





'''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       UpperCAmelCase :




  def __init__(self      :	Dict							,			A__      :	Tuple							,			A__      :	int=1_3							,			A__      :	Dict=3_0							,			A__      :	List[str]=2							,			A__      :	str=3							,			A__      :	str=True							,			A__      :	Dict=True							,			A__      :	Union[str, Any]=3_2							,			A__      :	int=2							,			A__      :	List[str]=4							,			A__      :	Any=3_7							,			A__      :	Optional[int]="gelu"							,			A__      :	Optional[Any]=0.1							,			A__      :	Dict=0.1							,			A__      :	Tuple=1_0							,			A__      :	Dict=0.0_2							,			A__      :	int=3							,			A__      :	str=None							,			A__      :	Union[str, Any]=2							,			)					-> Optional[Any]:
      lowercase         = parent
      lowercase         = batch_size
      lowercase         = image_size
      lowercase         = patch_size
      lowercase         = num_channels
      lowercase         = is_training
      lowercase         = use_labels
      lowercase         = hidden_size
      lowercase         = num_hidden_layers
      lowercase         = num_attention_heads
      lowercase         = intermediate_size
      lowercase         = hidden_act
      lowercase         = hidden_dropout_prob
      lowercase         = attention_probs_dropout_prob
      lowercase         = type_sequence_label_size
      lowercase         = initializer_range
      lowercase         = scope
      lowercase         = encoder_stride

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




  def 				UpperCAmelCase__   (self      :	str				)					-> Dict:
      lowercase         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

      lowercase         = self.get_config()

      return config, pixel_values, labels




  def 				UpperCAmelCase__   (self      :	List[str]				)					-> Optional[Any]:
      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=_SCREAMING_SNAKE_CASE							,			initializer_range=self.initializer_range							,			encoder_stride=self.encoder_stride							,			)




  def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Dict							,			A__      :	Tuple				)					-> List[str]:
      lowercase         = TFDeiTModel(config=_SCREAMING_SNAKE_CASE				)
      lowercase         = model(_SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)				)




  def 				UpperCAmelCase__   (self      :	Optional[Any]							,			A__      :	Optional[int]							,			A__      :	Union[str, Any]							,			A__      :	List[Any]				)					-> int:
      lowercase         = TFDeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE				)
      lowercase         = 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
      lowercase         = 1
      lowercase         = TFDeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE				)

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




  def 				UpperCAmelCase__   (self      :	Union[str, Any]							,			A__      :	Tuple							,			A__      :	List[str]							,			A__      :	Dict				)					-> int:
      lowercase         = self.type_sequence_label_size
      lowercase         = TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)
      lowercase         = 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
      lowercase         = 1
      lowercase         = TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE				)

      lowercase         = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
      lowercase         = model(_SCREAMING_SNAKE_CASE							,			labels=_SCREAMING_SNAKE_CASE				)
      self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)				)





  def 				UpperCAmelCase__   (self      :	str				)					-> str:
      lowercase         = self.prepare_config_and_inputs()
      lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
      lowercase         = {"pixel_values": pixel_values}
      return config, inputs_dict




@require_tf
class       UpperCAmelCase (						_lowercase			,       _lowercase			,       unittest.TestCase  ):
  UpperCAmelCase					:	Union[str, Any] 			=		(
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  UpperCAmelCase					:	Optional[Any] 			=		(
      {
          '''feature-extraction''': TFDeiTModel,
          '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

  UpperCAmelCase					:	List[str] 			=		False
  UpperCAmelCase					:	Optional[Any] 			=		False
  UpperCAmelCase					:	Optional[Any] 			=		False
  UpperCAmelCase					:	Optional[int] 			=		False




  def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
      lowercase         = TFDeiTModelTester(self				)
      lowercase         = ConfigTester(self							,			config_class=_SCREAMING_SNAKE_CASE							,			has_text_modality=_SCREAMING_SNAKE_CASE							,			hidden_size=3_7				)




  def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> str:
      self.config_tester.run_common_tests()




  @unittest.skip(reason="DeiT does not use inputs_embeds"				)
  def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[Any]:
      pass




  def 				UpperCAmelCase__   (self      :	Dict				)					-> List[Any]:
      lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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




  def 				UpperCAmelCase__   (self      :	Any				)					-> int:
      lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase         = ["pixel_values"]
          self.assertListEqual(arg_names[:1]							,			_SCREAMING_SNAKE_CASE				)




  def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[Any]:
      lowercase         = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)




  def 				UpperCAmelCase__   (self      :	List[str]				)					-> Union[str, Any]:
      lowercase         = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE				)




  def 				UpperCAmelCase__   (self      :	Any				)					-> List[str]:
      lowercase         = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE				)




  def 				UpperCAmelCase__   (self      :	Optional[int]							,			A__      :	str							,			A__      :	Any							,			A__      :	str=False				)					-> Union[str, Any]:
      lowercase         = super()._prepare_for_class(_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			return_labels=_SCREAMING_SNAKE_CASE				)

      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 				UpperCAmelCase__   (self      :	Tuple				)					-> str:






      for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase         = TFDeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
          self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)







def 							UpperCAmelCase_    (	):

    """simple docstring"""






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


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




  @cached_property
  def 				UpperCAmelCase__   (self      :	int				)					-> Any:
      return (
          DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
          if is_vision_available()
          else None
      )





  @slow
  def 				UpperCAmelCase__   (self      :	List[Any]				)					-> int:
      lowercase         = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				)

      lowercase         = self.default_image_processor
      lowercase         = prepare_img()
      lowercase         = image_processor(images=_SCREAMING_SNAKE_CASE							,			return_tensors="tf"				)

      # forward pass
      lowercase         = model(**_SCREAMING_SNAKE_CASE				)

      # verify the logits
      lowercase         = tf.TensorShape((1, 1_0_0_0)				)
      self.assertEqual(outputs.logits.shape							,			_SCREAMING_SNAKE_CASE				)

      lowercase         = 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]							,			_SCREAMING_SNAKE_CASE							,			atol=1e-4				)				)











'''simple docstring'''

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


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

__lowerCamelCase : Optional[Any]											=			{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		'''vit_mae'''




		def __init__(self      :	Dict							,			A__      :	Tuple=7_6_8							,			A__      :	str=1_2							,			A__      :	Dict=1_2							,			A__      :	Optional[Any]=3_0_7_2							,			A__      :	Union[str, Any]="gelu"							,			A__      :	Optional[int]=0.0							,			A__      :	str=0.0							,			A__      :	Optional[Any]=0.0_2							,			A__      :	Optional[Any]=1e-12							,			A__      :	Tuple=2_2_4							,			A__      :	str=1_6							,			A__      :	Optional[int]=3							,			A__      :	List[Any]=True							,			A__      :	Dict=1_6							,			A__      :	Dict=5_1_2							,			A__      :	Dict=8							,			A__      :	Union[str, Any]=2_0_4_8							,			A__      :	str=0.7_5							,			A__      :	str=False							,			**A__      :	Any							,			)					-> Union[str, Any]:
						super().__init__(**A__				)

						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = image_size
						lowercase         = patch_size
						lowercase         = num_channels
						lowercase         = qkv_bias
						lowercase         = decoder_num_attention_heads
						lowercase         = decoder_hidden_size
						lowercase         = decoder_num_hidden_layers
						lowercase         = decoder_intermediate_size
						lowercase         = mask_ratio
						lowercase         = norm_pix_loss




