





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			:





   def __init__( self					,			snake_case					,			snake_case=13					,			snake_case=30					,			snake_case=2					,			snake_case=3					,			snake_case=True					,			snake_case=True					,			snake_case=32					,			snake_case=2					,			snake_case=4					,			snake_case=37					,			snake_case="gelu"					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=10					,			snake_case=0.02					,			snake_case=3					,			snake_case=0.6					,			snake_case=None					,			)				->  List[str]:
      """simple docstring"""





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

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





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

      a__	:						Dict      		=  self.get_config()

      return config, pixel_values, labels





   def 	_snake_case ( self      )				->  int:
      """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=snake_case					,			initializer_range=self.initializer_range					,			mask_ratio=self.mask_ratio					,			)





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Dict:
      """simple docstring"""





      a__	:						List[str]      		=  TFViTMAEModel(config=snake_case      )
      a__	:						int      		=  model(snake_case					,			training=snake_case      )
      self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.seq_length, self.hidden_size)      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[int]      		=  TFViTMAEForPreTraining(snake_case      )
      a__	:						Dict      		=  model(snake_case					,			training=snake_case      )
      # expected sequence length = num_patches
      a__	:						Union[str, Any]      		=  (self.image_size // self.patch_size) ** 2
      a__	:						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
      a__	:						Any      		=  1
      a__	:						Any      		=  TFViTMAEForPreTraining(snake_case      )

      a__	:						int      		=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
      a__	:						Dict      		=  model(snake_case					,			training=snake_case      )
      a__	:						Dict      		=  self.patch_size**2
      self.parent.assertEqual(result.logits.shape					,			(self.batch_size, num_patches, expected_num_channels)      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





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







@require_tf
class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						List[Any]     		=				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   _UpperCamelCase :						Optional[int]     		=				{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

   _UpperCamelCase :						str     		=				False
   _UpperCamelCase :						Tuple     		=				False
   _UpperCamelCase :						Optional[Any]     		=				False
   _UpperCamelCase :						List[Any]     		=				False





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__	:						List[Any]      		=  TFViTMAEModelTester(self      )
      a__	:						Optional[Any]      		=  ConfigTester(self					,			config_class=snake_case					,			has_text_modality=snake_case					,			hidden_size=37      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      self.config_tester.run_common_tests()





   @unittest.skip(reason="ViTMAE does not use inputs_embeds"      )
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      pass





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						List[str]      		=  self.model_tester.prepare_config_and_inputs_for_common()

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





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						int      		=  self.model_tester.prepare_config_and_inputs_for_common()

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

         a__	:						Any      		=  ["pixel_values"]
         self.assertListEqual(arg_names[:1]					,			snake_case      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Optional[int]      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						Tuple      		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*snake_case      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      np.random.seed(2      )

      a__	:						Any      		=  self.model_tester.prepare_config_and_inputs_for_common()

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

      for model_class in self.all_model_classes:
         a__	:						Optional[Any]      		=  model_class(snake_case      )
         a__	:						Any      		=  self._prepare_for_class(snake_case					,			snake_case      )

         a__	:						Union[str, Any]      		=  model(snake_case					,			noise=snake_case      )

         a__	:						str      		=  copy.deepcopy(self._prepare_for_class(snake_case					,			snake_case      )      )
         a__	:						str      		=  model(**snake_case					,			noise=snake_case      )
         a__	:						List[str]      		=  outputs_dict[0].numpy()
         a__	:						int      		=  outputs_keywords[0].numpy()

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





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      np.random.seed(2      )

      a__	:						List[str]      		=  self.model_tester.prepare_config_and_inputs_for_common()

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

      def prepare_numpy_arrays(snake_case      ):
         a__	:						int      		=  {}
         for k, v in inputs_dict.items():
            if tf.is_tensor(snake_case      ):
               a__	:						Optional[int]      		=  v.numpy()
            else:
               a__	:						str      		=  np.array(snake_case      )

         return inputs_np_dict

      for model_class in self.all_model_classes:
         a__	:						Tuple      		=  model_class(snake_case      )

         a__	:						Dict      		=  self._prepare_for_class(snake_case					,			snake_case      )
         a__	:						Tuple      		=  prepare_numpy_arrays(snake_case      )

         a__	:						str      		=  model(snake_case					,			noise=snake_case      )
         a__	:						Tuple      		=  model(**snake_case					,			noise=snake_case      )
         self.assert_outputs_same(snake_case					,			snake_case      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case      )				->  int:
      """simple docstring"""





      np.random.seed(2      )

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

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

      super().check_pt_tf_models(snake_case					,			snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      np.random.seed(2      )

      a__	:						Tuple      		=  self.model_tester.prepare_config_and_inputs_for_common()

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

      a__	:						Dict      		=  int((config.image_size // config.patch_size) ** 2      )
      a__	:						int      		=  np.random.uniform(size=(self.model_tester.batch_size, num_patches)      )
      a__	:						Tuple      		=  tf.convert_to_tensor(snake_case      )
      inputs_dict.update({"noise": noise}      )

      for main_layer_class in tf_main_layer_classes:
         a__	:						str      		=  main_layer_class(snake_case      )

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

         a__	:						Union[str, Any]      		=  tf.keras.Model(snake_case					,			outputs=main_layer(snake_case      )      )
         a__	:						Optional[int]      		=  model(snake_case      )

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





   @slow
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      np.random.seed(2      )

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

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

      for model_class in self.all_model_classes:
         a__	:						Union[str, Any]      		=  model_class(snake_case      )
         a__	:						str      		=  self._prepare_for_class(snake_case					,			snake_case      )
         a__	:						List[Any]      		=  model(snake_case					,			noise=snake_case      )

         if model_class.__name__ == "TFViTMAEModel":
            a__	:						Tuple      		=  outputs.last_hidden_state.numpy()
            a__	:						Any      		=  0
         else:
            a__	:						Dict      		=  outputs.logits.numpy()
            a__	:						Union[str, Any]      		=  0

         with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(snake_case					,			saved_model=snake_case      )
            a__	:						List[str]      		=  model_class.from_pretrained(snake_case      )
            a__	:						Optional[Any]      		=  model(snake_case					,			noise=snake_case      )

            if model_class.__name__ == "TFViTMAEModel":
               a__	:						int      		=  after_outputs["last_hidden_state"].numpy()
               a__	:						List[Any]      		=  0
            else:
               a__	:						Union[str, Any]      		=  after_outputs["logits"].numpy()
               a__	:						Optional[int]      		=  0

            a__	:						str      		=  np.amax(np.abs(out_a - out_a      )      )
            self.assertLessEqual(snake_case					,			1E-5      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      np.random.seed(2      )

      a__	:						List[Any]      		=  self.model_tester.prepare_config_and_inputs_for_common()

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

      for model_class in self.all_model_classes:
         a__	:						Optional[int]      		=  model_class(snake_case      )
         a__	:						Tuple      		=  self._prepare_for_class(snake_case					,			snake_case      )

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

         self.assert_outputs_same(snake_case					,			snake_case      )





   @unittest.skip(
       reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."      )
   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      pass





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





      pass





   @slow
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						str      		=  TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"      )
      self.assertIsNotNone(snake_case      )







def       _A  (      ):
   a__	:						Optional[Any]      		=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
   return image







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





   @cached_property
   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





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





   @slow
   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      np.random.seed(2      )

      a__	:						Optional[int]      		=  TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"      )

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

      # forward pass
      a__	:						List[str]      		=  model(**snake_case					,			noise=snake_case      )

      # verify the logits
      a__	:						Any      		=  tf.convert_to_tensor([1, 196, 768]      )
      self.assertEqual(outputs.logits.shape					,			snake_case      )

      a__	:						Union[str, Any]      		=  tf.convert_to_tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]      )

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








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

