





import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
			from transformers import (
			    FlaxBertModel,
			    FlaxCLIPVisionModel,
			    FlaxVisionTextDualEncoderModel,
			    FlaxViTModel,
			    VisionTextDualEncoderConfig,
			    VisionTextDualEncoderProcessor,
			)
			from transformers.modeling_flax_pytorch_utils import (
			    convert_pytorch_state_dict_to_flax,
			    load_flax_weights_in_pytorch_model,
			)


if is_torch_available():
			import torch

			from transformers import VisionTextDualEncoderModel

if is_vision_available():
			from PIL import Image


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]			)   ->							Dict:




		'''simple docstring'''
		if isinstance(_lowercase   ,    collections.abc.Iterable			):
				return x
		return (x, x)


@require_flax
class       __lowerCAmelCase					:


	'''simple docstring'''





	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						int,					lowerCamelCase_:						Tuple			):
			pass

	def    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						Optional[int]			):
			pass

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						np.ndarray,					lowerCamelCase_:						np.ndarray,					lowerCamelCase_:						float			):
			lowercase__					:      int      =		np.abs((a - b)			).max()
			self.assertLessEqual(lowerCamelCase_,					lowerCamelCase_,					F"""Difference between torch and flax is {diff} (>= {tol})."""			)

	def    snake_case__(						self:						int,					lowerCamelCase_:						int,					lowerCamelCase_:						int,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						int=None,					**lowerCamelCase_:						List[str]			):
			lowercase__					:      Optional[Any]      =		VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase_,					lowerCamelCase_			)

			lowercase__					:      Optional[Any]      =		FlaxVisionTextDualEncoderModel(lowerCamelCase_			)

			lowercase__					:      List[Any]      =		model(input_ids=lowerCamelCase_,					pixel_values=lowerCamelCase_,					attention_mask=lowerCamelCase_			)

			self.assertEqual(output['text_embeds'].shape,					(input_ids.shape[0], config.projection_dim)			)
			self.assertEqual(output['image_embeds'].shape,					(pixel_values.shape[0], config.projection_dim)			)

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						str,					lowerCamelCase_:						List[str],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Optional[Any]=None,					**lowerCamelCase_:						int			):
			lowercase__   ,				lowercase__					:      Optional[Any]      =		self.get_vision_text_model(lowerCamelCase_,					lowerCamelCase_			)
			lowercase__					:      Union[str, Any]      =		{'vision_model': vision_model, 'text_model': text_model}
			lowercase__					:      Optional[Any]      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase_			)

			lowercase__					:      Any      =		model(input_ids=lowerCamelCase_,					pixel_values=lowerCamelCase_,					attention_mask=lowerCamelCase_			)

			self.assertEqual(output['text_embeds'].shape,					(input_ids.shape[0], model.config.projection_dim)			)
			self.assertEqual(output['image_embeds'].shape,					(pixel_values.shape[0], model.config.projection_dim)			)

	def    snake_case__(						self:						List[str],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Any,					lowerCamelCase_:						Optional[Any]=None,					**lowerCamelCase_:						Tuple			):
			lowercase__   ,				lowercase__					:      Dict      =		self.get_vision_text_model(lowerCamelCase_,					lowerCamelCase_			)
			lowercase__					:      str      =		{'vision_model': vision_model, 'text_model': text_model}
			lowercase__					:      Optional[Any]      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase_			)

			lowercase__					:      int      =		model(input_ids=lowerCamelCase_,					pixel_values=lowerCamelCase_,					attention_mask=lowerCamelCase_			)
			lowercase__					:      Optional[int]      =		output[0]

			with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_pretrained(lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase_			)

					lowercase__					:      List[str]      =		model(input_ids=lowerCamelCase_,					pixel_values=lowerCamelCase_,					attention_mask=lowerCamelCase_			)
					lowercase__					:      int      =		after_output[0]
					lowercase__					:      Optional[int]      =		np.amax(np.abs(out_a - out_a			)			)
					self.assertLessEqual(lowerCamelCase_,					1E-3			)

	def    snake_case__(						self:						str,					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Dict,					lowerCamelCase_:						Union[str, Any]=None,					**lowerCamelCase_:						List[str]			):
			lowercase__   ,				lowercase__					:      Optional[int]      =		self.get_vision_text_model(lowerCamelCase_,					lowerCamelCase_			)
			lowercase__					:      Dict      =		{'vision_model': vision_model, 'text_model': text_model}
			lowercase__					:      int      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase_			)

			lowercase__					:      Optional[Any]      =		model(
			    input_ids=lowerCamelCase_,					pixel_values=lowerCamelCase_,					attention_mask=lowerCamelCase_,					output_attentions=lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		output.vision_model_output.attentions
			self.assertEqual(len(lowerCamelCase_			),					vision_config.num_hidden_layers			)

			# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
			lowercase__					:      Optional[int]      =		to_atuple(vision_model.config.image_size			)
			lowercase__					:      str      =		to_atuple(vision_model.config.patch_size			)
			lowercase__					:      List[str]      =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
			lowercase__					:      Optional[Any]      =		num_patches + 1
			self.assertEqual(vision_attentions[0].shape[-3:],					(vision_config.num_attention_heads, seq_len, seq_len)			)

			lowercase__					:      str      =		output.text_model_output.attentions
			self.assertEqual(len(lowerCamelCase_			),					text_config.num_hidden_layers			)

			self.assertEqual(
			    text_attentions[0].shape[-3:],					(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),					)

	def    snake_case__(						self:						int,					lowerCamelCase_:						int,					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						List[Any]			):
			pt_model.to(lowerCamelCase_			)
			pt_model.eval()

			# prepare inputs
			lowercase__					:      List[str]      =		inputs_dict
			lowercase__					:      Optional[Any]      =		{k: torch.tensor(v.tolist()			) for k, v in flax_inputs.items()}

			with torch.no_grad():
					lowercase__					:      List[str]      =		pt_model(**lowerCamelCase_			).to_tuple()

			lowercase__					:      Tuple      =		fx_model(**lowerCamelCase_			).to_tuple()
			self.assertEqual(len(lowerCamelCase_			),					len(lowerCamelCase_			),					'Output lengths differ between Flax and PyTorch'			)
			for fx_output, pt_output in zip(fx_outputs[:4],					pt_outputs[:4]			):
					self.assert_almost_equals(lowerCamelCase_,					pt_output.numpy(),					4E-2			)

			# PT -> Flax
			with tempfile.TemporaryDirectory() as tmpdirname:
					pt_model.save_pretrained(lowerCamelCase_			)
					lowercase__					:      Dict      =		FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase_,					from_pt=lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		fx_model_loaded(**lowerCamelCase_			).to_tuple()
			self.assertEqual(len(lowerCamelCase_			),					len(lowerCamelCase_			),					'Output lengths differ between Flax and PyTorch'			)
			for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4],					pt_outputs[:4]			):
					self.assert_almost_equals(lowerCamelCase_,					pt_output.numpy(),					4E-2			)

			# Flax -> PT
			with tempfile.TemporaryDirectory() as tmpdirname:
					fx_model.save_pretrained(lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		VisionTextDualEncoderModel.from_pretrained(lowerCamelCase_,					from_flax=lowerCamelCase_			)

			pt_model_loaded.to(lowerCamelCase_			)
			pt_model_loaded.eval()

			with torch.no_grad():
					lowercase__					:      Dict      =		pt_model_loaded(**lowerCamelCase_			).to_tuple()

			self.assertEqual(len(lowerCamelCase_			),					len(lowerCamelCase_			),					'Output lengths differ between Flax and PyTorch'			)
			for fx_output, pt_output_loaded in zip(fx_outputs[:4],					pt_outputs_loaded[:4]			):
					self.assert_almost_equals(lowerCamelCase_,					pt_output_loaded.numpy(),					4E-2			)

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      str      =		VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase_,					lowerCamelCase_			)

			lowercase__					:      Optional[Any]      =		VisionTextDualEncoderModel(lowerCamelCase_			)
			lowercase__					:      Any      =		FlaxVisionTextDualEncoderModel(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		convert_pytorch_state_dict_to_flax(pt_model.state_dict(),					lowerCamelCase_			)
			lowercase__					:      Tuple      =		fx_state

			self.check_pt_flax_equivalence(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						Dict,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						str,					lowerCamelCase_:						List[str]			):
			lowercase__					:      Union[str, Any]      =		VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase_,					lowerCamelCase_			)

			lowercase__					:      Optional[int]      =		VisionTextDualEncoderModel(lowerCamelCase_			)
			lowercase__					:      Dict      =		FlaxVisionTextDualEncoderModel(lowerCamelCase_			)

			lowercase__					:      str      =		load_flax_weights_in_pytorch_model(lowerCamelCase_,					fx_model.params			)

			self.check_pt_flax_equivalence(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      Dict      =		self.prepare_config_and_inputs()
			self.check_model_from_pretrained_configs(**lowerCamelCase_			)

	def    snake_case__(						self:						int			):
			lowercase__					:      Dict      =		self.prepare_config_and_inputs()
			self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Dict      =		self.prepare_config_and_inputs()
			self.check_save_load(**lowerCamelCase_			)

	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      Optional[int]      =		self.prepare_config_and_inputs()
			self.check_vision_text_output_attention(**lowerCamelCase_			)

	@is_pt_flax_cross_test
	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[Any]      =		self.prepare_config_and_inputs()
			lowercase__					:      List[Any]      =		config_inputs_dict.pop('vision_config'			)
			lowercase__					:      List[str]      =		config_inputs_dict.pop('text_config'			)

			lowercase__					:      Any      =		config_inputs_dict

			self.check_equivalence_pt_to_flax(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)
			self.check_equivalence_flax_to_pt(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)





	@slow
	def    snake_case__(						self:						Tuple			):
			lowercase__   ,				lowercase__					:      Optional[int]      =		self.get_pretrained_model_and_inputs()

			lowercase__					:      str      =		model_a(**lowerCamelCase_			)
			lowercase__					:      Union[str, Any]      =		outputs[0]

			with tempfile.TemporaryDirectory() as tmp_dirname:
					model_a.save_pretrained(lowerCamelCase_			)
					lowercase__					:      Optional[int]      =		FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase_			)

					lowercase__					:      Any      =		model_a(**lowerCamelCase_			)
					lowercase__					:      Dict      =		after_outputs[0]
					lowercase__					:      Any      =		np.amax(np.abs(out_a - out_a			)			)
					self.assertLessEqual(lowerCamelCase_,					1E-5			)


@require_flax
class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Tuple			):
			lowercase__					:      str      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
			    'hf-internal-testing/tiny-random-vit',					'hf-internal-testing/tiny-bert',					vision_from_pt=lowerCamelCase_,					text_from_pt=lowerCamelCase_,					)
			lowercase__					:      str      =		13
			lowercase__					:      Optional[Any]      =		floats_tensor(
			    [
			        batch_size,
			        model.config.vision_config.num_channels,
			        model.config.vision_config.image_size,
			        model.config.vision_config.image_size,
			    ]			)
			lowercase__					:      List[str]      =		ids_tensor([batch_size, 4],					model.config.text_config.vocab_size			)
			lowercase__					:      Any      =		random_attention_mask([batch_size, 4]			)
			lowercase__					:      str      =		{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

			return model, inputs

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						str,					lowerCamelCase_:						Dict			):
			lowercase__					:      Optional[int]      =		FlaxViTModel(lowerCamelCase_			)
			lowercase__					:      Tuple      =		FlaxBertModel(lowerCamelCase_			)
			return vision_model, text_model





	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[Any]      =		FlaxViTModelTester(self			)
			lowercase__					:      List[str]      =		FlaxBertModelTester(self			)
			lowercase__					:      List[str]      =		vit_model_tester.prepare_config_and_inputs()
			lowercase__					:      Dict      =		bert_model_tester.prepare_config_and_inputs()

			lowercase__   ,				lowercase__					:      List[Any]      =		vision_config_and_inputs

			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		text_config_and_inputs

			# make sure that cross attention layers are added
			return {
			    "text_config": text_config,
			    "vision_config": vision_config,
			    "pixel_values": pixel_values,
			    "attention_mask": attention_mask,
			    "input_ids": input_ids,
			    "token_type_ids": token_type_ids,
			}


@require_torch
class       __lowerCAmelCase					(       _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						Tuple			):
			lowercase__					:      int      =		FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
			    'hf-internal-testing/tiny-random-clip',					'hf-internal-testing/tiny-bert',					vision_from_pt=lowerCamelCase_,					text_from_pt=lowerCamelCase_,					)
			lowercase__					:      str      =		13
			lowercase__					:      Union[str, Any]      =		floats_tensor(
			    [
			        batch_size,
			        model.config.vision_config.num_channels,
			        model.config.vision_config.image_size,
			        model.config.vision_config.image_size,
			    ]			)
			lowercase__					:      Dict      =		ids_tensor([batch_size, 4],					model.config.text_config.vocab_size			)
			lowercase__					:      Optional[Any]      =		random_attention_mask([batch_size, 4]			)
			lowercase__					:      int      =		{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

			return model, inputs

	def    snake_case__(						self:						Dict,					lowerCamelCase_:						str,					lowerCamelCase_:						str			):
			lowercase__					:      Optional[int]      =		FlaxCLIPVisionModel(lowerCamelCase_			)
			lowercase__					:      List[str]      =		FlaxBertModel(lowerCamelCase_			)
			return vision_model, text_model





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      List[Any]      =		FlaxCLIPVisionModelTester(self			)
			lowercase__					:      Dict      =		FlaxBertModelTester(self			)
			lowercase__					:      Optional[int]      =		clip_model_tester.prepare_config_and_inputs()
			lowercase__					:      Tuple      =		bert_model_tester.prepare_config_and_inputs()

			lowercase__   ,				lowercase__					:      Optional[int]      =		vision_config_and_inputs

			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Tuple      =		text_config_and_inputs

			# make sure that cross attention layers are added
			return {
			    "text_config": text_config,
			    "vision_config": vision_config,
			    "pixel_values": pixel_values,
			    "attention_mask": attention_mask,
			    "input_ids": input_ids,
			    "token_type_ids": token_type_ids,
			}


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


	'''simple docstring'''





	@slow
	def    snake_case__(						self:						Dict			):
			lowercase__					:      List[Any]      =		FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian',					logit_scale_init_value=1.0			)
			lowercase__					:      Tuple      =		VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'			)

			lowercase__					:      Tuple      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			lowercase__					:      Dict      =		processor(
			    text=['una foto di un gatto', 'una foto di un cane'],					images=lowerCamelCase_,					padding=lowerCamelCase_,					return_tensors='np'			)

			lowercase__					:      Union[str, Any]      =		model(**lowerCamelCase_			)

			# verify the logits
			self.assertEqual(outputs.logits_per_image.shape,					(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])			)
			self.assertEqual(
			    outputs.logits_per_text.shape,					(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),					)

			lowercase__					:      List[Any]      =		np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]			)

			self.assertTrue(np.allclose(outputs.logits_per_image,					lowerCamelCase_,					atol=1E-3			)			)









from __future__ import annotations

import math
import random
from typing import Any


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						Dict			):
			lowercase__					:      list[Any]      =		[]
			lowercase__					:      int      =		0
			lowercase__					:      int      =		0

	def    snake_case__(						self:						Optional[int]			):
			return self.head == self.tail

	def    snake_case__(						self:						Any,					lowerCamelCase_:						Any			):
			self.data.append(lowerCamelCase_			)
			lowercase__					:      int      =		self.tail + 1

	def    snake_case__(						self:						Tuple			):
			lowercase__					:      Optional[Any]      =		self.data[self.head]
			lowercase__					:      Optional[int]      =		self.head + 1
			return ret

	def    snake_case__(						self:						List[Any]			):
			return self.tail - self.head





	def    snake_case__(						self:						List[Any]			):
			print(self.data			)
			print('**************'			)
			print(self.data[self.head : self.tail]			)


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						Any			):
			lowercase__					:      List[Any]      =		data
			lowercase__					:      MyNode | None      =		None
			lowercase__					:      MyNode | None      =		None
			lowercase__					:      int      =		1

	def    snake_case__(						self:						Optional[Any]			):
			return self.data

	def    snake_case__(						self:						List[str]			):
			return self.left

	def    snake_case__(						self:						Optional[int]			):
			return self.right

	def    snake_case__(						self:						Optional[Any]			):
			return self.height

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		data

	def    snake_case__(						self:						Optional[int],					lowerCamelCase_:						MyNode | None			):
			lowercase__					:      Dict      =		node

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						MyNode | None			):
			lowercase__					:      Optional[Any]      =		node





	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						int			):
			lowercase__					:      Union[str, Any]      =		height


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode | None			)   ->							int:




		'''simple docstring'''
		if node is None:
				return 0
		return node.get_height()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							int:




		'''simple docstring'''
		if a > b:
				return a
		return b


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		print('left rotation node:'   ,    node.get_data()			)
		lowercase__					:      Optional[int]      =		node.get_left()
		assert ret is not None
		node.set_left(ret.get_right()			)
		ret.set_right(_lowercase			)
		lowercase__					:      List[str]      =		my_max(get_height(node.get_right()			)   ,    get_height(node.get_left()			)			) + 1
		node.set_height(_lowercase			)
		lowercase__					:      Optional[Any]      =		my_max(get_height(ret.get_right()			)   ,    get_height(ret.get_left()			)			) + 1
		ret.set_height(_lowercase			)
		return ret


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		print('right rotation node:'   ,    node.get_data()			)
		lowercase__					:      Union[str, Any]      =		node.get_right()
		assert ret is not None
		node.set_right(ret.get_left()			)
		ret.set_left(_lowercase			)
		lowercase__					:      str      =		my_max(get_height(node.get_right()			)   ,    get_height(node.get_left()			)			) + 1
		node.set_height(_lowercase			)
		lowercase__					:      List[str]      =		my_max(get_height(ret.get_right()			)   ,    get_height(ret.get_left()			)			) + 1
		ret.set_height(_lowercase			)
		return ret


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		lowercase__					:      str      =		node.get_left()
		assert left_child is not None
		node.set_left(left_rotation(_lowercase			)			)
		return right_rotation(_lowercase			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							MyNode:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		node.get_right()
		assert right_child is not None
		node.set_right(right_rotation(_lowercase			)			)
		return left_rotation(_lowercase			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode | None   ,    _lowercase     : Any			)   ->							MyNode | None:




		'''simple docstring'''
		if node is None:
				return MyNode(_lowercase			)
		if data < node.get_data():
				node.set_left(insert_node(node.get_left()   ,    _lowercase			)			)
				if (
				    get_height(node.get_left()			) - get_height(node.get_right()			) == 2
				):  # an unbalance detected
						lowercase__					:      Tuple      =		node.get_left()
						assert left_child is not None
						if (
						    data < left_child.get_data()
						):  # new node is the left child of the left child
								lowercase__					:      Optional[Any]      =		right_rotation(_lowercase			)
						else:
								lowercase__					:      List[str]      =		lr_rotation(_lowercase			)
		else:
				node.set_right(insert_node(node.get_right()   ,    _lowercase			)			)
				if get_height(node.get_right()			) - get_height(node.get_left()			) == 2:
						lowercase__					:      Dict      =		node.get_right()
						assert right_child is not None
						if data < right_child.get_data():
								lowercase__					:      Dict      =		rl_rotation(_lowercase			)
						else:
								lowercase__					:      Tuple      =		left_rotation(_lowercase			)
		lowercase__					:      List[Any]      =		my_max(get_height(node.get_right()			)   ,    get_height(node.get_left()			)			) + 1
		node.set_height(_lowercase			)
		return node


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							Any:




		'''simple docstring'''
		while True:
				lowercase__					:      List[Any]      =		root.get_right()
				if right_child is None:
						break
				lowercase__					:      List[str]      =		right_child
		return root.get_data()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode			)   ->							Any:




		'''simple docstring'''
		while True:
				lowercase__					:      Any      =		root.get_left()
				if left_child is None:
						break
				lowercase__					:      List[str]      =		left_child
		return root.get_data()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : MyNode   ,    _lowercase     : Any			)   ->							MyNode | None:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		root.get_left()
		lowercase__					:      List[Any]      =		root.get_right()
		if root.get_data() == data:
				if left_child is not None and right_child is not None:
						lowercase__					:      Dict      =		get_left_most(_lowercase			)
						root.set_data(_lowercase			)
						root.set_right(del_node(_lowercase   ,    _lowercase			)			)
				elif left_child is not None:
						lowercase__					:      List[Any]      =		left_child
				elif right_child is not None:
						lowercase__					:      Dict      =		right_child
				else:
						return None
		elif root.get_data() > data:
				if left_child is None:
						print('No such data'			)
						return root
				else:
						root.set_left(del_node(_lowercase   ,    _lowercase			)			)
		else:  # root.get_data() < data
				if right_child is None:
						return root
				else:
						root.set_right(del_node(_lowercase   ,    _lowercase			)			)

		if get_height(_lowercase			) - get_height(_lowercase			) == 2:
				assert right_child is not None
				if get_height(right_child.get_right()			) > get_height(right_child.get_left()			):
						lowercase__					:      List[Any]      =		left_rotation(_lowercase			)
				else:
						lowercase__					:      Optional[Any]      =		rl_rotation(_lowercase			)
		elif get_height(_lowercase			) - get_height(_lowercase			) == -2:
				assert left_child is not None
				if get_height(left_child.get_left()			) > get_height(left_child.get_right()			):
						lowercase__					:      Optional[int]      =		right_rotation(_lowercase			)
				else:
						lowercase__					:      Dict      =		lr_rotation(_lowercase			)
		lowercase__					:      Any      =		my_max(get_height(root.get_right()			)   ,    get_height(root.get_left()			)			) + 1
		root.set_height(_lowercase			)
		return root


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						str			):
			lowercase__					:      MyNode | None      =		None

	def    snake_case__(						self:						Optional[Any]			):
			return get_height(self.root			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						Any			):
			print('insert:' + str(lowerCamelCase_			)			)
			lowercase__					:      int      =		insert_node(self.root,					lowerCamelCase_			)

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						Any			):
			print('delete:' + str(lowerCamelCase_			)			)
			if self.root is None:
					print('Tree is empty!'			)
					return
			lowercase__					:      int      =		del_node(self.root,					lowerCamelCase_			)





	def __str__(						self:						str,					):  # a level traversale, gives a more intuitive look on the tree
			lowercase__					:      Optional[int]      =		''
			lowercase__					:      Any      =		MyQueue()
			q.push(self.root			)
			lowercase__					:      Dict      =		self.get_height()
			if layer == 0:
					return output
			lowercase__					:      Optional[int]      =		0
			while not q.is_empty():
					lowercase__					:      Dict      =		q.pop()
					lowercase__					:      Optional[Any]      =		' ' * int(math.pow(2,					layer - 1			)			)
					output += space
					if node is None:
							output += "*"
							q.push(lowerCamelCase_			)
							q.push(lowerCamelCase_			)
					else:
							output += str(node.get_data()			)
							q.push(node.get_left()			)
							q.push(node.get_right()			)
					output += space
					lowercase__					:      Union[str, Any]      =		cnt + 1
					for i in range(100			):
							if cnt == math.pow(2,					lowerCamelCase_			) - 1:
									lowercase__					:      Optional[int]      =		layer - 1
									if layer == 0:
											output += "\n*************************************"
											return output
									output += "\n"
									break
			output += "\n*************************************"
			return output


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		import doctest

		doctest.testmod()


if __name__ == "__main__":
			_test()
			__UpperCamelCase:	int = AVLtree()
			__UpperCamelCase:	int = list(range(1_0))
			random.shuffle(lst)
			for i in lst:
						t.insert(i)
						print(str(t))
			random.shuffle(lst)
			for i in lst:
						t.del_node(i)
						print(str(t))


