

"""simple docstring"""




import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil







class      __lowercase			:

  def __init__(  self		:			str					,A		:			str = "cpu"					,A		:			str = "openai/clip-vit-large-patch14"    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					device
   UpperCAmelCase__					:  List[Any]					   =					CLIPTokenizerFast.from_pretrained(A    )
   UpperCAmelCase__					:  int					   =					[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
   UpperCAmelCase__					:  str					   =					[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
   UpperCAmelCase__					:  Any					   =					torchvision.transforms.Normalize(self.image_mean					,self.image_std    )
   UpperCAmelCase__					:  str					   =					torchvision.transforms.Resize(224    )
   UpperCAmelCase__					:  Optional[Any]					   =					torchvision.transforms.CenterCrop(224    )

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					self.resize(A    )
   UpperCAmelCase__					:  Any					   =					self.center_crop(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					self.normalize(A    )
   return images




  def __call__(  self		:			Optional[int]					,A		:			Optional[int]=None					,A		:			Any=None					,**A		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.tokenizer(text=A					,**A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					self.preprocess_img(A    )
   UpperCAmelCase__					:  List[str]					   =					{key: value.to(self.device    ) for (key, value) in encoding.items()}
   return encoding







class      __lowercase			(    nn.Module					):

  def __init__(  self		:			List[str]					,A		:			Union[str, Any]=10					,A		:			Union[str, Any]=0.0_1					,A		:			int=None					,A		:			Any=None					,A		:			Optional[int]=None					,A		:			List[Any]=None					,A		:			List[Any]=None					,A		:			str=None					,A		:			Union[str, Any]=False					,A		:			Union[str, Any]=True					,A		:			Tuple="image"					,A		:			Tuple=True					,A		:			Optional[int]=False					,A		:			Union[str, Any]=False					,A		:			List[Any]=False					,):






   '''simple docstring'''
   super().__init__()
   UpperCAmelCase__					:  Union[str, Any]					   =					None
   UpperCAmelCase__					:  Optional[int]					   =					device if device else get_device()
   if vqgan:
    UpperCAmelCase__					:  Optional[int]					   =					vqgan
   else:
    UpperCAmelCase__					:  Any					   =					load_vqgan(self.device					,conf_path=A					,ckpt_path=A    )
   self.vqgan.eval()
   if clip:
    UpperCAmelCase__					:  Any					   =					clip
   else:
    UpperCAmelCase__					:  Dict					   =					CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""    )
   self.clip.to(self.device    )
   UpperCAmelCase__					:  int					   =					ProcessorGradientFlow(device=self.device    )

   UpperCAmelCase__					:  Dict					   =					iterations
   UpperCAmelCase__					:  Optional[Any]					   =					lr
   UpperCAmelCase__					:  str					   =					log
   UpperCAmelCase__					:  List[Any]					   =					make_grid
   UpperCAmelCase__					:  Tuple					   =					return_val
   UpperCAmelCase__					:  int					   =					quantize
   UpperCAmelCase__					:  Optional[int]					   =					self.vqgan.decoder.z_shape

  def    __lowercase       (  self		:			str					,A		:			str=None					,A		:			Optional[Any]=None					,A		:			Optional[Any]=5					,A		:			Any=True    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					[]
   if output_path is None:
    UpperCAmelCase__					:  List[Any]					   =					"""./animation.gif"""
   if input_path is None:
    UpperCAmelCase__					:  Optional[int]					   =					self.save_path
   UpperCAmelCase__					:  str					   =					sorted(glob(input_path + """/*"""    )    )
   if not len(A    ):
    raise ValueError(
        """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
        """ function?)"""    )
   if len(A    ) == 1:
    print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""    )
   UpperCAmelCase__					:  List[str]					   =					total_duration / len(A    )
   UpperCAmelCase__					:  int					   =					[frame_duration] * len(A    )
   if extend_frames:
    UpperCAmelCase__					:  str					   =					1.5
    UpperCAmelCase__					:  List[str]					   =					3
   for file_name in paths:
    if file_name.endswith(""".png"""    ):
     images.append(imageio.imread(A    )    )
   imageio.mimsave(A					,A					,duration=A    )
   print(f"gif saved to {output_path}"    )

  def    __lowercase       (  self		:			Optional[Any]					,A		:			Optional[int]=None					,A		:			List[str]=None    ):






   '''simple docstring'''
   if not (path or img):
    raise ValueError("""Input either path or tensor"""    )
   if img is not None:
    raise NotImplementedError
   UpperCAmelCase__					:  Dict					   =					preprocess(Image.open(A    )					,target_image_size=256    ).to(self.device    )
   UpperCAmelCase__					:  Union[str, Any]					   =					preprocess_vqgan(A    )
   UpperCAmelCase__   ,      *UpperCAmelCase__					:  Union[str, Any]					   =					self.vqgan.encode(A    )
   return z

  def    __lowercase       (  self		:			Dict					,A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					self.latent.detach().requires_grad_()
   UpperCAmelCase__					:  List[str]					   =					base_latent + transform_vector
   if self.quantize:
    UpperCAmelCase__   ,      *UpperCAmelCase__					:  Optional[int]					   =					self.vqgan.quantize(A    )
   else:
    UpperCAmelCase__					:  int					   =					trans_latent
   return self.vqgan.decode(A    )

  def    __lowercase       (  self		:			List[Any]					,A		:			Optional[Any]					,A		:			str					,A		:			Any=None    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					self.clip_preprocessor(text=A					,images=A					,return_tensors="""pt"""					,padding=A    )
   UpperCAmelCase__					:  Tuple					   =					self.clip(**A    )
   UpperCAmelCase__					:  Optional[int]					   =					clip_outputs.logits_per_image
   if weights is not None:
    UpperCAmelCase__					:  Optional[int]					   =					similarity_logits * weights
   return similarity_logits.sum()

  def    __lowercase       (  self		:			List[Any]					,A		:			Tuple					,A		:			List[Any]					,A		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self._get_clip_similarity(pos_prompts["""prompts"""]					,A					,weights=(1 / pos_prompts["""weights"""])    )
   if neg_prompts:
    UpperCAmelCase__					:  Any					   =					self._get_clip_similarity(neg_prompts["""prompts"""]					,A					,weights=neg_prompts["""weights"""]    )
   else:
    UpperCAmelCase__					:  Dict					   =					torch.tensor([1]					,device=self.device    )
   UpperCAmelCase__					:  Dict					   =					-torch.log(A    ) + torch.log(A    )
   return loss

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[Any]					,A		:			Dict					,A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					torch.randn_like(self.latent					,requires_grad=A					,device=self.device    )
   UpperCAmelCase__					:  str					   =					torch.optim.Adam([vector]					,lr=self.lr    )

   for i in range(self.iterations    ):
    optim.zero_grad()
    UpperCAmelCase__					:  Dict					   =					self._add_vector(A    )
    UpperCAmelCase__					:  Optional[int]					   =					loop_post_process(A    )
    UpperCAmelCase__					:  Optional[int]					   =					self._get_CLIP_loss(A					,A					,A    )
    print("""CLIP loss"""					,A    )
    if self.log:
     wandb.log({"""CLIP Loss""": clip_loss}    )
    clip_loss.backward(retain_graph=A    )
    optim.step()
    if self.return_val == "image":
     yield custom_to_pil(transformed_img[0]    )
    else:
     yield vector

  def    __lowercase       (  self		:			Dict					,A		:			str					,A		:			str					,A		:			Optional[Any]    ):






   '''simple docstring'''
   wandb.init(reinit=A					,project="""face-editor"""    )
   wandb.config.update({"""Positive Prompts""": positive_prompts}    )
   wandb.config.update({"""Negative Prompts""": negative_prompts}    )
   wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}    )
   if image_path:
    UpperCAmelCase__					:  Any					   =					Image.open(A    )
    UpperCAmelCase__					:  List[Any]					   =					image.resize((256, 256)    )
    wandb.log("""Original Image"""					,wandb.Image(A    )    )

  def    __lowercase       (  self		:			int					,A		:			List[str]    ):






   '''simple docstring'''
   if not prompts:
    return []
   UpperCAmelCase__					:  Any					   =					[]
   UpperCAmelCase__					:  int					   =					[]
   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Tuple					   =					[prompt.strip() for prompt in prompts.split("""|"""    )]
   for prompt in prompts:
    if isinstance(A					,(tuple, list)    ):
     UpperCAmelCase__					:  str					   =					prompt[0]
     UpperCAmelCase__					:  Optional[int]					   =					float(prompt[1]    )
    elif ":" in prompt:
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					prompt.split(""":"""    )
     UpperCAmelCase__					:  str					   =					float(A    )
    else:
     UpperCAmelCase__					:  List[str]					   =					prompt
     UpperCAmelCase__					:  str					   =					1.0
    processed_prompts.append(A    )
    weights.append(A    )
   return {
       "prompts": processed_prompts,
       "weights": torch.tensor(A					,device=self.device    ),
   }




  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Union[str, Any]					,A		:			int=None					,A		:			List[str]=None					,A		:			Optional[Any]=True					,A		:			Any=False					,A		:			List[str]=True					,A		:			Dict=True					,A		:			List[Any]=None					,):






   '''simple docstring'''
   if image_path:
    UpperCAmelCase__					:  int					   =					self._get_latent(A    )
   else:
    UpperCAmelCase__					:  Dict					   =					torch.randn(self.latent_dim					,device=self.device    )
   if self.log:
    self._init_logging(A					,A					,A    )

   assert pos_prompts, "You must provide at least one positive prompt."
   UpperCAmelCase__					:  int					   =					self.process_prompts(A    )
   UpperCAmelCase__					:  Dict					   =					self.process_prompts(A    )
   if save_final and save_path is None:
    UpperCAmelCase__					:  Optional[int]					   =					os.path.join("""./outputs/"""					,"""_""".join(pos_prompts["""prompts"""]    )    )
   if not os.path.exists(A    ):
    os.makedirs(A    )
   else:
    UpperCAmelCase__					:  Tuple					   =					save_path + """_""" + get_timestamp()
    os.makedirs(A    )
   UpperCAmelCase__					:  Tuple					   =					save_path

   UpperCAmelCase__					:  int					   =					self.vqgan.decode(self.latent    )[0]
   if show_intermediate:
    print("""Original Image"""    )
    show_pil(custom_to_pil(A    )    )

   UpperCAmelCase__					:  int					   =					loop_post_process(A    )
   for iter, transformed_img in enumerate(self._optimize_CLIP(A					,A					,A    )    ):
    if show_intermediate:
     show_pil(A    )
    if save_intermediate:
     transformed_img.save(os.path.join(self.save_path					,f"iter_{iter:03d}.png"    )    )
    if self.log:
     wandb.log({"""Image""": wandb.Image(A    )}    )
   if show_final:
    show_pil(A    )
   if save_final:
    transformed_img.save(os.path.join(self.save_path					,f"iter_{iter:03d}_final.png"    )    )








"""simple docstring"""




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor







class      __lowercase			:

  def __init__(  self		:			Optional[int]					,A		:			Optional[Any]					,A		:			int=13					,A		:			Any=30					,A		:			str=2					,A		:			Tuple=3					,A		:			Optional[Any]=True					,A		:			Union[str, Any]=True					,A		:			Dict=32					,A		:			Optional[int]=2					,A		:			Any=4					,A		:			str=37					,A		:			int="gelu"					,A		:			Dict=0.1					,A		:			str=0.1					,A		:			Optional[Any]=10					,A		:			List[str]=0.0_2					,A		:			Any=3					,A		:			Any=None					,A		:			int=2					,):






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

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

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

   UpperCAmelCase__					:  int					   =					self.get_config()

   return config, pixel_values, labels

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''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		:			Union[str, Any]					,A		:			Optional[int]					,A		:			Optional[Any]					,A		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					TFDeiTModel(config=A    )
   UpperCAmelCase__					:  Dict					   =					model(A    )
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			Union[str, Any]					,A		:			Optional[int]					,A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					TFDeiTForMaskedImageModeling(config=A    )
   UpperCAmelCase__					:  Any					   =					model(A    )
   self.parent.assertEqual(
       result.reconstruction.shape					,(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

   # test greyscale images
   UpperCAmelCase__					:  Union[str, Any]					   =					1
   UpperCAmelCase__					:  Any					   =					TFDeiTForMaskedImageModeling(A    )

   UpperCAmelCase__					:  str					   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
   UpperCAmelCase__					:  str					   =					model(A    )
   self.parent.assertEqual(result.reconstruction.shape					,(self.batch_size, 1, self.image_size, self.image_size)    )

  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			Optional[Any]					,A		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.type_sequence_label_size
   UpperCAmelCase__					:  int					   =					TFDeiTForImageClassification(A    )
   UpperCAmelCase__					:  int					   =					model(A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)    )

   # test greyscale images
   UpperCAmelCase__					:  Optional[int]					   =					1
   UpperCAmelCase__					:  int					   =					TFDeiTForImageClassification(A    )

   UpperCAmelCase__					:  List[str]					   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
   UpperCAmelCase__					:  str					   =					model(A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)    )




  def    __lowercase       (  self		:			int    ):






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







@require_tf
class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       (
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  snake_case_				       =       (
      {
          """feature-extraction""": TFDeiTModel,
          """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False
  snake_case_				       =       False

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					TFDeiTModelTester(self    )
   UpperCAmelCase__					:  int					   =					ConfigTester(self					,config_class=A					,has_text_modality=A					,hidden_size=37    )

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






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

  @unittest.skip(reason="""DeiT does not use inputs_embeds"""    )
  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  int					   =					model_class(A    )
    self.assertIsInstance(model.get_input_embeddings()					,(tf.keras.layers.Layer)    )
    UpperCAmelCase__					:  Tuple					   =					model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(A					,tf.keras.layers.Dense    )    )

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__					:  Dict					   =					model_class(A    )
    UpperCAmelCase__					:  Any					   =					inspect.signature(model.call    )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    UpperCAmelCase__					:  List[Any]					   =					[*signature.parameters.keys()]

    UpperCAmelCase__					:  Optional[int]					   =					["""pixel_values"""]
    self.assertListEqual(arg_names[:1]					,A    )

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*A    )

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*A    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*A    )

  def    __lowercase       (  self		:			str					,A		:			List[str]					,A		:			Optional[int]					,A		:			Union[str, Any]=False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					super()._prepare_for_class(A					,A					,return_labels=A    )

   if return_labels:
    if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call    ).parameters:
     del inputs_dict["labels"]

   return inputs_dict




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






   '''simple docstring'''
   for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__					:  List[Any]					   =					TFDeiTModel.from_pretrained(A    )
    self.assertIsNotNone(A    )







def   lowerCAmelCase						(					):
 '''simple docstring'''





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







@require_tf
@require_vision
class      __lowercase			(    unittest.TestCase					):

  @cached_property
  def    __lowercase       (  self		:			int    ):






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




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






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""    )

   UpperCAmelCase__					:  Union[str, Any]					   =					self.default_image_processor
   UpperCAmelCase__					:  Optional[int]					   =					prepare_img()
   UpperCAmelCase__					:  Optional[int]					   =					image_processor(images=A					,return_tensors="""tf"""    )

   # forward pass
   UpperCAmelCase__					:  Optional[Any]					   =					model(**A    )

   # verify the logits
   UpperCAmelCase__					:  int					   =					tf.TensorShape((1, 1_000)    )
   self.assertEqual(outputs.logits.shape					,A    )

   UpperCAmelCase__					:  Any					   =					tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]    )

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





