'''simple docstring'''
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor







class lowerCamelCase							:




  '''simple docstring'''



  def __init__(      self			:       Optional[Any]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       List[str]=13  ,							UpperCAmelCase__			:       Union[str, Any]=30  ,							UpperCAmelCase__			:       Dict=2  ,							UpperCAmelCase__			:       Optional[int]=3  ,							UpperCAmelCase__			:       Any=True  ,							UpperCAmelCase__			:       Any=True  ,							UpperCAmelCase__			:       Tuple=32  ,							UpperCAmelCase__			:       Optional[Any]=2  ,							UpperCAmelCase__			:       Tuple=4  ,							UpperCAmelCase__			:       Optional[int]=37  ,							UpperCAmelCase__			:       Optional[int]="gelu"  ,							UpperCAmelCase__			:       int=0.1  ,							UpperCAmelCase__			:       List[Any]=0.1  ,							UpperCAmelCase__			:       Any=10  ,							UpperCAmelCase__			:       int=0.02  ,							UpperCAmelCase__			:       Dict=3  ,							UpperCAmelCase__			:       Optional[Any]=0.6  ,							UpperCAmelCase__			:       Optional[int]=None  ,							)				->Union[str, Any]:
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  image_size
        UpperCAmelCase_            =  patch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  is_training
        UpperCAmelCase_            =  use_labels
        UpperCAmelCase_            =  hidden_size
        UpperCAmelCase_            =  num_hidden_layers
        UpperCAmelCase_            =  num_attention_heads
        UpperCAmelCase_            =  intermediate_size
        UpperCAmelCase_            =  hidden_act
        UpperCAmelCase_            =  hidden_dropout_prob
        UpperCAmelCase_            =  attention_probs_dropout_prob
        UpperCAmelCase_            =  type_sequence_label_size
        UpperCAmelCase_            =  initializer_range
        UpperCAmelCase_            =  mask_ratio
        UpperCAmelCase_            =  scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        UpperCAmelCase_            =  (image_size // patch_size) ** 2
        UpperCAmelCase_            =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )






  def  lowerCAmelCase__ (      self			:       str  )				->Tuple:
        UpperCAmelCase_            =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

        UpperCAmelCase_            =  self.get_config()

        return config, pixel_values, labels






  def  lowerCAmelCase__ (      self			:       Dict  )				->Tuple:
        return ViTMAEConfig(
            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  ,							decoder_hidden_size=self.hidden_size  ,							decoder_num_hidden_layers=self.num_hidden_layers  ,							decoder_num_attention_heads=self.num_attention_heads  ,							decoder_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  ,							mask_ratio=self.mask_ratio  ,							)






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Dict  )				->Union[str, Any]:
        UpperCAmelCase_            =  TFViTMAEModel(config=UpperCAmelCase__  )
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							training=UpperCAmelCase__  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size)  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       Tuple  )				->Tuple:
        UpperCAmelCase_            =  TFViTMAEForPreTraining(UpperCAmelCase__  )
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							training=UpperCAmelCase__  )
        # expected sequence length = num_patches
        UpperCAmelCase_            =  (self.image_size // self.patch_size) ** 2
        UpperCAmelCase_            =  self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, num_patches, expected_num_channels)  )

        # test greyscale images
        UpperCAmelCase_            =  1
        UpperCAmelCase_            =  TFViTMAEForPreTraining(UpperCAmelCase__  )

        UpperCAmelCase_            =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        UpperCAmelCase_            =  model(UpperCAmelCase__  ,							training=UpperCAmelCase__  )
        UpperCAmelCase_            =  self.patch_size**2
        self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, num_patches, expected_num_channels)  )






  def  lowerCAmelCase__ (      self			:       int  )				->Tuple:
        UpperCAmelCase_            =  self.prepare_config_and_inputs()
        ((UpperCAmelCase_)      ,		(UpperCAmelCase_)      ,		(UpperCAmelCase_))            =  config_and_inputs
        UpperCAmelCase_            =  {'''pixel_values''': pixel_values}
        return config, inputs_dict







@require_tf
class lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
  lowerCAmelCase__										=	{'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

  lowerCAmelCase__										=	False
  lowerCAmelCase__										=	False
  lowerCAmelCase__										=	False
  lowerCAmelCase__										=	False






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->int:
        UpperCAmelCase_            =  TFViTMAEModelTester(self  )
        UpperCAmelCase_            =  ConfigTester(self  ,							config_class=UpperCAmelCase__  ,							has_text_modality=UpperCAmelCase__  ,							hidden_size=37  )






  def  lowerCAmelCase__ (      self			:       Dict  )				->List[str]:
        self.config_tester.run_common_tests()






  @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''  )
  def  lowerCAmelCase__ (      self			:       str  )				->Dict:
        pass






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->List[str]:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

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






  def  lowerCAmelCase__ (      self			:       List[str]  )				->str:
        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_            =  ['''pixel_values''']
              self.assertListEqual(arg_names[:1]  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       str  )				->List[Any]:
        UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Optional[Any]:
        # make the mask reproducible
        np.random.seed(2  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase_            =  int((config.image_size // config.patch_size) ** 2  )
        UpperCAmelCase_            =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

        for model_class in self.all_model_classes:
              UpperCAmelCase_            =  model_class(UpperCAmelCase__  )
              UpperCAmelCase_            =  self._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  )

              UpperCAmelCase_            =  model(UpperCAmelCase__  ,							noise=UpperCAmelCase__  )

              UpperCAmelCase_            =  copy.deepcopy(self._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  )  )
              UpperCAmelCase_            =  model(**UpperCAmelCase__  ,							noise=UpperCAmelCase__  )
              UpperCAmelCase_            =  outputs_dict[0].numpy()
              UpperCAmelCase_            =  outputs_keywords[0].numpy()

              self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )  ,							1e-6  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[int]:
        # make the mask reproducible
        np.random.seed(2  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase_            =  int((config.image_size // config.patch_size) ** 2  )
        UpperCAmelCase_            =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

        def prepare_numpy_arrays(UpperCAmelCase__			:       Optional[Any]  ):
              UpperCAmelCase_            =  {}
              for k, v in inputs_dict.items():
                    if tf.is_tensor(UpperCAmelCase__  ):
                          UpperCAmelCase_            =  v.numpy()
                    else:
                          UpperCAmelCase_            =  np.array(UpperCAmelCase__  )

              return inputs_np_dict

        for model_class in self.all_model_classes:
              UpperCAmelCase_            =  model_class(UpperCAmelCase__  )

              UpperCAmelCase_            =  self._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  )
              UpperCAmelCase_            =  prepare_numpy_arrays(UpperCAmelCase__  )

              UpperCAmelCase_            =  model(UpperCAmelCase__  ,							noise=UpperCAmelCase__  )
              UpperCAmelCase_            =  model(**UpperCAmelCase__  ,							noise=UpperCAmelCase__  )
              self.assert_outputs_same(UpperCAmelCase__  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Optional[int]  )				->Tuple:
        # make masks reproducible
        np.random.seed(2  )

        UpperCAmelCase_            =  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
        UpperCAmelCase_            =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
        UpperCAmelCase_            =  tf.constant(UpperCAmelCase__  )

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        UpperCAmelCase_            =  tf_noise

        super().check_pt_tf_models(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Union[str, Any]:
        # make mask reproducible
        np.random.seed(2  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase_            =  {
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__  ),)
            for module_member_name in dir(UpperCAmelCase__  )
            if module_member_name.endswith('''MainLayer'''  )
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len('''MainLayer'''  )] == model_class.__name__[: -len('''Model'''  )]
            for module_member in (getattr(UpperCAmelCase__  ,							UpperCAmelCase__  ),)
            if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(UpperCAmelCase__  ,							'''_keras_serializable'''  ,							UpperCAmelCase__  )
        }

        UpperCAmelCase_            =  int((config.image_size // config.patch_size) ** 2  )
        UpperCAmelCase_            =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
        UpperCAmelCase_            =  tf.convert_to_tensor(UpperCAmelCase__  )
        inputs_dict.update({'''noise''': noise}  )

        for main_layer_class in tf_main_layer_classes:
              UpperCAmelCase_            =  main_layer_class(UpperCAmelCase__  )

              UpperCAmelCase_            =  {
                  name: tf.keras.Input(tensor.shape[1:]  ,							dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
              }

              UpperCAmelCase_            =  tf.keras.Model(UpperCAmelCase__  ,							outputs=main_layer(UpperCAmelCase__  )  )
              UpperCAmelCase_            =  model(UpperCAmelCase__  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                    UpperCAmelCase_            =  os.path.join(UpperCAmelCase__  ,							'''keras_model.h5'''  )
                    model.save(UpperCAmelCase__  )
                    UpperCAmelCase_            =  tf.keras.models.load_model(
                        UpperCAmelCase__  ,							custom_objects={main_layer_class.__name__: main_layer_class}  )
                    assert isinstance(UpperCAmelCase__  ,							tf.keras.Model  )
                    UpperCAmelCase_            =  model(UpperCAmelCase__  )
                    self.assert_outputs_same(UpperCAmelCase__  ,							UpperCAmelCase__  )






  @slow
  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[str]:
        # make mask reproducible
        np.random.seed(2  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase_            =  int((config.image_size // config.patch_size) ** 2  )
        UpperCAmelCase_            =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

        for model_class in self.all_model_classes:
              UpperCAmelCase_            =  model_class(UpperCAmelCase__  )
              UpperCAmelCase_            =  self._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  )
              UpperCAmelCase_            =  model(UpperCAmelCase__  ,							noise=UpperCAmelCase__  )

              if model_class.__name__ == "TFViTMAEModel":
                    UpperCAmelCase_            =  outputs.last_hidden_state.numpy()
                    UpperCAmelCase_            =  0
              else:
                    UpperCAmelCase_            =  outputs.logits.numpy()
                    UpperCAmelCase_            =  0

              with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(UpperCAmelCase__  ,							saved_model=UpperCAmelCase__  )
                    UpperCAmelCase_            =  model_class.from_pretrained(UpperCAmelCase__  )
                    UpperCAmelCase_            =  model(UpperCAmelCase__  ,							noise=UpperCAmelCase__  )

                    if model_class.__name__ == "TFViTMAEModel":
                          UpperCAmelCase_            =  after_outputs['''last_hidden_state'''].numpy()
                          UpperCAmelCase_            =  0
                    else:
                          UpperCAmelCase_            =  after_outputs['''logits'''].numpy()
                          UpperCAmelCase_            =  0

                    UpperCAmelCase_            =  np.amax(np.abs(out_a - out_a  )  )
                    self.assertLessEqual(UpperCAmelCase__  ,							1e-5  )






  def  lowerCAmelCase__ (      self			:       str  )				->List[Any]:
        # make mask reproducible
        np.random.seed(2  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.model_tester.prepare_config_and_inputs_for_common()

        UpperCAmelCase_            =  int((config.image_size // config.patch_size) ** 2  )
        UpperCAmelCase_            =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

        for model_class in self.all_model_classes:
              UpperCAmelCase_            =  model_class(UpperCAmelCase__  )
              UpperCAmelCase_            =  self._prepare_for_class(UpperCAmelCase__  ,							UpperCAmelCase__  )

              UpperCAmelCase_            =  model(UpperCAmelCase__  ,							noise=UpperCAmelCase__  )
              UpperCAmelCase_            =  model.get_config()
              # make sure that returned config is jsonifiable, which is required by keras
              json.dumps(UpperCAmelCase__  )
              UpperCAmelCase_            =  model_class.from_config(model.get_config()  )
              # make sure it also accepts a normal config
              UpperCAmelCase_            =  model_class.from_config(model.config  )
              UpperCAmelCase_            =  new_model(UpperCAmelCase__  )  # Build model
              new_model.set_weights(model.get_weights()  )
              UpperCAmelCase_            =  new_model(UpperCAmelCase__  ,							noise=UpperCAmelCase__  )

              self.assert_outputs_same(UpperCAmelCase__  ,							UpperCAmelCase__  )






  @unittest.skip(
      reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''  )
  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Optional[int]:
        pass






  @unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''  )
  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[str]:
        pass






  @slow
  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        UpperCAmelCase_            =  TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''  )
        self.assertIsNotNone(UpperCAmelCase__  )






def  __lowerCamelCase       (      ):


      '''simple docstring'''


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







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




  '''simple docstring'''



  @cached_property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->Dict:
        return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''  ) if is_vision_available() else None






  @slow
  def  lowerCAmelCase__ (      self			:       Dict  )				->Union[str, Any]:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2  )

        UpperCAmelCase_            =  TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  )

        UpperCAmelCase_            =  self.default_image_processor
        UpperCAmelCase_            =  prepare_img()
        UpperCAmelCase_            =  image_processor(images=UpperCAmelCase__  ,							return_tensors='''tf'''  )

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        UpperCAmelCase_            =  ViTMAEConfig()
        UpperCAmelCase_            =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
        UpperCAmelCase_            =  np.random.uniform(size=(1, num_patches)  )

        # forward pass
        UpperCAmelCase_            =  model(**UpperCAmelCase__  ,							noise=UpperCAmelCase__  )

        # verify the logits
        UpperCAmelCase_            =  tf.convert_to_tensor([1, 196, 768]  )
        self.assertEqual(outputs.logits.shape  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  tf.convert_to_tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

        tf.debugging.assert_near(outputs.logits[0, :3, :3]  ,							UpperCAmelCase__  ,							atol=1e-4  )






'''simple docstring'''
lowercase__  :      Union[str, Any]        =					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__  :      str        =					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase__  :      Any        =					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





