



from collections.abc import Iterable
from typing import Any



class     _UpperCAmelCase			:




     def __init__(				self ,						a__ = None):
         A__		 =					value
         A__		 =					None  # Added in order to delete a node easier
         A__		 =					None
         A__		 =					None




     def __repr__(				self):
         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 ,						a__ = None):
         A__		 =					root




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




     def 		snake_case_		(				self ,						a__ ,						a__):

         if new_children is not None:  # reset its kids
             A__		 =					node.parent
         if node.parent is not None:  # reset its parent
             if self.is_right(__SCREAMING_SNAKE_CASE):  # If it is the right children
                 A__		 =					new_children
             else:
                 A__		 =					new_children
         else:
             A__		 =					new_children




     def 		snake_case_		(				self ,						a__):

         if node.parent and node.parent.right:
             return node == node.parent.right
         return False




     def 		snake_case_		(				self):
         return self.root is None




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




     def 		snake_case_		(				self ,						*a__):

         for value in values:
             self.__insert(__SCREAMING_SNAKE_CASE)




     def 		snake_case_		(				self ,						a__):

         if self.empty():
             raise IndexError('''Warning: Tree is empty! please use another.''')
         else:
             A__		 =					self.root
             # use lazy evaluation here to avoid NoneType Attribute error
             while node is not None and node.value is not value:
                 A__		 =					node.left if value < node.value else node.right
             return node




     def 		snake_case_		(				self ,						a__ = None):

         if node is None:
             if self.root is None:
                 return None
             A__		 =					self.root

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




     def 		snake_case_		(				self ,						a__ = None):

         if node is None:
             A__		 =					self.root
         if self.root is None:
             return None
         if not self.empty():
             A__		 =					self.root
             while node.left is not None:
                 A__		 =					node.left
         return node




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




     def 		snake_case_		(				self ,						a__):

         if node is not None:
             yield node  # Preorder Traversal
             yield from self.preorder_traverse(node.left)
             yield from self.preorder_traverse(node.right)




     def 		snake_case_		(				self ,						a__=None):

         if traversal_function is None:
             return self.preorder_traverse(self.root)
         else:
             return traversal_function(self.root)




     def 		snake_case_		(				self ,						a__ ,						a__):

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




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					[]
         self.inorder(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE)  # append all values to list using inorder traversal
         return arr[k - 1]







def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any]	)->  Union[str, Any]:
    A__		 =					[]
    if curr_node is not None:
        A__		 =					postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
    return node_list



def 		lowerCAmelCase__    (					)->  Any:
    A__		 =					(8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
    A__		 =					BinarySearchTree()
    for i in testlist:
        t.insert(_UpperCAmelCase	)

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

    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(_UpperCAmelCase	)
        print(_UpperCAmelCase	)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				IFPipeline
     UpperCamelCase__		    =				TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     UpperCamelCase__		    =				TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		    =				PipelineTesterMixin.required_optional_params - {'''latents'''}




     def 		snake_case_		(				self):
         return self._get_dummy_components()




     def 		snake_case_		(				self ,						a__ ,						a__=0):
         if str(a__).startswith('''mps'''):
             A__		 =					torch.manual_seed(a__)
         else:
             A__		 =					torch.Generator(device=a__).manual_seed(a__)

         A__		 =					{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs




     def 		snake_case_		(				self):
         self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != '''cuda''' ,						reason='''float16 requires CUDA''')
     def 		snake_case_		(				self):
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1)




     def 		snake_case_		(				self):
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




     def 		snake_case_		(				self):
         self._test_save_load_local()




     def 		snake_case_		(				self):
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,						)




     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available() ,						reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,						)
     def 		snake_case_		(				self):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)







@slow
@require_torch_gpu
class     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(				self):
         # if

         A__		 =					IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' ,						variant='''fp16''' ,						torch_dtype=torch.floataa)

         A__		 =					IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0''' ,						variant='''fp16''' ,						torch_dtype=torch.floataa ,						text_encoder=a__ ,						tokenizer=a__)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda''')

         A__ ,	A__		 =					pipe_a.encode_prompt('''anime turtle''' ,						device='''cuda''')

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         A__		 =					None
         A__		 =					None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         A__		 =					IFImgaImgPipeline(**pipe_a.components)
         A__		 =					IFImgaImgSuperResolutionPipeline(**pipe_a.components)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if_imgaimg(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         A__		 =					IFInpaintingPipeline(**pipe_a.components)
         A__		 =					IFInpaintingSuperResolutionPipeline(**pipe_a.components)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if_inpainting(a__ ,						a__ ,						a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_3 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(1)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(1)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)







def 		lowerCAmelCase__    (					)->  List[Any]:
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()
