import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					int						=					{
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     "sew"




  def __init__(      self     :	Any     , a_     :	Dict=32     , a_     :	Optional[Any]=768     , a_     :	List[Any]=12     , a_     :	Any=12     , a_     :	str=3072     , a_     :	Tuple=2     , a_     :	List[Any]="gelu"     , a_     :	Optional[Any]=0.1     , a_     :	Optional[Any]=0.1     , a_     :	Optional[int]=0.1     , a_     :	Any=0.0     , a_     :	str=0.1     , a_     :	Optional[int]=0.1     , a_     :	Dict=0.02     , a_     :	List[str]=1e-5     , a_     :	List[Any]="group"     , a_     :	List[str]="gelu"     , a_     :	Optional[int]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     , a_     :	Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     , a_     :	Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     , a_     :	Tuple=False     , a_     :	int=128     , a_     :	Any=16     , a_     :	List[Any]=True     , a_     :	List[Any]=0.05     , a_     :	List[str]=10     , a_     :	Optional[Any]=2     , a_     :	Union[str, Any]=0.0     , a_     :	Dict=10     , a_     :	Tuple=0     , a_     :	List[Any]="mean"     , a_     :	List[Any]=False     , a_     :	str=False     , a_     :	Tuple=256     , a_     :	int=0     , a_     :	str=1     , a_     :	List[str]=2     , **a_     :	Optional[int]     , )->       Tuple:




       """simple docstring"""
       super().__init__(**a_     , pad_token_id=a_     , bos_token_id=a_     , eos_token_id=a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		hidden_size
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		feat_extract_norm
       SCREAMING_SNAKE_CASE__				:				Dict			=		feat_extract_activation
       SCREAMING_SNAKE_CASE__				:				Any			=		list(a_				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		list(a_				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		list(a_				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		conv_bias
       SCREAMING_SNAKE_CASE__				:				int			=		num_conv_pos_embeddings
       SCREAMING_SNAKE_CASE__				:				Any			=		num_conv_pos_embedding_groups
       SCREAMING_SNAKE_CASE__				:				int			=		len(self.conv_dim				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		num_hidden_layers
       SCREAMING_SNAKE_CASE__				:				Dict			=		intermediate_size
       SCREAMING_SNAKE_CASE__				:				Dict			=		squeeze_factor
       SCREAMING_SNAKE_CASE__				:				str			=		hidden_act
       SCREAMING_SNAKE_CASE__				:				Dict			=		num_attention_heads
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hidden_dropout
       SCREAMING_SNAKE_CASE__				:				str			=		attention_dropout
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		activation_dropout
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		feat_proj_dropout
       SCREAMING_SNAKE_CASE__				:				int			=		final_dropout
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		layerdrop
       SCREAMING_SNAKE_CASE__				:				Dict			=		layer_norm_eps
       SCREAMING_SNAKE_CASE__				:				Tuple			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				Any			=		vocab_size

       if (
           (len(self.conv_stride				) != self.num_feat_extract_layers)
           or (len(self.conv_kernel				) != self.num_feat_extract_layers)
           or (len(self.conv_dim				) != self.num_feat_extract_layers)
       ):
            raise ValueError(
                'Configuration for convolutional layers is incorrect.'
                'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
                F'''but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)'''
                F'''= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'''				)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       SCREAMING_SNAKE_CASE__				:				str			=		apply_spec_augment
       SCREAMING_SNAKE_CASE__				:				int			=		mask_time_prob
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		mask_time_length
       SCREAMING_SNAKE_CASE__				:				Dict			=		mask_time_min_masks
       SCREAMING_SNAKE_CASE__				:				Any			=		mask_feature_prob
       SCREAMING_SNAKE_CASE__				:				List[str]			=		mask_feature_length
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		mask_feature_min_masks

       # ctc loss
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ctc_loss_reduction
       SCREAMING_SNAKE_CASE__				:				int			=		ctc_zero_infinity

       # sequence classification
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		use_weighted_layer_sum
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		classifier_proj_size




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       return functools.reduce(operator.mul     , self.conv_stride     , 1				)




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
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 (
          MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
          DeiTModel,
      )
      from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor

class       snake_case					:




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




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

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		num_patches + 2




  def 	__lowercase(      self     :	Optional[Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.get_config()

       return config, pixel_values, labels




  def 	__lowercase(      self     :	Optional[Any]				)->       Tuple:




       """simple docstring"""
       return DeiTConfig(
           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=a_     , initializer_range=self.initializer_range     , encoder_stride=self.encoder_stride     , )




  def 	__lowercase(      self     :	List[str]     , a_     :	List[str]     , a_     :	Optional[Any]     , a_     :	str				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		DeiTModel(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_				)
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)				)




  def 	__lowercase(      self     :	List[Any]     , a_     :	List[str]     , a_     :	List[str]     , a_     :	List[Any]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		DeiTForMaskedImageModeling(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_				)
       self.parent.assertEqual(
           result.reconstruction.shape     , (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

       # test greyscale images
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		1
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		DeiTForMaskedImageModeling(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       SCREAMING_SNAKE_CASE__				:				int			=		model(a_				)
       self.parent.assertEqual(result.reconstruction.shape     , (self.batch_size, 1, self.image_size, self.image_size)				)




  def 	__lowercase(      self     :	List[str]     , a_     :	Union[str, Any]     , a_     :	Union[str, Any]     , a_     :	Tuple				)->       List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		DeiTForImageClassification(a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)				)

       # test greyscale images
       SCREAMING_SNAKE_CASE__				:				Any			=		1
       SCREAMING_SNAKE_CASE__				:				int			=		DeiTForImageClassification(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				Dict			=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							
       )				:				List[Any]			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				Dict			=		{'pixel_values': pixel_values}
       return config, inputs_dict




@require_torch
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (
      (
          DeiTModel,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  lowercase_			=     (
      {
          'feature-extraction': DeiTModel,
          'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  lowercase_			=     False
  lowercase_			=     False
  lowercase_			=     False




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		DeiTModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ConfigTester(self     , config_class=a_     , has_text_modality=a_     , hidden_size=37				)




  def 	__lowercase(      self     :	Optional[Any]				)->       List[Any]:




       """simple docstring"""
       self.config_tester.run_common_tests()




  @unittest.skip(reason='DeiT does not use inputs_embeds'				)
  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       pass




  def 	__lowercase(      self     :	str				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()

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




  def 	__lowercase(      self     :	str				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		['pixel_values']
            self.assertListEqual(arg_names[:1]     , a_				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a_				)




  def 	__lowercase(      self     :	List[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a_				)




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a_				)




  def 	__lowercase(      self     :	str     , a_     :	str     , a_     :	Tuple     , a_     :	Union[str, Any]=False				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		super()._prepare_for_class(a_     , a_     , return_labels=a_				)

       if return_labels:
            if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                 del inputs_dict["labels"]

       return inputs_dict




  def 	__lowercase(      self     :	Optional[Any]				)->       Any:




       """simple docstring"""
       if not self.model_tester.is_training:
            return

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		True

       for model_class in self.all_model_classes:
            # DeiTForImageClassificationWithTeacher supports inference-only
            if (
                model_class in get_values(a_				)
                or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
            ):
                 continue
            SCREAMING_SNAKE_CASE__				:				Tuple			=		model_class(a_				)
            model.to(a_				)
            model.train()
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		self._prepare_for_class(a_     , a_     , return_labels=a_				)
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(**a_				).loss
            loss.backward()




  def 	__lowercase(      self     :	Optional[int]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
            return

       SCREAMING_SNAKE_CASE__				:				List[str]			=		False
       SCREAMING_SNAKE_CASE__				:				Tuple			=		True

       for model_class in self.all_model_classes:
            if model_class in get_values(a_				) or not model_class.supports_gradient_checkpointing:
                 continue
            # DeiTForImageClassificationWithTeacher supports inference-only
            if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                 continue
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model_class(a_				)
            model.gradient_checkpointing_enable()
            model.to(a_				)
            model.train()
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._prepare_for_class(a_     , a_     , return_labels=a_				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(**a_				).loss
            loss.backward()




  def 	__lowercase(      self     :	Optional[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE__				:				List[str]			=		[
           {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
           {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
           {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
       ]

       for model_class in self.all_model_classes:
            if (
                model_class
                not in [
                    *get_values(a_				),
                    *get_values(a_				),
                ]
                or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
            ):
                 continue

            for problem_type in problem_types:
                 with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''				):
                      SCREAMING_SNAKE_CASE__				:				int			=		problem_type['title']
                      SCREAMING_SNAKE_CASE__				:				Tuple			=		problem_type['num_labels']

                      SCREAMING_SNAKE_CASE__				:				str			=		model_class(a_				)
                      model.to(a_				)
                      model.train()

                      SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._prepare_for_class(a_     , a_     , return_labels=a_				)

                      if problem_type["num_labels"] > 1:
                           SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		inputs['labels'].unsqueeze(1				).repeat(1     , problem_type['num_labels']				)

                      SCREAMING_SNAKE_CASE__				:				Any			=		inputs['labels'].to(problem_type['dtype']				)

                      # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                      # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                      # they have the same size." which is a symptom something in wrong for the regression problem.
                      # See https://github.com/huggingface/transformers/issues/11780
                      with warnings.catch_warnings(record=a_				) as warning_list:
                           SCREAMING_SNAKE_CASE__				:				str			=		model(**a_				).loss
                      for w in warning_list:
                           if "Using a target size that is different to the input size" in str(w.message				):
                                raise ValueError(
                                    F'''Something is going wrong in the regression problem: intercepted {w.message}'''				)

                      loss.backward()




  @slow
  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[int]:




       """simple docstring"""
       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		DeiTModel.from_pretrained(a_				)
            self.assertIsNotNone(a_				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
     return image







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




  @cached_property
  def 	__lowercase(      self     :	int				)->       Dict:




       """simple docstring"""
       return (
           DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'				)
           if is_vision_available()
           else None
       )




  @slow
  def 	__lowercase(      self     :	Any				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'				).to(
           a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.default_image_processor
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_img()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(images=a_     , return_tensors='pt'				).to(a_				)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(**a_				)

       # verify the logits
       SCREAMING_SNAKE_CASE__				:				int			=		torch.Size((1, 1000)				)
       self.assertEqual(outputs.logits.shape     , a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.tensor([-1.0266, 0.1912, -1.2861]				).to(a_				)

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




  @slow
  @require_accelerate
  @require_torch_gpu
  def 	__lowercase(      self     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		DeiTModel.from_pretrained(
           'facebook/deit-base-distilled-patch16-224'     , torch_dtype=torch.floataa     , device_map='auto'				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.default_image_processor

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

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_				)



