"""simple docstring"""







import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Dict=False      ):
     lowercase_	:				List[Any]													=	[]
     for i in range(config.num_hidden_layers      ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')      )
          rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')      )
          rename_keys.append(
              (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')      )
          rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')      )
          rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')      )
          rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')      )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')      )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')      )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')      )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')      )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('module.cls_token', 'vit.embeddings.cls_token'),
             ('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
             ('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
             ('module.pos_embed', 'vit.embeddings.position_embeddings'),
         ]      )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('module.norm.weight', 'layernorm.weight'),
                  ('module.norm.bias', 'layernorm.bias'),
              ]      )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          lowercase_	:				str													=	[(pair[0], pair[1][4:]) if pair[1].startswith('vit'      ) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]      )

     return rename_keys



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]=False      ):
     for i in range(config.num_hidden_layers      ):
          if base_model:
               lowercase_	:				Any													=	''
          else:
               lowercase_	:				Tuple													=	'vit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          lowercase_	:				Tuple													=	state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight'''      )
          lowercase_	:				Dict													=	state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias'''      )
          # next, add query, keys and values (in that order) to the state dict
          lowercase_	:				Optional[int]													=	in_proj_weight[
              : config.hidden_size, :
          ]
          lowercase_	:				Any													=	in_proj_bias[: config.hidden_size]
          lowercase_	:				Union[str, Any]													=	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          lowercase_	:				Optional[Any]													=	in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          lowercase_	:				str													=	in_proj_weight[
              -config.hidden_size :, :
          ]
          lowercase_	:				List[str]													=	in_proj_bias[-config.hidden_size :]



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict      ):
     lowercase_	:				Any													=	['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     # projection head is used in the self-supervised pre-training in MSN,
     # for downstream task it's not needed.
     lowercase_	:				Optional[int]													=	[
         'module.fc.fc1.weight',
         'module.fc.fc1.bias',
         'module.fc.bn1.weight',
         'module.fc.bn1.bias',
         'module.fc.bn1.running_mean',
         'module.fc.bn1.running_var',
         'module.fc.bn1.num_batches_tracked',
         'module.fc.fc2.weight',
         'module.fc.fc2.bias',
         'module.fc.bn2.weight',
         'module.fc.bn2.bias',
         'module.fc.bn2.running_mean',
         'module.fc.bn2.running_var',
         'module.fc.bn2.num_batches_tracked',
         'module.fc.fc3.weight',
         'module.fc.fc3.bias',
     ]
     for k in ignore_keys:
          state_dict.pop(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     Tuple      ):
     lowercase_	:				Union[str, Any]													=	dct.pop(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[str]													=	val



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_	:				Any													=	ViTMSNConfig()
     lowercase_	:				Optional[int]													=	10_00

     lowercase_	:				str													=	'datasets/huggingface/label-files'
     lowercase_	:				str													=	'imagenet-1k-id2label.json'
     lowercase_	:				Optional[int]													=	json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )     ,			'r'      )      )
     lowercase_	:				Tuple													=	{int(__SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
     lowercase_	:				Any													=	idalabel
     lowercase_	:				Dict													=	{v: k for k, v in idalabel.items()}

     if "s16" in checkpoint_url:
          lowercase_	:				Optional[Any]													=	3_84
          lowercase_	:				int													=	15_36
          lowercase_	:				int													=	6
     elif "l16" in checkpoint_url:
          lowercase_	:				List[Any]													=	10_24
          lowercase_	:				Dict													=	40_96
          lowercase_	:				List[str]													=	24
          lowercase_	:				Any													=	16
          lowercase_	:				Optional[int]													=	0.1
     elif "b4" in checkpoint_url:
          lowercase_	:				List[Any]													=	4
     elif "l7" in checkpoint_url:
          lowercase_	:				Any													=	7
          lowercase_	:				int													=	10_24
          lowercase_	:				Optional[Any]													=	40_96
          lowercase_	:				Optional[int]													=	24
          lowercase_	:				List[Any]													=	16
          lowercase_	:				List[Any]													=	0.1

     lowercase_	:				Union[str, Any]													=	ViTMSNModel(__SCREAMING_SNAKE_CASE      )

     lowercase_	:				int													=	torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )['target_encoder']

     lowercase_	:				List[str]													=	ViTImageProcessor(size=config.image_size      )

     remove_projection_head(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				str													=	create_rename_keys(__SCREAMING_SNAKE_CASE     ,			base_model=__SCREAMING_SNAKE_CASE      )

     for src, dest in rename_keys:
          rename_key(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
     read_in_q_k_v(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			base_model=__SCREAMING_SNAKE_CASE      )

     model.load_state_dict(__SCREAMING_SNAKE_CASE      )
     model.eval()

     lowercase_	:				Union[str, Any]													=	'http://images.cocodataset.org/val2017/000000039769.jpg'

     lowercase_	:				Dict													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,			stream=__SCREAMING_SNAKE_CASE      ).raw      )
     lowercase_	:				List[Any]													=	ViTImageProcessor(
         size=config.image_size     ,			image_mean=__SCREAMING_SNAKE_CASE     ,			image_std=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[str]													=	image_processor(images=__SCREAMING_SNAKE_CASE     ,			return_tensors='pt'      )

     # forward pass
     torch.manual_seed(2      )
     lowercase_	:				int													=	model(**__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	outputs.last_hidden_state

     # The following Colab Notebook was used to generate these outputs:
     # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
     if "s16" in checkpoint_url:
          lowercase_	:				int													=	torch.tensor([[-1.0915, -1.4876, -1.1809]]      )
     elif "b16" in checkpoint_url:
          lowercase_	:				Tuple													=	torch.tensor([[14.2889, -18.9045, 11.7281]]      )
     elif "l16" in checkpoint_url:
          lowercase_	:				List[Any]													=	torch.tensor([[41.5028, -22.8681, 45.6475]]      )
     elif "b4" in checkpoint_url:
          lowercase_	:				Union[str, Any]													=	torch.tensor([[-4.3868, 5.2932, -0.4137]]      )
     else:
          lowercase_	:				List[Any]													=	torch.tensor([[-0.1792, -0.6465, 2.4263]]      )

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

     print(F'''Saving model to {pytorch_dump_folder_path}'''      )
     model.save_pretrained(__SCREAMING_SNAKE_CASE      )

     print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
     image_processor.save_pretrained(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--checkpoint_url",
           default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
           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."
       )

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


"""simple docstring"""







import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
       from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=30     ,__UpperCamelCase=2     ,__UpperCamelCase=3     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=32     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=10     ,__UpperCamelCase=0.02     ,__UpperCamelCase=None     ,__UpperCamelCase=2     ,)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	parent
        lowercase_	:				List[Any]													=	batch_size
        lowercase_	:				Optional[int]													=	image_size
        lowercase_	:				List[str]													=	patch_size
        lowercase_	:				int													=	num_channels
        lowercase_	:				List[str]													=	is_training
        lowercase_	:				Union[str, Any]													=	use_labels
        lowercase_	:				str													=	hidden_size
        lowercase_	:				List[str]													=	num_hidden_layers
        lowercase_	:				List[str]													=	num_attention_heads
        lowercase_	:				Dict													=	intermediate_size
        lowercase_	:				Optional[int]													=	hidden_act
        lowercase_	:				Any													=	hidden_dropout_prob
        lowercase_	:				Any													=	attention_probs_dropout_prob
        lowercase_	:				List[str]													=	type_sequence_label_size
        lowercase_	:				Union[str, Any]													=	initializer_range
        lowercase_	:				int													=	scope
        lowercase_	:				Dict													=	encoder_stride

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase_	:				Any													=	(image_size // patch_size) ** 2
        lowercase_	:				Optional[int]													=	num_patches + 1
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				Tuple													=	None
        if self.use_labels:
             lowercase_	:				Dict													=	ids_tensor([self.batch_size]     ,self.type_sequence_label_size			)

        lowercase_	:				Any													=	self.get_config()

        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        return ViTConfig(
            image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,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     ,is_decoder=__UpperCamelCase     ,initializer_range=self.initializer_range     ,encoder_stride=self.encoder_stride     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	ViTModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTForMaskedImageModeling(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				List[str]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(
            result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase_	:				Any													=	1
        lowercase_	:				List[Any]													=	ViTForMaskedImageModeling(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				str													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.reconstruction.shape     ,(self.batch_size, 1, self.image_size, self.image_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.type_sequence_label_size
        lowercase_	:				Dict													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				int													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase_	:				Dict													=	1
        lowercase_	:				Any													=	ViTForImageClassification(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        lowercase_	:				Dict													=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase_	:				Dict													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)			)







   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  
        )	:				Optional[Any]													=	config_and_inputs
        lowercase_	:				str													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (
       (
           ViTModel,
           ViTForImageClassification,
           ViTForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   lowercase							=  (
       {'feature-extraction': ViTModel, 'image-classification': ViTForImageClassification}
       if is_torch_available()
       else {}
   )
   lowercase							=  True

   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModelTester(self			)
        lowercase_	:				Dict													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,has_text_modality=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   @unittest.skip(reason='ViT does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				str													=	model_class(__UpperCamelCase			)
             self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)			)
             lowercase_	:				List[Any]													=	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCamelCase     ,nn.Linear			)			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase_	:				Tuple													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_	:				List[Any]													=	ViTModel.from_pretrained(__UpperCamelCase			)
             self.assertIsNotNone(__UpperCamelCase			)






def 			lowercase__(       ):
     lowercase_	:				Any													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
@require_vision
class        UpperCamelCase		(			unittest.TestCase  ):
   @cached_property
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'			) if is_vision_available() else None
   @slow
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'			).to(__UpperCamelCase			)

        lowercase_	:				Any													=	self.default_image_processor
        lowercase_	:				Dict													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			).to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				Any													=	model(**__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Optional[Any]													=	torch.Size((1, 1000)			)
        self.assertEqual(outputs.logits.shape     ,__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	torch.tensor([-0.2744, 0.8215, -0.0836]			).to(__UpperCamelCase			)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,__UpperCamelCase     ,atol=1e-4			)			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				str													=	ViTModel.from_pretrained('facebook/dino-vits8'			).to(__UpperCamelCase			)

        lowercase_	:				int													=	ViTImageProcessor.from_pretrained('facebook/dino-vits8'     ,size=480			)
        lowercase_	:				int													=	prepare_img()
        lowercase_	:				Dict													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				int													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass
        with torch.no_grad():
             lowercase_	:				int													=	model(__UpperCamelCase     ,interpolate_pos_encoding=__UpperCamelCase			)

        # verify the logits
        lowercase_	:				Any													=	torch.Size((1, 3601, 384)			)
        self.assertEqual(outputs.last_hidden_state.shape     ,__UpperCamelCase			)

        lowercase_	:				Any													=	torch.tensor(
            [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]			).to(__UpperCamelCase			)

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







   @slow
   @require_accelerate
   @require_torch_gpu
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	ViTModel.from_pretrained('facebook/dino-vits8'     ,torch_dtype=torch.floataa     ,device_map='auto'			)
        lowercase_	:				int													=	self.default_image_processor

        lowercase_	:				Optional[int]													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				Union[str, Any]													=	inputs.pixel_values.to(__UpperCamelCase			)

        # forward pass to make sure inference works in fp16
        with torch.no_grad():
             lowercase_	:				Dict													=	model(__UpperCamelCase			)

