




import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_lowerCAmelCase  :      Union[str, Any]						=      logging.get_logger(__name__)
class 	_UpperCamelCase					( enum.Enum						):
      UpperCAmelCase_						=						0
      UpperCAmelCase_						=						1



@add_end_docstrings(lowercase_						)
class 	_UpperCamelCase					( lowercase_						):
      UpperCAmelCase_						=						"""generated"""
      def __init__(	self      :List[Any]       ,					*lowerCamelCase      :List[Any]       ,					**lowerCamelCase      :int					)   ->	Tuple:
       super().__init__(*__UpperCamelCase       ,					**__UpperCamelCase					)

       self.check_model_type(
           TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
           if self.framework == "tf"
           else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING					)
      def 		UpperCAmelCase_  (	self      :Dict       ,					lowerCamelCase      :Dict=None       ,					lowerCamelCase      :int=None       ,					lowerCamelCase      :Tuple=None       ,					lowerCamelCase      :Optional[Any]=None       ,					lowerCamelCase      :List[str]=None       ,					lowerCamelCase      :List[Any]=None       ,					**lowerCamelCase      :Optional[Any]       ,					)   ->	Optional[Any]:
       UpperCAmelCase__							       =  {}
       if truncation is not None:
        UpperCAmelCase__							       =  truncation

       UpperCAmelCase__							       =  generate_kwargs

       UpperCAmelCase__							       =  {}
       if return_tensors is not None and return_type is None:
        UpperCAmelCase__							       =  ReturnType.TENSORS if return_tensors else ReturnType.TEXT
       if return_type is not None:
        UpperCAmelCase__							       =  return_type

       if clean_up_tokenization_spaces is not None:
        UpperCAmelCase__							       =  clean_up_tokenization_spaces

       if stop_sequence is not None:
        UpperCAmelCase__							       =  self.tokenizer.encode(__UpperCamelCase       ,					add_special_tokens=__UpperCamelCase					)
        if len(__UpperCamelCase					) > 1:
         warnings.warn(
             "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
             " the stop sequence will be used as the stop sequence string in the interim."					)
        UpperCAmelCase__							       =  stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Tuple       ,					lowerCamelCase      :int					)   ->	Optional[int]:
       return True
      def 		UpperCAmelCase_  (	self      :Dict       ,					*lowerCamelCase      :Any       ,					lowerCamelCase      :int					)   ->	Dict:
       UpperCAmelCase__							       =  self.model.config.prefix if self.model.config.prefix is not None else ''
       if isinstance(args[0]       ,					__UpperCamelCase					):
        if self.tokenizer.pad_token_id is None:
         raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"					)
        UpperCAmelCase__							       =  ([prefix + arg for arg in args[0]],)
        UpperCAmelCase__							       =  True

       elif isinstance(args[0]       ,					__UpperCamelCase					):
        UpperCAmelCase__							       =  (prefix + args[0],)
        UpperCAmelCase__							       =  False
       else:
        raise ValueError(
            f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''					)
       UpperCAmelCase__							       =  self.tokenizer(*__UpperCamelCase       ,					padding=__UpperCamelCase       ,					truncation=__UpperCamelCase       ,					return_tensors=self.framework					)
       # This is produced by tokenizers but is an invalid generate kwargs
       if "token_type_ids" in inputs:
        del inputs["token_type_ids"]
       return inputs
      def __call__(	self      :List[Any]       ,					*lowerCamelCase      :List[Any]       ,					**lowerCamelCase      :str					)   ->	Dict:
       UpperCAmelCase__							       =  super().__call__(*__UpperCamelCase       ,					**__UpperCamelCase					)
       if (
           isinstance(args[0]       ,					__UpperCamelCase					)
           and all(isinstance(__UpperCamelCase       ,					__UpperCamelCase					) for el in args[0]					)
           and all(len(__UpperCamelCase					) == 1 for res in result					)
       ):
        return [res[0] for res in result]
       return result
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :List[Any]       ,					lowerCamelCase      :List[Any]=TruncationStrategy.DO_NOT_TRUNCATE       ,					**lowerCamelCase      :Dict					)   ->	List[str]:
       UpperCAmelCase__							       =  self._parse_and_tokenize(__UpperCamelCase       ,					truncation=__UpperCamelCase       ,					**__UpperCamelCase					)
       return inputs
      def 		UpperCAmelCase_  (	self      :List[Any]       ,					lowerCamelCase      :Optional[int]       ,					**lowerCamelCase      :List[Any]					)   ->	Optional[int]:


       if self.framework == "pt":
        UpperCAmelCase__							       =  model_inputs['input_ids'].shape
       elif self.framework == "tf":
        UpperCAmelCase__							       =  tf.shape(model_inputs["input_ids"]					).numpy()

       UpperCAmelCase__							       =  generate_kwargs.get("min_length"       ,					self.model.config.min_length					)
       UpperCAmelCase__							       =  generate_kwargs.get("max_length"       ,					self.model.config.max_length					)
       self.check_inputs(__UpperCamelCase       ,					generate_kwargs["min_length"]       ,					generate_kwargs["max_length"]					)
       UpperCAmelCase__							       =  self.model.generate(**__UpperCamelCase       ,					**__UpperCamelCase					)
       UpperCAmelCase__							       =  output_ids.shape[0]
       if self.framework == "pt":
        UpperCAmelCase__							       =  output_ids.reshape(__UpperCamelCase       ,					out_b // in_b       ,					*output_ids.shape[1:]					)
       elif self.framework == "tf":
        UpperCAmelCase__							       =  tf.reshape(__UpperCamelCase       ,					(in_b, out_b // in_b, *output_ids.shape[1:])					)
       return {"output_ids": output_ids}







      def 		UpperCAmelCase_  (	self      :Any       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :Dict=ReturnType.TEXT       ,					lowerCamelCase      :Dict=False					)   ->	Dict:
       UpperCAmelCase__							       =  []
       for output_ids in model_outputs["output_ids"][0]:
        if return_type == ReturnType.TENSORS:
         UpperCAmelCase__							       =  {f'''{self.return_name}_token_ids''': output_ids}
        elif return_type == ReturnType.TEXT:
         UpperCAmelCase__							       =  {
             f'''{self.return_name}_text''': self.tokenizer.decode(
                 __UpperCamelCase       ,					skip_special_tokens=__UpperCamelCase       ,					clean_up_tokenization_spaces=__UpperCamelCase       ,					)
         }
        records.append(__UpperCamelCase					)
       return records



@add_end_docstrings(lowercase_						)
class 	_UpperCamelCase					( lowercase_						):
      UpperCAmelCase_						=						"""summary"""
      def __call__(	self      :List[str]       ,					*lowerCamelCase      :str       ,					**lowerCamelCase      :Tuple					)   ->	Union[str, Any]:
       return super().__call__(*__UpperCamelCase       ,					**__UpperCamelCase					)







      def 		UpperCAmelCase_  (	self      :List[Any]       ,					lowerCamelCase      :int       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :str					)   ->	bool:


       if max_length < min_length:
        logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''					)

       if input_length < max_length:
        logger.warning(
            f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
            "a summarization task, where outputs shorter than the input are typically wanted, you might "
            f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''					)



@add_end_docstrings(lowercase_						)
class 	_UpperCamelCase					( lowercase_						):
      UpperCAmelCase_						=						"""translation"""
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :int       ,					lowerCamelCase      :List[Any]       ,					lowerCamelCase      :List[Any]					)   ->	Dict:


       if input_length > 0.9 * max_length:
        logger.warning(
            f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
            "increasing your max_length manually, e.g. translator(\'...\', max_length=400)"					)
       return True
      def 		UpperCAmelCase_  (	self      :List[str]       ,					*lowerCamelCase      :Tuple       ,					lowerCamelCase      :int=TruncationStrategy.DO_NOT_TRUNCATE       ,					lowerCamelCase      :Any=None       ,					lowerCamelCase      :Optional[Any]=None					)   ->	int:


       if getattr(self.tokenizer       ,					"_build_translation_inputs"       ,					__UpperCamelCase					):
        return self.tokenizer._build_translation_inputs(
            *__UpperCamelCase       ,					return_tensors=self.framework       ,					truncation=__UpperCamelCase       ,					src_lang=__UpperCamelCase       ,					tgt_lang=__UpperCamelCase					)
       else:
        return super()._parse_and_tokenize(*__UpperCamelCase       ,					truncation=__UpperCamelCase					)
      def 		UpperCAmelCase_  (	self      :str       ,					lowerCamelCase      :str=None       ,					lowerCamelCase      :List[Any]=None       ,					**lowerCamelCase      :List[Any]					)   ->	str:
       UpperCAmelCase__							       =  super()._sanitize_parameters(**__UpperCamelCase					)
       if src_lang is not None:
        UpperCAmelCase__							       =  src_lang
       if tgt_lang is not None:
        UpperCAmelCase__							       =  tgt_lang
       if src_lang is None and tgt_lang is None:
        # Backward compatibility, direct arguments use is preferred.
        UpperCAmelCase__							       =  kwargs.get("task"       ,					self.task					)
        UpperCAmelCase__							       =  task.split("_"					)
        if task and len(__UpperCamelCase					) == 4:
         # translation, XX, to YY
         UpperCAmelCase__							       =  items[1]
         UpperCAmelCase__							       =  items[3]
       return preprocess_params, forward_params, postprocess_params







      def __call__(	self      :str       ,					*lowerCamelCase      :Union[str, Any]       ,					**lowerCamelCase      :Optional[Any]					)   ->	Dict:
       return super().__call__(*__UpperCamelCase       ,					**__UpperCamelCase					)






"""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			)

