





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    A__  :





      def __init__(					self			,				A_			,				A_=13			,				A_=30			,				A_=2			,				A_=3			,				A_=True			,				A_=True			,				A_=32			,				A_=2			,				A_=4			,				A_=37			,				A_="gelu"			,				A_=0.1			,				A_=0.1			,				A_=10			,				A_=0.02			,				A_=3			,				A_=0.6			,				A_=None			,				):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  parent
       UpperCamelCase      :   Dict			      =  batch_size
       UpperCamelCase      :   str			      =  image_size
       UpperCamelCase      :   str			      =  patch_size
       UpperCamelCase      :   Optional[Any]			      =  num_channels
       UpperCamelCase      :   Any			      =  is_training
       UpperCamelCase      :   Union[str, Any]			      =  use_labels
       UpperCamelCase      :   List[str]			      =  hidden_size
       UpperCamelCase      :   Dict			      =  num_hidden_layers
       UpperCamelCase      :   str			      =  num_attention_heads
       UpperCamelCase      :   Optional[Any]			      =  intermediate_size
       UpperCamelCase      :   int			      =  hidden_act
       UpperCamelCase      :   Any			      =  hidden_dropout_prob
       UpperCamelCase      :   Any			      =  attention_probs_dropout_prob
       UpperCamelCase      :   List[str]			      =  type_sequence_label_size
       UpperCamelCase      :   Any			      =  initializer_range
       UpperCamelCase      :   List[str]			      =  mask_ratio
       UpperCamelCase      :   int			      =  scope

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       UpperCamelCase      :   int			      =  self.get_config()

       return config, pixel_values, labels





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       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=A_			,				initializer_range=self.initializer_range			,				mask_ratio=self.mask_ratio			,				)





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  TFViTMAEModel(config=A_  )
       UpperCamelCase      :   str			      =  model(A_			,				training=A_  )
       self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  TFViTMAEForPreTraining(A_  )
       UpperCamelCase      :   Any			      =  model(A_			,				training=A_  )
       # expected sequence length = num_patches
       UpperCamelCase      :   str			      =  (self.image_size // self.patch_size) ** 2
       UpperCamelCase      :   List[Any]			      =  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      :   str			      =  1
       UpperCamelCase      :   str			      =  TFViTMAEForPreTraining(A_  )

       UpperCamelCase      :   Optional[int]			      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       UpperCamelCase      :   str			      =  model(A_			,				training=A_  )
       UpperCamelCase      :   Tuple			      =  self.patch_size**2
       self.parent.assertEqual(result.logits.shape			,				(self.batch_size, num_patches, expected_num_channels)  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  self.prepare_config_and_inputs()
       ((UpperCamelCase)      ,       (UpperCamelCase)      ,       (UpperCamelCase))      :   List[str]			      =  config_and_inputs
       UpperCamelCase      :   List[Any]			      =  {"pixel_values": pixel_values}
       return config, inputs_dict



@require_tf
class    A__  (			__snake_case			,  __snake_case			,  unittest.TestCase     ):
      _UpperCAmelCase      :int		       =  (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
      _UpperCAmelCase      :Any		       =  {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

      _UpperCAmelCase      :List[str]		       =  False
      _UpperCAmelCase      :str		       =  False
      _UpperCAmelCase      :Union[str, Any]		       =  False
      _UpperCAmelCase      :Tuple		       =  False





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  TFViTMAEModelTester(self  )
       UpperCamelCase      :   Dict			      =  ConfigTester(self			,				config_class=A_			,				has_text_modality=A_			,				hidden_size=37  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      @unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Any			      =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCamelCase      :   List[Any]			      =  model_class(A_  )
        self.assertIsInstance(model.get_input_embeddings()			,				(tf.keras.layers.Layer)  )
        UpperCamelCase      :   int			      =  model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A_			,				tf.keras.layers.Layer  )  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      :   Union[str, Any]			      =  self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase      :   Optional[Any]			      =  ["pixel_values"]
        self.assertListEqual(arg_names[:1]			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      ,       UpperCamelCase      :   Optional[int]			      =  self.model_tester.prepare_config_and_inputs_for_common()

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

       for model_class in self.all_model_classes:
        UpperCamelCase      :   int			      =  model_class(A_  )
        UpperCamelCase      :   int			      =  self._prepare_for_class(A_			,				A_  )

        UpperCamelCase      :   List[Any]			      =  model(A_			,				noise=A_  )

        UpperCamelCase      :   List[str]			      =  copy.deepcopy(self._prepare_for_class(A_			,				A_  )  )
        UpperCamelCase      :   List[Any]			      =  model(**A_			,				noise=A_  )
        UpperCamelCase      :   Dict			      =  outputs_dict[0].numpy()
        UpperCamelCase      :   int			      =  outputs_keywords[0].numpy()

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      ,       UpperCamelCase      :   int			      =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

        return inputs_np_dict

       for model_class in self.all_model_classes:
        UpperCamelCase      :   Tuple			      =  model_class(A_  )

        UpperCamelCase      :   int			      =  self._prepare_for_class(A_			,				A_  )
        UpperCamelCase      :   List[Any]			      =  prepare_numpy_arrays(A_  )

        UpperCamelCase      :   Optional[Any]			      =  model(A_			,				noise=A_  )
        UpperCamelCase      :   List[str]			      =  model(**A_			,				noise=A_  )
        self.assert_outputs_same(A_			,				A_  )





      def 			__UpperCamelCase(					self			,				A_			,				A_			,				A_  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      :   List[str]			      =  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
       UpperCamelCase      :   Tuple			      =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
       UpperCamelCase      :   Any			      =  tf.constant(A_  )

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

       super().check_pt_tf_models(A_			,				A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      ,       UpperCamelCase      :   int			      =  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase      :   Tuple			      =  {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__  ),)
           for module_member_name in dir(A_  )
           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(A_			,				A_  ),)
           if isinstance(A_			,				A_  )
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(A_			,				"_keras_serializable"			,				A_  )
       }

       UpperCamelCase      :   Dict			      =  int((config.image_size // config.patch_size) ** 2  )
       UpperCamelCase      :   Optional[Any]			      =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
       UpperCamelCase      :   List[str]			      =  tf.convert_to_tensor(A_  )
       inputs_dict.update({"noise": noise}  )

       for main_layer_class in tf_main_layer_classes:
        UpperCamelCase      :   str			      =  main_layer_class(A_  )

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

        UpperCamelCase      :   List[Any]			      =  tf.keras.Model(A_			,				outputs=main_layer(A_  )  )
        UpperCamelCase      :   Tuple			      =  model(A_  )

        with tempfile.TemporaryDirectory() as tmpdirname:
         UpperCamelCase      :   int			      =  os.path.join(A_			,				"keras_model.h5"  )
         model.save(A_  )
         UpperCamelCase      :   int			      =  tf.keras.models.load_model(
             A_			,				custom_objects={main_layer_class.__name__: main_layer_class}  )
         assert isinstance(A_			,				tf.keras.Model  )
         UpperCamelCase      :   List[Any]			      =  model(A_  )
         self.assert_outputs_same(A_			,				A_  )





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      ,       UpperCamelCase      :   Union[str, Any]			      =  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase      :   List[str]			      =  int((config.image_size // config.patch_size) ** 2  )
       UpperCamelCase      :   Optional[int]			      =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

       for model_class in self.all_model_classes:
        UpperCamelCase      :   Union[str, Any]			      =  model_class(A_  )
        UpperCamelCase      :   Union[str, Any]			      =  self._prepare_for_class(A_			,				A_  )
        UpperCamelCase      :   int			      =  model(A_			,				noise=A_  )

        if model_class.__name__ == "TFViTMAEModel":
         UpperCamelCase      :   Optional[int]			      =  outputs.last_hidden_state.numpy()
         UpperCamelCase      :   Dict			      =  0
        else:
         UpperCamelCase      :   str			      =  outputs.logits.numpy()
         UpperCamelCase      :   Dict			      =  0

        with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(A_			,				saved_model=A_  )
         UpperCamelCase      :   List[str]			      =  model_class.from_pretrained(A_  )
         UpperCamelCase      :   Union[str, Any]			      =  model(A_			,				noise=A_  )

         if model_class.__name__ == "TFViTMAEModel":
          UpperCamelCase      :   int			      =  after_outputs["last_hidden_state"].numpy()
          UpperCamelCase      :   Dict			      =  0
         else:
          UpperCamelCase      :   str			      =  after_outputs["logits"].numpy()
          UpperCamelCase      :   Optional[int]			      =  0

         UpperCamelCase      :   Any			      =  np.amax(np.abs(out_a - out_a  )  )
         self.assertLessEqual(A_			,				1e-5  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      ,       UpperCamelCase      :   Optional[Any]			      =  self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase      :   Union[str, Any]			      =  int((config.image_size // config.patch_size) ** 2  )
       UpperCamelCase      :   Optional[Any]			      =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

       for model_class in self.all_model_classes:
        UpperCamelCase      :   Dict			      =  model_class(A_  )
        UpperCamelCase      :   str			      =  self._prepare_for_class(A_			,				A_  )

        UpperCamelCase      :   Dict			      =  model(A_			,				noise=A_  )
        UpperCamelCase      :   Optional[int]			      =  model.get_config()
        # make sure that returned config is jsonifiable, which is required by keras
        json.dumps(A_  )
        UpperCamelCase      :   Any			      =  model_class.from_config(model.get_config()  )
        # make sure it also accepts a normal config
        UpperCamelCase      :   List[str]			      =  model_class.from_config(model.config  )
        UpperCamelCase      :   Optional[int]			      =  new_model(A_  )  # Build model
        new_model.set_weights(model.get_weights()  )
        UpperCamelCase      :   Dict			      =  new_model(A_			,				noise=A_  )

        self.assert_outputs_same(A_			,				A_  )





      @unittest.skip(
          reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"  )
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       pass





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"  )
       self.assertIsNotNone(A_  )



def 						A_							(	)		->					List[Any]:
 UpperCamelCase      :   int			      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
 return image



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





      @cached_property
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"  ) if is_vision_available() else None





      @slow
      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       np.random.seed(2  )

       UpperCamelCase      :   List[str]			      =  TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  )

       UpperCamelCase      :   Union[str, Any]			      =  self.default_image_processor
       UpperCamelCase      :   str			      =  prepare_img()
       UpperCamelCase      :   Any			      =  image_processor(images=A_			,				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      :   Union[str, Any]			      =  ViTMAEConfig()
       UpperCamelCase      :   Optional[Any]			      =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
       UpperCamelCase      :   List[str]			      =  np.random.uniform(size=(1, num_patches)  )

       # forward pass
       UpperCamelCase      :   List[Any]			      =  model(**A_			,				noise=A_  )

       # verify the logits
       UpperCamelCase      :   Union[str, Any]			      =  tf.convert_to_tensor([1, 196, 768]  )
       self.assertEqual(outputs.logits.shape			,				A_  )

       UpperCamelCase      :   Tuple			      =  tf.convert_to_tensor(
           [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]  )

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







__lowerCamelCase			:	str					    =	6_5521



def 						A_							(	_lowerCAmelCase	)		->					int:
 UpperCamelCase      :   Any			      =  1
 UpperCamelCase      :   str			      =  0
 for plain_chr in plain_text:
  UpperCamelCase      :   List[Any]			      =  (a + ord(_lowerCAmelCase	)) % MOD_ADLER
  UpperCamelCase      :   List[Any]			      =  (b + a) % MOD_ADLER
 return (b << 16) | a
