


'''simple docstring'''






from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor




class 		a__    :





       '''simple docstring'''






       def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=3 ,   lowerCamelCase_=32 ,   lowerCamelCase_=3 ,   lowerCamelCase_=10 ,   lowerCamelCase_=[10, 20, 30, 40] ,   lowerCamelCase_=[1, 1, 2, 1] ,   lowerCamelCase_=True ,   lowerCamelCase_=True ,   lowerCamelCase_="relu" ,   lowerCamelCase_=3 ,   lowerCamelCase_=None ,   )	->							Optional[Any]:
              lowerCAmelCase__	       =     parent
              lowerCAmelCase__	       =     batch_size
              lowerCAmelCase__	       =     image_size
              lowerCAmelCase__	       =     num_channels
              lowerCAmelCase__	       =     embeddings_size
              lowerCAmelCase__	       =     hidden_sizes
              lowerCAmelCase__	       =     depths
              lowerCAmelCase__	       =     is_training
              lowerCAmelCase__	       =     use_labels
              lowerCAmelCase__	       =     hidden_act
              lowerCAmelCase__	       =     num_labels
              lowerCAmelCase__	       =     scope
              lowerCAmelCase__	       =     len(_SCREAMING_SNAKE_CASE       )







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
              lowerCAmelCase__	       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

              lowerCAmelCase__	       =     None
              if self.use_labels:
                     lowerCAmelCase__	       =     ids_tensor([self.batch_size] ,   self.num_labels       )

              lowerCAmelCase__	       =     self.get_config()
              return config, pixel_values, labels







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
              return RegNetConfig(
                  num_channels=self.num_channels ,   embeddings_size=self.embeddings_size ,   hidden_sizes=self.hidden_sizes ,   depths=self.depths ,   hidden_act=self.hidden_act ,   num_labels=self.num_labels ,   )







       def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Optional[int]:
              lowerCAmelCase__	       =     TFRegNetModel(config=_SCREAMING_SNAKE_CASE       )
              lowerCAmelCase__	       =     model(_SCREAMING_SNAKE_CASE ,   training=_SCREAMING_SNAKE_CASE       )
              # expected last hidden states: B, C, H // 32, W // 32
              self.parent.assertEqual(
                  result.last_hidden_state.shape ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,   )







       def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
              lowerCAmelCase__	       =     self.num_labels
              lowerCAmelCase__	       =     TFRegNetForImageClassification(_SCREAMING_SNAKE_CASE       )
              lowerCAmelCase__	       =     model(_SCREAMING_SNAKE_CASE ,   labels=_SCREAMING_SNAKE_CASE ,   training=_SCREAMING_SNAKE_CASE       )
              self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)       )







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
              lowerCAmelCase__	       =     self.prepare_config_and_inputs()
              lowerCAmelCase__	       =     config_and_inputs
              lowerCAmelCase__	       =     {"pixel_values": pixel_values}
              return config, inputs_dict




@require_tf
class 		a__    (							SCREAMING_SNAKE_CASE__    ,    SCREAMING_SNAKE_CASE__    ,    unittest.TestCase  ):





       '''simple docstring'''






       lowercase__      :       Optional[Any]     						=				(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
       lowercase__      :       str     						=				(
           {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
           if is_tf_available()
           else {}
       )

       lowercase__      :       Tuple     						=				False
       lowercase__      :       Any     						=				False
       lowercase__      :       str     						=				False
       lowercase__      :       int     						=				False
       lowercase__      :       str     						=				False







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
              lowerCAmelCase__	       =     TFRegNetModelTester(self       )
              lowerCAmelCase__	       =     ConfigTester(self ,   config_class=_SCREAMING_SNAKE_CASE ,   has_text_modality=_SCREAMING_SNAKE_CASE       )







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
              return







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







       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''       )       ) == 0 ,   reason='''TF does not support backprop for grouped convolutions on CPU.''' ,   )
       @slow
       def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
              super().test_keras_fit()







       @unittest.skip(reason='''RegNet does not support input and output embeddings'''       )
       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
              pass







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

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

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







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
              lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE       )







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
              def check_hidden_states_output(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       ):
                     lowerCAmelCase__	       =     model_class(_SCREAMING_SNAKE_CASE       )
                     lowerCAmelCase__	       =     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ) ,   training=_SCREAMING_SNAKE_CASE       )

                     lowerCAmelCase__	       =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                     lowerCAmelCase__	       =     self.model_tester.num_stages
                     self.assertEqual(len(_SCREAMING_SNAKE_CASE       ) ,   expected_num_stages + 1       )

                     # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-2:]       ) ,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,   )

              lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs_for_common()
              lowerCAmelCase__	       =     ["basic", "bottleneck"]
              for model_class in self.all_model_classes:
                     for layer_type in layers_type:
                            lowerCAmelCase__	       =     layer_type
                            lowerCAmelCase__	       =     True
                            check_hidden_states_output(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

                            # check that output_hidden_states also work using config
                            del inputs_dict["output_hidden_states"]
                            lowerCAmelCase__	       =     True

                            check_hidden_states_output(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )







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

              def check_equivalence(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_={}       ):
                     lowerCAmelCase__	       =     model(_SCREAMING_SNAKE_CASE ,   return_dict=_SCREAMING_SNAKE_CASE ,   **_SCREAMING_SNAKE_CASE       )
                     lowerCAmelCase__	       =     model(_SCREAMING_SNAKE_CASE ,   return_dict=_SCREAMING_SNAKE_CASE ,   **_SCREAMING_SNAKE_CASE       ).to_tuple()

                     def recursive_check(lowerCamelCase_ ,   lowerCamelCase_       ):
                            if isinstance(_SCREAMING_SNAKE_CASE ,   (List, Tuple)       ):
                                   for tuple_iterable_value, dict_iterable_value in zip(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
                                          recursive_check(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                            elif tuple_object is None:
                                   return
                            else:
                                   self.assertTrue(
                                       all(tf.equal(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )       ) ,   msg=(
                                           '''Tuple and dict output are not equal. Difference:'''
                                           F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}"""
                                       ) ,   )

                            recursive_check(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

              for model_class in self.all_model_classes:
                     lowerCAmelCase__	       =     model_class(_SCREAMING_SNAKE_CASE       )

                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                     check_equivalence(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   return_labels=_SCREAMING_SNAKE_CASE       )
                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   return_labels=_SCREAMING_SNAKE_CASE       )
                     check_equivalence(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                     check_equivalence(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   {'''output_hidden_states''': True}       )

                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   return_labels=_SCREAMING_SNAKE_CASE       )
                     lowerCAmelCase__	       =     self._prepare_for_class(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   return_labels=_SCREAMING_SNAKE_CASE       )
                     check_equivalence(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   {'''output_hidden_states''': True}       )







       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
              lowerCAmelCase__	       =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE       )







       @slow
       def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
              for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowerCAmelCase__	       =     TFRegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE       )
                     self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )


def  _snake_case       (					)						->   str:
       lowerCAmelCase__	       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
       return image






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





       '''simple docstring'''






       @cached_property
       def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
              return (
                  AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
                  if is_vision_available()
                  else None
              )







       @slow
       def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
              lowerCAmelCase__	       =     TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

              lowerCAmelCase__	       =     self.default_image_processor
              lowerCAmelCase__	       =     prepare_img()
              lowerCAmelCase__	       =     image_processor(images=_SCREAMING_SNAKE_CASE ,   return_tensors='''tf'''       )

              # forward pass
              lowerCAmelCase__	       =     model(**_SCREAMING_SNAKE_CASE ,   training=_SCREAMING_SNAKE_CASE       )

              # verify the logits
              lowerCAmelCase__	       =     tf.TensorShape((1, 10_00)       )
              self.assertEqual(outputs.logits.shape ,   _SCREAMING_SNAKE_CASE       )

              lowerCAmelCase__	       =     tf.constant([-0.4_180, -1.5_051, -3.4_836]       )

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



'''simple docstring'''






from collections.abc import Iterable
from typing import Any




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							List[str]:
														lowerCAmelCase__	       =     value
														lowerCAmelCase__	       =     None  # Added in order to delete a node easier
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     None







							def __repr__(		self       )	->							str:
														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 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     root







							def __str__(		self       )	->							str:
														return str(self.root       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if new_children is not None:  # reset its kids
																					lowerCAmelCase__	       =     node.parent
														if node.parent is not None:  # reset its parent
																					if self.is_right(lowerCamelCase_       ):  # If it is the right children
																												lowerCAmelCase__	       =     new_children
																					else:
																												lowerCAmelCase__	       =     new_children
														else:
																					lowerCAmelCase__	       =     new_children







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							bool:
														if node.parent and node.parent.right:
																					return node == node.parent.right
														return False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							bool:
														return self.root is None







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     Node(lowerCamelCase_       )  # create a new Node
														if self.empty():  # if Tree is empty
																					lowerCAmelCase__	       =     new_node  # set its root
														else:  # Tree is not empty
																					lowerCAmelCase__	       =     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:
																																										lowerCAmelCase__	       =     new_node  # We insert the new node in a leaf
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.left
																												else:
																																			if parent_node.right is None:
																																										lowerCAmelCase__	       =     new_node
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.right
																					lowerCAmelCase__	       =     parent_node







							def   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_       )	->							None:
														for value in values:
																					self.__insert(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Node | None:
														if self.empty():
																					raise IndexError('''Warning: Tree is empty! please use another.'''       )
														else:
																					lowerCAmelCase__	       =     self.root
																					# use lazy evaluation here to avoid NoneType Attribute error
																					while node is not None and node.value is not value:
																												lowerCAmelCase__	       =     node.left if value < node.value else node.right
																					return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					if self.root is None:
																												return None
																					lowerCAmelCase__	       =     self.root

														if not self.empty():
																					while node.right is not None:
																												lowerCAmelCase__	       =     node.right
														return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					lowerCAmelCase__	       =     self.root
														if self.root is None:
																					return None
														if not self.empty():
																					lowerCAmelCase__	       =     self.root
																					while node.left is not None:
																												lowerCAmelCase__	       =     node.left
														return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     self.search(lowerCamelCase_       )  # 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(lowerCamelCase_ ,   lowerCamelCase_       )
																					elif node.left is None:  # Has only right children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.right       )
																					elif node.right is None:  # Has only left children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.left       )
																					else:
																												lowerCAmelCase__	       =     self.get_max(
																												    node.left       )  # Gets the max value of the left branch
																												self.remove(tmp_node.value       )  # type: ignore
																												lowerCAmelCase__	       =     (
																												    tmp_node.value  # type: ignore
																												)  # Assigns the value to the node to delete and keep tree structure







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Iterable:
														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 ,   lowerCamelCase_=None       )	->							Any:
														if traversal_function is None:
																					return self.preorder_traverse(self.root       )
														else:
																					return traversal_function(self.root       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if node:
																					self.inorder(lowerCamelCase_ ,   node.left       )
																					arr.append(node.value       )
																					self.inorder(lowerCamelCase_ ,   node.right       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     []
														self.inorder(lowerCamelCase_ ,   lowerCamelCase_       )  # append all values to list using inorder traversal
														return arr[k - 1]


def  _snake_case       (					A	)						->   list[Node]:
							lowerCAmelCase__	       =     []
							if curr_node is not None:
														lowerCAmelCase__	       =     postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
							return node_list


def  _snake_case       (					)						->   None:
							lowerCAmelCase__	       =     (8, 3, 6, 1, 10, 14, 13, 4, 7)
							lowerCAmelCase__	       =     BinarySearchTree()
							for i in testlist:
														t.insert(A	)

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

							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(A	)
														print(A	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)