
"""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  ,     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__=None  ,     snake_case__=2  ,     ):




       """simple docstring"""


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

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       _SCREAMING_SNAKE_CASE   :				Any							    = (image_size // patch_size) ** 2
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = num_patches + 2
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

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

       _SCREAMING_SNAKE_CASE   :				Any							    = self.get_config()

       return config, pixel_values, labels
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """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=snake_case__  ,     initializer_range=self.initializer_range  ,     encoder_stride=self.encoder_stride  ,     )
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = TFDeiTModel(config=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = model(snake_case__				)
       self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = TFDeiTForMaskedImageModeling(config=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = model(snake_case__				)
       self.parent.assertEqual(
           result.reconstruction.shape  ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

       # test greyscale images
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 1
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = TFDeiTForMaskedImageModeling(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Any							    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       _SCREAMING_SNAKE_CASE   :				Any							    = model(snake_case__				)
       self.parent.assertEqual(result.reconstruction.shape  ,     (self.batch_size, 1, self.image_size, self.image_size)				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.type_sequence_label_size
       _SCREAMING_SNAKE_CASE   :				Dict							    = TFDeiTForImageClassification(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = model(snake_case__  ,     labels=snake_case__				)
       self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)				)

       # test greyscale images
       _SCREAMING_SNAKE_CASE   :				Tuple							    = 1
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = TFDeiTForImageClassification(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = model(snake_case__  ,     labels=snake_case__				)
       self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.prepare_config_and_inputs()
       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = config_and_inputs
       _SCREAMING_SNAKE_CASE   :				List[str]							    = {"pixel_values": pixel_values}
       return config, inputs_dict
@require_tf
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			(
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     A__      =			(
         {
             """feature-extraction""": TFDeiTModel,
             """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

     A__      =			False
     A__      =			False
     A__      =			False
     A__      =			False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = TFDeiTModelTester(self				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = ConfigTester(self  ,     config_class=snake_case__  ,     has_text_modality=snake_case__  ,     hidden_size=37				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       self.config_tester.run_common_tests()
     @unittest.skip(reason="DeiT does not use inputs_embeds"				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       pass
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

       for model_class in self.all_model_classes:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model_class(snake_case__				)
         self.assertIsInstance(model.get_input_embeddings()  ,     (tf.keras.layers.Layer)				)
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(snake_case__  ,     tf.keras.layers.Dense				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

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

         _SCREAMING_SNAKE_CASE   :				Tuple							    = ["pixel_values"]
         self.assertListEqual(arg_names[:1]  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__=False				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = super()._prepare_for_class(snake_case__  ,     snake_case__  ,     return_labels=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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
  return image





@require_tf
@require_vision
class 			UpperCamelCase      (							unittest.TestCase  ):
     @cached_property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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




     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				)

       _SCREAMING_SNAKE_CASE   :				str							    = self.default_image_processor
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = prepare_img()
       _SCREAMING_SNAKE_CASE   :				Dict							    = image_processor(images=snake_case__  ,     return_tensors="tf"				)

       # forward pass
       _SCREAMING_SNAKE_CASE   :				int							    = model(**snake_case__				)

       # verify the logits
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tf.TensorShape((1, 1000)				)
       self.assertEqual(outputs.logits.shape  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.constant([-1.0_266, 0.1_912, -1.2_861]				)

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





"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = value
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None  # Added in order to delete a node easier
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None




     def __repr__(     self				):




       """simple docstring"""


       from pprint import pformat

       if self.left is None and self.right is None:
         return str(self.value				)
       return pformat({F'''{self.value}''': (self.left, self.right)}  ,     indent=1				)
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = root
     def __str__(     self				):




       """simple docstring"""


       return str(self.root				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if new_children is not None:  # reset its kids
         _SCREAMING_SNAKE_CASE   :				Dict							    = node.parent
       if node.parent is not None:  # reset its parent
         if self.is_right(snake_case__				):  # If it is the right children
           _SCREAMING_SNAKE_CASE   :				Any							    = new_children
         else:
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_children
       else:
         _SCREAMING_SNAKE_CASE   :				Any							    = new_children
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if node.parent and node.parent.right:
         return node == node.parent.right
       return False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.root is None
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Node(snake_case__				)  # create a new Node
       if self.empty():  # if Tree is empty
         _SCREAMING_SNAKE_CASE   :				str							    = new_node  # set its root
       else:  # Tree is not empty
         _SCREAMING_SNAKE_CASE   :				Dict							    = self.root  # from root
         if parent_node is None:
           return
         while True:  # While we don't get to a leaf
           if value < parent_node.value:  # We go left
             if parent_node.left is None:
               _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_node  # We insert the new node in a leaf
               break
             else:
               _SCREAMING_SNAKE_CASE   :				int							    = parent_node.left
           else:
             if parent_node.right is None:
               _SCREAMING_SNAKE_CASE   :				str							    = new_node
               break
             else:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = parent_node.right
         _SCREAMING_SNAKE_CASE   :				Any							    = parent_node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__				):




       """simple docstring"""


       for value in values:
         self.__insert(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.empty():
         raise IndexError("Warning: Tree is empty! please use another."				)
       else:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.root
         # use lazy evaluation here to avoid NoneType Attribute error
         while node is not None and node.value is not value:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = node.left if value < node.value else node.right
         return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         if self.root is None:
           return None
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.root

       if not self.empty():
         while node.right is not None:
           _SCREAMING_SNAKE_CASE   :				Dict							    = node.right
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.root
       if self.root is None:
         return None
       if not self.empty():
         _SCREAMING_SNAKE_CASE   :				Any							    = self.root
         while node.left is not None:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = node.left
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.search(snake_case__				)  # Look for the node with that label
       if node is not None:
         if node.left is None and node.right is None:  # If it has no children
           self.__reassign_nodes(snake_case__  ,     snake_case__				)
         elif node.left is None:  # Has only right children
           self.__reassign_nodes(snake_case__  ,     node.right				)
         elif node.right is None:  # Has only left children
           self.__reassign_nodes(snake_case__  ,     node.left				)
         else:
           _SCREAMING_SNAKE_CASE   :				Dict							    = self.get_max(
               node.left				)  # Gets the max value of the left branch
           self.remove(tmp_node.value				)  # type: ignore
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = (
               tmp_node.value  # type: ignore
           )  # Assigns the value to the node to delete and keep tree structure
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if node is not None:
         yield node  # Preorder Traversal
         yield from self.preorder_traverse(node.left				)
         yield from self.preorder_traverse(node.right				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None				):




       """simple docstring"""


       if traversal_function is None:
         return self.preorder_traverse(self.root				)
       else:
         return traversal_function(self.root				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if node:
         self.inorder(snake_case__  ,     node.left				)
         arr.append(node.value				)
         self.inorder(snake_case__  ,     node.right				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				list[int]							    = []
       self.inorder(snake_case__  ,     snake_case__				)  # append all values to list using inorder traversal
       return arr[k - 1]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Node | None				)  ->    list[Node]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  if curr_node is not None:
    _SCREAMING_SNAKE_CASE   :				int							    = postorder(curr_node.left				) + postorder(curr_node.right				) + [curr_node]
  return node_list





def    _lowerCAmelCase    (     )  ->    None:
  _SCREAMING_SNAKE_CASE   :				Any							    = (8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = BinarySearchTree()
  for i in testlist:
    t.insert(lowerCamelCase__				)

  # Prints all the elements of the list in order traversal
  print(lowerCamelCase__				)

  if t.search(6				) is not None:
    print("The value 6 exists"				)
  else:
    print("The value 6 doesn't exist"				)

  if t.search(-1				) is not None:
    print("The value -1 exists"				)
  else:
    print("The value -1 doesn't exist"				)

  if not t.empty():
    print("Max Value: ",						t.get_max().value				)  # type: ignore
    print("Min Value: ",						t.get_min().value				)  # type: ignore

  for i in testlist:
    t.remove(lowerCamelCase__				)
    print(lowerCamelCase__				)


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



