





'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		tempfile.mkdtemp()

											# fmt: off
											_snake_case:   Union[str, Any]  					=		['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
											# fmt: on
											_snake_case:   str  					=		dict(zip(__snake_case       ,     range(len(__snake_case       )       )       )       )
											_snake_case:   str  					=		['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
											_snake_case:   Optional[int]  					=		{'unk_token': '<unk>'}

											_snake_case:   int  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['vocab_file']       )
											_snake_case:   Optional[int]  					=		os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	fp.write(json.dumps(__snake_case       ) + '\n'       )
											with open(self.merges_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	fp.write('\n'.join(__snake_case       )       )

											_snake_case:   Dict  					=		{
											    'do_resize': True,
											    'size': 20,
											    'do_center_crop': True,
											    'crop_size': 18,
											    'do_normalize': True,
											    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
											    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
											}
											_snake_case:   Union[str, Any]  					=		os.path.join(self.tmpdirname       ,     __snake_case       )
											with open(self.image_processor_file       ,     'w'       ,     encoding='utf-8'       ) as fp:
																	json.dump(__snake_case       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											return CLIPTokenizer.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





											return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											return CLIPImageProcessor.from_pretrained(self.tmpdirname       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											shutil.rmtree(self.tmpdirname       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		[np.random.randint(2_55       ,     size=(3, 30, 4_00)       ,     dtype=np.uinta       )]

											_snake_case:   Any  					=		[Image.fromarray(np.moveaxis(__snake_case       ,     0       ,     -1       )       ) for x in image_inputs]

											return image_inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_tokenizer()
											_snake_case:   List[str]  					=		self.get_rust_tokenizer()
											_snake_case:   List[str]  					=		self.get_image_processor()

											_snake_case:   Union[str, Any]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											_snake_case:   Any  					=		CLIPProcessor.from_pretrained(self.tmpdirname       ,     use_fast=__snake_case       )

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											_snake_case:   int  					=		CLIPProcessor.from_pretrained(self.tmpdirname       )

											self.assertEqual(processor_slow.tokenizer.get_vocab()       ,     tokenizer_slow.get_vocab()       )
											self.assertEqual(processor_fast.tokenizer.get_vocab()       ,     tokenizer_fast.get_vocab()       )
											self.assertEqual(tokenizer_slow.get_vocab()       ,     tokenizer_fast.get_vocab()       )
											self.assertIsInstance(processor_slow.tokenizer       ,     __snake_case       )
											self.assertIsInstance(processor_fast.tokenizer       ,     __snake_case       )

											self.assertEqual(processor_slow.image_processor.to_json_string()       ,     image_processor.to_json_string()       )
											self.assertEqual(processor_fast.image_processor.to_json_string()       ,     image_processor.to_json_string()       )
											self.assertIsInstance(processor_slow.image_processor       ,     __snake_case       )
											self.assertIsInstance(processor_fast.image_processor       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		CLIPProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											_snake_case:   List[Any]  					=		self.get_tokenizer(bos_token='(BOS)'       ,     eos_token='(EOS)'       )
											_snake_case:   List[Any]  					=		self.get_image_processor(do_normalize=__snake_case       ,     padding_value=1.0       )

											_snake_case:   Any  					=		CLIPProcessor.from_pretrained(
											    self.tmpdirname       ,     bos_token='(BOS)'       ,     eos_token='(EOS)'       ,     do_normalize=__snake_case       ,     padding_value=1.0       )

											self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()       )
											self.assertIsInstance(processor.tokenizer       ,     __snake_case       )

											self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()       )
											self.assertIsInstance(processor.image_processor       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   List[str]  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   List[str]  					=		self.prepare_image_inputs()

											_snake_case:   List[str]  					=		image_processor(__snake_case       ,     return_tensors='np'       )
											_snake_case:   Dict  					=		processor(images=__snake_case       ,     return_tensors='np'       )

											for key in input_image_proc.keys():
																	self.assertAlmostEqual(input_image_proc[key].sum()       ,     input_processor[key].sum()       ,     delta=1e-2       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   List[str]  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   int  					=		'lower newer'

											_snake_case:   str  					=		processor(text=__snake_case       )

											_snake_case:   List[Any]  					=		tokenizer(__snake_case       )

											for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.get_image_processor()
											_snake_case:   List[Any]  					=		self.get_tokenizer()

											_snake_case:   Tuple  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Optional[int]  					=		'lower newer'
											_snake_case:   Union[str, Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

											self.assertListEqual(list(inputs.keys()       )       ,     ['input_ids', 'attention_mask', 'pixel_values']       )

											# test if it raises when no input is passed
											with pytest.raises(__snake_case       ):
																	processor()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		self.get_image_processor()
											_snake_case:   Dict  					=		self.get_tokenizer()

											_snake_case:   str  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   str  					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											_snake_case:   List[Any]  					=		processor.batch_decode(__snake_case       )
											_snake_case:   Any  					=		tokenizer.batch_decode(__snake_case       )

											self.assertListEqual(__snake_case       ,     __snake_case       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.get_image_processor()
											_snake_case:   Any  					=		self.get_tokenizer()

											_snake_case:   Any  					=		CLIPProcessor(tokenizer=__snake_case       ,     image_processor=__snake_case       )

											_snake_case:   Any  					=		'lower newer'
											_snake_case:   Optional[Any]  					=		self.prepare_image_inputs()

											_snake_case:   int  					=		processor(text=__snake_case       ,     images=__snake_case       )

											self.assertListEqual(list(inputs.keys()       )       ,     processor.model_input_names       )











'''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 			lowerCamelCase					:


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  str=13       ,     __snake_case	:  Union[str, Any]=30       ,     __snake_case	:  Union[str, Any]=2       ,     __snake_case	:  Dict=3       ,     __snake_case	:  Optional[Any]=True       ,     __snake_case	:  Optional[int]=True       ,     __snake_case	:  Optional[int]=32       ,     __snake_case	:  Optional[int]=5       ,     __snake_case	:  Any=4       ,     __snake_case	:  int=37       ,     __snake_case	:  int="gelu"       ,     __snake_case	:  Union[str, Any]=0.1       ,     __snake_case	:  Optional[int]=0.1       ,     __snake_case	:  int=10       ,     __snake_case	:  Any=0.02       ,     __snake_case	:  List[str]=None       ,     __snake_case	:  Tuple=2       ,     ):





											'''simple docstring'''





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

											# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											_snake_case:   Tuple  					=		(image_size // patch_size) ** 2
											_snake_case:   List[str]  					=		num_patches + 1


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





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

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

											_snake_case:   Union[str, Any]  					=		self.get_config()

											return config, pixel_values, labels


					def    SCREAMING_SNAKE_CASE_		(					self	:  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=__snake_case       ,     initializer_range=self.initializer_range       ,     encoder_stride=self.encoder_stride       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  int       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		ViTModel(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Tuple  					=		model(__snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   int  					=		ViTForMaskedImageModeling(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Dict  					=		model(__snake_case       )
											self.parent.assertEqual(
											    result.reconstruction.shape       ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

											# test greyscale images
											_snake_case:   List[str]  					=		1
											_snake_case:   Tuple  					=		ViTForMaskedImageModeling(__snake_case       )
											model.to(__snake_case       )
											model.eval()

											_snake_case:   List[str]  					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											_snake_case:   Union[str, Any]  					=		model(__snake_case       )
											self.parent.assertEqual(result.reconstruction.shape       ,     (self.batch_size, 1, self.image_size, self.image_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.type_sequence_label_size
											_snake_case:   Union[str, Any]  					=		ViTForImageClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   List[Any]  					=		model(__snake_case       ,     labels=__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											_snake_case:   Tuple  					=		1
											_snake_case:   Optional[int]  					=		ViTForImageClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()

											_snake_case:   List[Any]  					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											_snake_case:   Any  					=		model(__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.prepare_config_and_inputs()
											(
											    (
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						
											):   int  					=		config_and_inputs
											_snake_case:   Dict  					=		{'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    (
					    (
					        ViTModel,
					        ViTForImageClassification,
					        ViTForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE							   =    (
					    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
					    if is_torch_available()
					    else {}
					)
					_SCREAMING_SNAKE_CASE							   =    True

					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		ViTModelTester(self       )
											_snake_case:   Union[str, Any]  					=		ConfigTester(self       ,     config_class=__snake_case       ,     has_text_modality=__snake_case       ,     hidden_size=37       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					@unittest.skip(reason='ViT does not use inputs_embeds'       )
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											pass


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   Dict  					=		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	_snake_case:   Optional[int]  					=		model_class(__snake_case       )
																	self.assertIsInstance(model.get_input_embeddings()       ,     (nn.Module)       )
																	_snake_case:   Optional[Any]  					=		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__snake_case       ,     nn.Linear       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   List[Any]  					=		self.model_tester.prepare_config_and_inputs_for_common()

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

																	_snake_case:   str  					=		['pixel_values']
																	self.assertListEqual(arg_names[:1]       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_snake_case:   Any  					=		ViTModel.from_pretrained(__snake_case       )
																	self.assertIsNotNone(__snake_case       )





def       lowercase_      (		)   ->List[Any]:
						_snake_case:   Dict  					=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						return image







@require_torch
@require_vision
class 			lowerCamelCase					(						unittest.TestCase  ):


					@cached_property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'       ) if is_vision_available() else None


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'       ).to(__snake_case       )

											_snake_case:   Dict  					=		self.default_image_processor
											_snake_case:   Optional[Any]  					=		prepare_img()
											_snake_case:   List[str]  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       ).to(__snake_case       )

											# forward pass
											with torch.no_grad():
																	_snake_case:   Optional[int]  					=		model(**__snake_case       )

											# verify the logits
											_snake_case:   Union[str, Any]  					=		torch.Size((1, 10_00)       )
											self.assertEqual(outputs.logits.shape       ,     __snake_case       )

											_snake_case:   Dict  					=		torch.tensor([-0.2_744, 0.8_215, -0.0_836]       ).to(__snake_case       )

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


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		ViTModel.from_pretrained('facebook/dino-vits8'       ).to(__snake_case       )

											_snake_case:   Any  					=		ViTImageProcessor.from_pretrained('facebook/dino-vits8'       ,     size=4_80       )
											_snake_case:   Optional[int]  					=		prepare_img()
											_snake_case:   Dict  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Optional[Any]  					=		inputs.pixel_values.to(__snake_case       )

											# forward pass
											with torch.no_grad():
																	_snake_case:   str  					=		model(__snake_case       ,     interpolate_pos_encoding=__snake_case       )

											# verify the logits
											_snake_case:   List[str]  					=		torch.Size((1, 36_01, 3_84)       )
											self.assertEqual(outputs.last_hidden_state.shape       ,     __snake_case       )

											_snake_case:   Any  					=		torch.tensor(
											    [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]       ).to(__snake_case       )

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




					@slow
					@require_accelerate
					@require_torch_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		ViTModel.from_pretrained('facebook/dino-vits8'       ,     torch_dtype=torch.floataa       ,     device_map='auto'       )
											_snake_case:   Dict  					=		self.default_image_processor

											_snake_case:   Any  					=		prepare_img()
											_snake_case:   str  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Any  					=		inputs.pixel_values.to(__snake_case       )

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
																	_snake_case:   int  					=		model(__snake_case       )





