import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE :	List[Any]							     =		get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			XGLMTokenizer
  _SCREAMING_SNAKE_CASE    				=			XGLMTokenizerFast
  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			True

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __a       =  XGLMTokenizer(__SCREAMING_SNAKE_CASE     ,							keep_accents=__SCREAMING_SNAKE_CASE       )
   tokenizer.save_pretrained(self.tmpdirname       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  "<pad>"
   __a       =  1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  list(self.get_tokenizer().get_vocab().keys()       )

   self.assertEqual(vocab_keys[0]     ,							"<s>"       )
   self.assertEqual(vocab_keys[1]     ,							"<pad>"       )
   self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							10_08       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   self.assertEqual(self.get_tokenizer().vocab_size     ,							10_08       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  XGLMTokenizer(__SCREAMING_SNAKE_CASE     ,							keep_accents=__SCREAMING_SNAKE_CASE       )

   __a       =  tokenizer.tokenize("This is a test"       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							["▁This", "▁is", "▁a", "▁t", "est"]       )

   self.assertListEqual(
       tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )     ,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,							)

   __a       =  tokenizer.tokenize("I was born in 92000, and this is falsé."       )
   self.assertListEqual(
       __SCREAMING_SNAKE_CASE     ,							[
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "9",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "é",
           ".",
       ]     ,							)
   __a       =  tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(
       __SCREAMING_SNAKE_CASE     ,							[
           value + tokenizer.fairseq_offset
           for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
       ]     ,							)

   __a       =  tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(
       __SCREAMING_SNAKE_CASE     ,							[
           SPIECE_UNDERLINE + "I",
           SPIECE_UNDERLINE + "was",
           SPIECE_UNDERLINE + "b",
           "or",
           "n",
           SPIECE_UNDERLINE + "in",
           SPIECE_UNDERLINE + "",
           "<unk>",
           "2",
           "0",
           "0",
           "0",
           ",",
           SPIECE_UNDERLINE + "and",
           SPIECE_UNDERLINE + "this",
           SPIECE_UNDERLINE + "is",
           SPIECE_UNDERLINE + "f",
           "al",
           "s",
           "<unk>",
           ".",
       ]     ,							)

  @cached_property
  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   return XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   with tempfile.NamedTemporaryFile() as f:
    shutil.copyfile(__SCREAMING_SNAKE_CASE     ,							f.name       )
    __a       =  XGLMTokenizer(f.name     ,							keep_accents=__SCREAMING_SNAKE_CASE       )
    __a       =  pickle.dumps(__SCREAMING_SNAKE_CASE       )
   pickle.loads(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   if not self.test_rust_tokenizer:
    return

   __a       =  self.get_tokenizer()
   __a       =  self.get_rust_tokenizer()

   __a       =  "I was born in 92000, and this is falsé."

   __a       =  tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )
   __a       =  rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  tokenizer.encode(__SCREAMING_SNAKE_CASE     ,							add_special_tokens=__SCREAMING_SNAKE_CASE       )
   __a       =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,							add_special_tokens=__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  self.get_rust_tokenizer()
   __a       =  tokenizer.encode(__SCREAMING_SNAKE_CASE       )
   __a       =  rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  "Hello World!"
   __a       =  [2, 3_12_27, 44_47, 35]

   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  (
       "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
       " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
   )
   # fmt: off
   __a       =  [2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
   # fmt: on

   self.assertListEqual(__SCREAMING_SNAKE_CASE     ,							self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   # fmt: off
   __a       =  {
       "input_ids": [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
       "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
   }  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=__SCREAMING_SNAKE_CASE     ,							model_name="facebook/xglm-564M"     ,							padding=__SCREAMING_SNAKE_CASE     ,							)



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    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]=13     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=32     ,							__SCREAMING_SNAKE_CASE :      str=5     ,							__SCREAMING_SNAKE_CASE :      List[str]=4     ,							__SCREAMING_SNAKE_CASE :      List[Any]=37     ,							__SCREAMING_SNAKE_CASE :      int="gelu"     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      List[Any]=10     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      str=2     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  image_size
   __a       =  patch_size
   __a       =  num_channels
   __a       =  is_training
   __a       =  use_labels
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_size
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  type_sequence_label_size
   __a       =  initializer_range
   __a       =  scope
   __a       =  encoder_stride

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

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

   __a       =  None
   if self.use_labels:
    __a       =  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size       )

   __a       =  self.get_config()

   return config, pixel_values, labels

  def 		_UpperCAmelCase   (		self :      str       ):
   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=__SCREAMING_SNAKE_CASE     ,							initializer_range=self.initializer_range     ,							encoder_stride=self.encoder_stride     ,							)

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  ViTModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  ViTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.reconstruction.shape     ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

   # test greyscale images
   __a       =  1
   __a       =  ViTForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.reconstruction.shape     ,							(self.batch_size, 1, self.image_size, self.image_size)       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  self.type_sequence_label_size
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)       )

   # test greyscale images
   __a       =  1
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.prepare_config_and_inputs()
   (
       (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     
   )       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       a_			,       a_			,       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 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  ViTModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE     ,							has_text_modality=__SCREAMING_SNAKE_CASE     ,							hidden_size=37       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="ViT does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(model.get_input_embeddings()     ,							(nn.Module)       )
    __a       =  model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE     ,							nn.Linear       )       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

    __a       =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  ViTModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


 __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image






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

  @cached_property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"       ) if is_vision_available() else None

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  self.default_image_processor
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(**__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 10_00)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor([-0.27_44, 0.82_15, -0.08_36]       ).to(__SCREAMING_SNAKE_CASE       )

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

  @slow
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   # ViT models have an `interpolate_pos_encoding` argument in their forward method,
   # allowing to interpolate the pre-trained position embeddings in order to use
   # the model on higher resolutions. The DINO model by Facebook AI leverages this
   # to visualize self-attention on higher resolution images.
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  ViTImageProcessor.from_pretrained("facebook/dino-vits8"     ,							size=4_80       )
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE     ,							interpolate_pos_encoding=__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 36_01, 3_84)       )
   self.assertEqual(outputs.last_hidden_state.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]       ).to(__SCREAMING_SNAKE_CASE       )

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

  @slow
  @require_accelerate
  @require_torch_gpu
  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"     ,							torch_dtype=torch.floataa     ,							device_map="auto"       )
   __a       =  self.default_image_processor

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass to make sure inference works in fp16
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )


