      torch.manual_seed(2    )
      _lowerCAmelCase					=				model(**lowerCAmelCase    )
      _lowerCAmelCase					=				outputs.logits

      if "large" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor(
                [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]    )
      elif "huge" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor(
                [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]    )
      else:
            _lowerCAmelCase					=				torch.tensor(
                [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]    )

      # verify logits
      assert torch.allclose(logits[0, :3, :3]				,      lowerCAmelCase				,      atol=1e-4    )

      print(f"Saving model to {pytorch_dump_folder_path}"    )
      model.save_pretrained(lowerCAmelCase    )

      print(f"Saving image processor to {pytorch_dump_folder_path}"    )
      image_processor.save_pretrained(lowerCAmelCase    )


if __name__ == "__main__":
      A__							:  Dict    =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
          type=str,
          help='''URL of the checkpoint you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      A__							:  List[Any]    =parser.parse_args()
      convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






