import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    @property
    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	1
       lowerCAmelCase_:							Dict       =	3
       lowerCAmelCase_:							Any       =	(32, 32)

       lowerCAmelCase_:							str       =	floats_tensor((batch_size, num_channels) + sizes       ,						rng=random.Random(0 ) ).to(UpperCamelCase_ )
       return image




    @property
    def      _a    (    self ):
       torch.manual_seed(0 )
       lowerCAmelCase_:							Union[str, Any]       =	UNetaDConditionModel(
           block_out_channels=(32, 64)       ,						layers_per_block=2       ,						sample_size=32       ,						in_channels=4       ,						out_channels=4       ,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")       ,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")       ,						cross_attention_dim=32       ,						)
       return model




    @property
    def      _a    (    self ):
       torch.manual_seed(0 )
       lowerCAmelCase_:							int       =	AutoencoderKL(
           block_out_channels=[32, 64]       ,						in_channels=3       ,						out_channels=3       ,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]       ,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]       ,						latent_channels=4       ,						)
       return model




    @property
    def      _a    (    self ):
       torch.manual_seed(0 )
       lowerCAmelCase_:							Any       =	RobertaSeriesConfig(
           hidden_size=32       ,						project_dim=32       ,						intermediate_size=37       ,						layer_norm_eps=1E-05       ,						num_attention_heads=4       ,						num_hidden_layers=5       ,						pad_token_id=1       ,						vocab_size=5_006       ,						)
       return RobertaSeriesModelWithTransformation(UpperCamelCase_ )




    @property
    def      _a    (    self ):


       def extract(*lowerCamelCase__       ,						**lowerCamelCase__ ):

          class 					_lowercase    :






              '''simple docstring'''




              def __init__(    self ):
                 lowerCAmelCase_:							List[Any]       =	torch.ones([0] )




              def      _a    (    self       ,						lowerCamelCase__ ):
                 self.pixel_values.to(UpperCamelCase_ )
                 return self





          return Out()

       return extract




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       lowerCAmelCase_:							Any       =	self.dummy_cond_unet
       lowerCAmelCase_:							Any       =	PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
       lowerCAmelCase_:							Dict       =	self.dummy_vae
       lowerCAmelCase_:							Tuple       =	self.dummy_text_encoder
       lowerCAmelCase_:							Dict       =	XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
       lowerCAmelCase_:							Optional[int]       =	77

       lowerCAmelCase_:							Optional[Any]       =	self.dummy_image.to(UpperCamelCase_ )
       lowerCAmelCase_:							Optional[Any]       =	init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       lowerCAmelCase_:							str       =	AltDiffusionImgaImgPipeline(
           unet=UpperCamelCase_       ,						scheduler=UpperCamelCase_       ,						vae=UpperCamelCase_       ,						text_encoder=UpperCamelCase_       ,						tokenizer=UpperCamelCase_       ,						safety_checker=UpperCamelCase_       ,						feature_extractor=self.dummy_extractor       ,						)
       lowerCAmelCase_:							Dict       =	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,						do_normalize=UpperCamelCase_ )
       lowerCAmelCase_:							List[Any]       =	alt_pipe.to(UpperCamelCase_ )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

       lowerCAmelCase_:							Tuple       =	'''A painting of a squirrel eating a burger'''
       lowerCAmelCase_:							List[Any]       =	torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
       lowerCAmelCase_:							Tuple       =	alt_pipe(
           [prompt]       ,						generator=UpperCamelCase_       ,						guidance_scale=6.0       ,						num_inference_steps=2       ,						output_type="np"       ,						image=UpperCamelCase_       ,						)

       lowerCAmelCase_:							List[Any]       =	output.images

       lowerCAmelCase_:							int       =	torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	alt_pipe(
           [prompt]       ,						generator=UpperCamelCase_       ,						guidance_scale=6.0       ,						num_inference_steps=2       ,						output_type="np"       ,						image=UpperCamelCase_       ,						return_dict=UpperCamelCase_       ,						)[0]

       lowerCAmelCase_:							str       =	image[0, -3:, -3:, -1]
       lowerCAmelCase_:							List[Any]       =	image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       lowerCAmelCase_:							Union[str, Any]       =	np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3




    @unittest.skipIf(torch_device != "cuda"       ,						"This test requires a GPU" )
    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.dummy_cond_unet
       lowerCAmelCase_:							Union[str, Any]       =	PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
       lowerCAmelCase_:							Optional[Any]       =	self.dummy_vae
       lowerCAmelCase_:							List[str]       =	self.dummy_text_encoder
       lowerCAmelCase_:							Optional[int]       =	XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
       lowerCAmelCase_:							Optional[int]       =	77

       lowerCAmelCase_:							str       =	self.dummy_image.to(UpperCamelCase_ )

       # put models in fp16
       lowerCAmelCase_:							Any       =	unet.half()
       lowerCAmelCase_:							Tuple       =	vae.half()
       lowerCAmelCase_:							Tuple       =	bert.half()

       # make sure here that pndm scheduler skips prk
       lowerCAmelCase_:							Union[str, Any]       =	AltDiffusionImgaImgPipeline(
           unet=UpperCamelCase_       ,						scheduler=UpperCamelCase_       ,						vae=UpperCamelCase_       ,						text_encoder=UpperCamelCase_       ,						tokenizer=UpperCamelCase_       ,						safety_checker=UpperCamelCase_       ,						feature_extractor=self.dummy_extractor       ,						)
       lowerCAmelCase_:							List[Any]       =	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,						do_normalize=UpperCamelCase_ )
       lowerCAmelCase_:							int       =	alt_pipe.to(UpperCamelCase_ )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

       lowerCAmelCase_:							str       =	'''A painting of a squirrel eating a burger'''
       lowerCAmelCase_:							List[Any]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	alt_pipe(
           [prompt]       ,						generator=UpperCamelCase_       ,						num_inference_steps=2       ,						output_type="np"       ,						image=UpperCamelCase_       ,						).images

       assert image.shape == (1, 32, 32, 3)




    @unittest.skipIf(torch_device != "cuda"       ,						"This test requires a GPU" )
    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/img2img/sketch-mountains-input.jpg" )
       # resize to resolution that is divisible by 8 but not 16 or 32
       lowerCAmelCase_:							Optional[int]       =	init_image.resize((760, 504) )

       lowerCAmelCase_:							Any       =	'''BAAI/AltDiffusion'''
       lowerCAmelCase_:							Union[str, Any]       =	AltDiffusionImgaImgPipeline.from_pretrained(
           UpperCamelCase_       ,						safety_checker=UpperCamelCase_       ,						)
       pipe.to(UpperCamelCase_ )
       pipe.set_progress_bar_config(disable=UpperCamelCase_ )
       pipe.enable_attention_slicing()

       lowerCAmelCase_:							Any       =	'''A fantasy landscape, trending on artstation'''

       lowerCAmelCase_:							Optional[int]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							str       =	pipe(
           prompt=UpperCamelCase_       ,						image=UpperCamelCase_       ,						strength=0.7_5       ,						guidance_scale=7.5       ,						generator=UpperCamelCase_       ,						output_type="np"       ,						)
       lowerCAmelCase_:							Tuple       =	output.images[0]

       lowerCAmelCase_:							Optional[int]       =	image[255:258, 383:386, -1]

       assert image.shape == (504, 760, 3)
       lowerCAmelCase_:							str       =	np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2





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






    '''simple docstring'''




    def      _a    (    self ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/img2img/sketch-mountains-input.jpg" )
       lowerCAmelCase_:							Any       =	init_image.resize((768, 512) )
       lowerCAmelCase_:							Any       =	load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )

       lowerCAmelCase_:							str       =	'''BAAI/AltDiffusion'''
       lowerCAmelCase_:							str       =	AltDiffusionImgaImgPipeline.from_pretrained(
           UpperCamelCase_       ,						safety_checker=UpperCamelCase_       ,						)
       pipe.to(UpperCamelCase_ )
       pipe.set_progress_bar_config(disable=UpperCamelCase_ )
       pipe.enable_attention_slicing()

       lowerCAmelCase_:							Optional[int]       =	'''A fantasy landscape, trending on artstation'''

       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Any       =	pipe(
           prompt=UpperCamelCase_       ,						image=UpperCamelCase_       ,						strength=0.7_5       ,						guidance_scale=7.5       ,						generator=UpperCamelCase_       ,						output_type="np"       ,						)
       lowerCAmelCase_:							Any       =	output.images[0]

       assert image.shape == (512, 768, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image ).max() < 1E-2



'''simple docstring'''
from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class   lowercase      :

		"""simple docstring"""

		_a       					=							42
		_a       					=							None
		_a       					=							None



__snake_case				 =	namedtuple('''CoinsDistribResult''', '''moves excess''')


def      a (     __a  )    ->      int:



					'''simple docstring'''






					if root is None:
										return 0

					# Validation
					def count_nodes(__a  ) -> int:
										if node is None:
															return 0

										return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

					def count_coins(__a  ) -> int:
										if node is None:
															return 0

										return count_coins(node.left  ) + count_coins(node.right  ) + node.data

					if count_nodes(__a  ) != count_coins(__a  ):
										raise ValueError('''The nodes number should be same as the number of coins'''  )

					# Main calculation
					def get_distrib(__a  ) -> CoinsDistribResult:

										if node is None:
															return CoinsDistribResult(0					,   1  )

										UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					get_distrib(node.left  )
										UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					get_distrib(node.right  )

										UpperCamelCase__  :Tuple				   =					1 - left_distrib_excess
										UpperCamelCase__  :Optional[int]				   =					1 - right_distrib_excess

										UpperCamelCase__  :int				   =					(
										    left_distrib_moves
										    + right_distrib_moves
										    + abs(__a  )
										    + abs(__a  )
										)
										UpperCamelCase__  :Optional[Any]				   =					node.data - coins_to_left - coins_to_right

										return CoinsDistribResult(__a					,   __a  )

					return get_distrib(__a  )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()