




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 		__A      (			unittest.TestCase			):

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

						@property
						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	str								=1
										__magic_name__	:	Tuple								=3
										__magic_name__	:	Tuple								=(32, 32)

										__magic_name__	:	Tuple								=floats_tensor((batch_size, num_channels) + sizes    , rng=random.Random(0 ) ).to(lowerCAmelCase__ )
										return image

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										torch.manual_seed(0 )
										__magic_name__	:	List[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   :Union[str, Any] ):
										'''simple docstring'''

										torch.manual_seed(0 )
										__magic_name__	:	Dict								=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   :Any ):
										'''simple docstring'''

										torch.manual_seed(0 )
										__magic_name__	:	Union[str, 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=50_06    , )
										return RobertaSeriesModelWithTransformation(lowerCAmelCase__ )

						@property
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										def extract(*__snake_case   :Tuple    , **__snake_case   :Any ):




														class 		__A      :

																				def __init__(		self   :Tuple ):
																								'''simple docstring'''

																								__magic_name__	:	Dict								=torch.ones([0] )


																				def     A__						(		self   :Any    , __snake_case   :List[Any] ):
																								'''simple docstring'''

																								self.pixel_values.to(lowerCAmelCase__ )
																								return self


														return Out()

										return extract

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Dict								="cpu"  # ensure determinism for the device-dependent torch.Generator
										__magic_name__	:	Any								=self.dummy_cond_unet
										__magic_name__	:	Dict								=PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
										__magic_name__	:	Tuple								=self.dummy_vae
										__magic_name__	:	Any								=self.dummy_text_encoder
										__magic_name__	:	Tuple								=XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
										__magic_name__	:	str								=77

										__magic_name__	:	Tuple								=self.dummy_image.to(lowerCAmelCase__ )
										__magic_name__	:	Optional[int]								=init_image / 2 + 0.5

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

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

										__magic_name__	:	List[str]								=output.images

										__magic_name__	:	Dict								=torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
										__magic_name__	:	List[Any]								=alt_pipe(
										    [prompt]    , generator=lowerCAmelCase__    , guidance_scale=6.0    , num_inference_steps=2    , output_type="""np"""    , image=lowerCAmelCase__    , return_dict=lowerCAmelCase__    , )[0]

										__magic_name__	:	Optional[int]								=image[0, -3:, -3:, -1]
										__magic_name__	:	Optional[Any]								=image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										__magic_name__	:	Dict								=np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499] )

										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   :str ):
										'''simple docstring'''

										__magic_name__	:	str								=self.dummy_cond_unet
										__magic_name__	:	int								=PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
										__magic_name__	:	Optional[Any]								=self.dummy_vae
										__magic_name__	:	Tuple								=self.dummy_text_encoder
										__magic_name__	:	Optional[int]								=XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta""" )
										__magic_name__	:	Optional[Any]								=77

										__magic_name__	:	Any								=self.dummy_image.to(lowerCAmelCase__ )

										# put models in fp16
										__magic_name__	:	Any								=unet.half()
										__magic_name__	:	List[str]								=vae.half()
										__magic_name__	:	Optional[Any]								=bert.half()

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

										__magic_name__	:	Any								="A painting of a squirrel eating a burger"
										__magic_name__	:	Any								=torch.manual_seed(0 )
										__magic_name__	:	int								=alt_pipe(
										    [prompt]    , generator=lowerCAmelCase__    , num_inference_steps=2    , output_type="""np"""    , image=lowerCAmelCase__    , ).images

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


						@unittest.skipIf(torch_device != """cuda"""    , """This test requires a GPU""" )
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	List[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
										__magic_name__	:	Optional[Any]								=init_image.resize((7_60, 5_04) )

										__magic_name__	:	Optional[int]								="BAAI/AltDiffusion"
										__magic_name__	:	Union[str, Any]								=AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__    , safety_checker=lowerCAmelCase__    , )
										pipe.to(lowerCAmelCase__ )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
										pipe.enable_attention_slicing()

										__magic_name__	:	str								="A fantasy landscape, trending on artstation"

										__magic_name__	:	Tuple								=torch.manual_seed(0 )
										__magic_name__	:	List[Any]								=pipe(
										    prompt=lowerCAmelCase__    , image=lowerCAmelCase__    , strength=0.75    , guidance_scale=7.5    , generator=lowerCAmelCase__    , output_type="""np"""    , )
										__magic_name__	:	Union[str, Any]								=output.images[0]

										__magic_name__	:	List[Any]								=image[2_55:2_58, 3_83:3_86, -1]

										assert image.shape == (5_04, 7_60, 3)
										__magic_name__	:	Dict								=np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000] )

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




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

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	str								=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/img2img/sketch-mountains-input.jpg""" )
										__magic_name__	:	Any								=init_image.resize((7_68, 5_12) )
										__magic_name__	:	Optional[int]								=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy""" )

										__magic_name__	:	Optional[Any]								="BAAI/AltDiffusion"
										__magic_name__	:	Optional[int]								=AltDiffusionImgaImgPipeline.from_pretrained(
										    lowerCAmelCase__    , safety_checker=lowerCAmelCase__    , )
										pipe.to(lowerCAmelCase__ )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
										pipe.enable_attention_slicing()

										__magic_name__	:	Tuple								="A fantasy landscape, trending on artstation"

										__magic_name__	:	Union[str, Any]								=torch.manual_seed(0 )
										__magic_name__	:	Dict								=pipe(
										    prompt=lowerCAmelCase__    , image=lowerCAmelCase__    , strength=0.75    , guidance_scale=7.5    , generator=lowerCAmelCase__    , output_type="""np"""    , )
										__magic_name__	:	int								=output.images[0]

										assert image.shape == (5_12, 7_68, 3)
										# img2img is flaky across GPUs even in fp32, so using MAE here
										assert np.abs(expected_image - image ).max() < 1E-2










from math import pow, sqrt




def 		lowerCAmelCase_   (	*lowerCamelCase   ):
				__magic_name__	:	Tuple								=len(lowerCamelCase   ) > 0 and all(value > 0.0 for value in values   )
				return result




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(sqrt(molar_mass_a / molar_mass_a   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase   )
				    else ValueError("""Input Error: Molar mass values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(molar_mass / pow(effusion_rate_a / effusion_rate_a	,     2   )	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				return (
				    round(pow(effusion_rate_a / effusion_rate_a	,     2   ) / molar_mass	,     6   )
				    if validate(lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   )
				    else ValueError(
				        """Input Error: Molar mass and effusion rate values must greater than 0."""   )
				)




