


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Any   				=      KandinskyInpaintPipeline
					UpperCAmelCase__ :   Optional[int]   				=      ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
					UpperCAmelCase__ :   str   				=      [
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					    '''mask_image''',
					]
					UpperCAmelCase__ :   Optional[int]   				=      [
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__ :   List[str]   				=      False
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   Tuple:
												return 32
					@property
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[Any]:
												return 32
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   List[str]:
												return self.time_input_dim
					@property
					def 			lowerCamelCase__(				self       :int				) ->   str:
												return self.time_input_dim * 4
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
												return 1_00
					@property
					def 			lowerCamelCase__(				self       :int				) ->   Tuple:
												a__					=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'				)
												return tokenizer
					@property
					def 			lowerCamelCase__(				self       :str				) ->   List[str]:
												torch.manual_seed(0				)
												a__					=					MCLIPConfig(
												    numDims=self.cross_attention_dim      ,transformerDimensions=self.text_embedder_hidden_size      ,hidden_size=self.text_embedder_hidden_size      ,intermediate_size=37      ,num_attention_heads=4      ,num_hidden_layers=5      ,vocab_size=10_05      ,)

												a__					=					MultilingualCLIP(__snake_case				)
												a__					=					text_encoder.eval()

												return text_encoder
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   List[str]:
												torch.manual_seed(0				)

												a__					=					{
												    'in_channels': 9,
												    # Out channels is double in channels because predicts mean and variance
												    'out_channels': 8,
												    'addition_embed_type': 'text_image',
												    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
												    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
												    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
												    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    'layers_per_block': 1,
												    'encoder_hid_dim': self.text_embedder_hidden_size,
												    'encoder_hid_dim_type': 'text_image_proj',
												    'cross_attention_dim': self.cross_attention_dim,
												    'attention_head_dim': 4,
												    'resnet_time_scale_shift': 'scale_shift',
												    'class_embed_type': None,
												}

												a__					=					UNetaDConditionModel(**__snake_case				)
												return model
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   Any:
												return {
												    "block_out_channels": [32, 64],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 12,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Union[str, Any]:
												torch.manual_seed(0				)
												a__					=					VQModel(**self.dummy_movq_kwargs				)
												return model
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[Any]:
												a__					=					self.dummy_text_encoder
												a__					=					self.dummy_tokenizer
												a__					=					self.dummy_unet
												a__					=					self.dummy_movq

												a__					=					DDIMScheduler(
												    num_train_timesteps=10_00      ,beta_schedule='linear'      ,beta_start=0.0_00_85      ,beta_end=0.0_12      ,clip_sample=__snake_case      ,set_alpha_to_one=__snake_case      ,steps_offset=1      ,prediction_type='epsilon'      ,thresholding=__snake_case      ,)

												a__					=					{
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'unet': unet,
												    'scheduler': scheduler,
												    'movq': movq,
												}

												return components
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Tuple=0				) ->   Optional[int]:
												a__					=					floats_tensor((1, self.cross_attention_dim)      ,rng=random.Random(__snake_case				)				).to(__snake_case				)
												a__					=					floats_tensor((1, self.cross_attention_dim)      ,rng=random.Random(seed + 1				)				).to(__snake_case				)
												# create init_image
												a__					=					floats_tensor((1, 3, 64, 64)      ,rng=random.Random(__snake_case				)				).to(__snake_case				)
												a__					=					image.cpu().permute(0      ,2      ,3      ,1				)[0]
												a__					=					Image.fromarray(np.uinta(__snake_case				)				).convert('RGB'				).resize((2_56, 2_56)				)
												# create mask
												a__					=					np.ones((64, 64)      ,dtype=np.floataa				)
												a__					=					0

												if str(__snake_case				).startswith('mps'				):
																			a__					=					torch.manual_seed(__snake_case				)
												else:
																			a__					=					torch.Generator(device=__snake_case				).manual_seed(__snake_case				)
												a__					=					{
												    'prompt': 'horse',
												    'image': init_image,
												    'mask_image': mask,
												    'image_embeds': image_embeds,
												    'negative_image_embeds': negative_image_embeds,
												    'generator': generator,
												    'height': 64,
												    'width': 64,
												    'num_inference_steps': 2,
												    'guidance_scale': 4.0,
												    'output_type': 'np',
												}
												return inputs
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												a__					=					'cpu'

												a__					=					self.get_dummy_components()

												a__					=					self.pipeline_class(**__snake_case				)
												a__					=					pipe.to(__snake_case				)

												pipe.set_progress_bar_config(disable=__snake_case				)

												a__					=					pipe(**self.get_dummy_inputs(__snake_case				)				)
												a__					=					output.images

												a__					=					pipe(
												    **self.get_dummy_inputs(__snake_case				)      ,return_dict=__snake_case      ,)[0]

												a__					=					image[0, -3:, -3:, -1]
												a__					=					image_from_tuple[0, -3:, -3:, -1]

												print(F'image.shape {image.shape}'				)

												assert image.shape == (1, 64, 64, 3)

												a__					=					np.array(
												    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]				)

												assert (
												    np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
												), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
												), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'



					def 			lowerCamelCase__(				self       :List[Any]				) ->   int:
												super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







@slow
@require_torch_gpu
class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def 			lowerCamelCase__(				self       :int				) ->   int:
												a__					=					load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'				)

												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
												a__					=					np.ones((7_68, 7_68)      ,dtype=np.floataa				)
												a__					=					0

												a__					=					'a hat'

												a__					=					KandinskyPriorPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-1-prior'      ,torch_dtype=torch.floataa				)
												pipe_prior.to(__snake_case				)

												a__					=					KandinskyInpaintPipeline.from_pretrained(
												    'kandinsky-community/kandinsky-2-1-inpaint'      ,torch_dtype=torch.floataa				)
												a__					=					pipeline.to(__snake_case				)
												pipeline.set_progress_bar_config(disable=__snake_case				)

												a__					=					torch.Generator(device='cpu'				).manual_seed(0				)
												a__				, a__					=					pipe_prior(
												    __snake_case      ,generator=__snake_case      ,num_inference_steps=5      ,negative_prompt=''      ,).to_tuple()

												a__					=					pipeline(
												    __snake_case      ,image=__snake_case      ,mask_image=__snake_case      ,image_embeds=__snake_case      ,negative_image_embeds=__snake_case      ,generator=__snake_case      ,num_inference_steps=1_00      ,height=7_68      ,width=7_68      ,output_type='np'      ,)

												a__					=					output.images[0]

												assert image.shape == (7_68, 7_68, 3)

												assert_mean_pixel_difference(__snake_case      ,__snake_case				)








from __future__ import annotations







class 	snake_case_       :
					def __init__(				self       :List[str]      ,__snake_case       :int				) ->   None:
												a__					=					data
												a__					=					None
												a__					=					None






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):  # In Order traversal of the tree
							if tree:
														display(tree.left       )
														print(tree.data       )
														display(tree.right       )






def 							__lowercase       (			__lowerCAmelCase							:    Node | None       ):
							return 1 + max(depth_of_tree(tree.left       ) ,   depth_of_tree(tree.right       )       ) if tree else 0






def 							__lowercase       (			__lowerCAmelCase							:    Node       ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left       ) and is_full_binary_tree(tree.right       )
							else:
														return not tree.left and not tree.right






def 							__lowercase       (			):  # Main function for testing.
							a__					=					Node(1       )
							a__					=					Node(2       )
							a__					=					Node(3       )
							a__					=					Node(4       )
							a__					=					Node(5       )
							a__					=					Node(6       )
							a__					=					Node(7       )
							a__					=					Node(8       )
							a__					=					Node(9       )

							print(is_full_binary_tree(__lowerCAmelCase       )       )
							print(depth_of_tree(__lowerCAmelCase       )       )
							print('Tree is: '       )
							display(__lowerCAmelCase       )


if __name__ == "__main__":
						main()




