





'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


snake_case_  :							Dict            =	False


@nightly
@require_torch_gpu
class        __a   (unittest.TestCase  ):

							def      UpperCAmelCase__						(				self				:  Dict )       ->							int:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							def      UpperCAmelCase__						(				self				:  Tuple )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   str      =	load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )

													UpperCAmelCase_      :   List[str]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Dict      =	pipe(
													    prompt='''first prompt'''							,  image=__magic_name__							,  text_to_image_strength=0.7_5							,  generator=__magic_name__							,  guidance_scale=7.5							,  num_inference_steps=2							,  output_type='''numpy'''							,  ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(__magic_name__ )
																			UpperCAmelCase_      :   List[str]      =	VersatileDiffusionDualGuidedPipeline.from_pretrained(__magic_name__ )

													pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   str      =	generator.manual_seed(0 )
													UpperCAmelCase_      :   str      =	pipe(
													    prompt='''first prompt'''							,  image=__magic_name__							,  text_to_image_strength=0.7_5							,  generator=__magic_name__							,  guidance_scale=7.5							,  num_inference_steps=2							,  output_type='''numpy'''							,  ).images

													assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"



							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
													pipe.remove_unused_weights()
													pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   Optional[int]      =	'''cyberpunk 2077'''
													UpperCAmelCase_      :   List[Any]      =	load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
													UpperCAmelCase_      :   List[str]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   str      =	pipe(
													    prompt=__magic_name__							,  image=__magic_name__							,  text_to_image_strength=0.7_5							,  generator=__magic_name__							,  guidance_scale=7.5							,  num_inference_steps=50							,  output_type='''numpy'''							,  ).images

													UpperCAmelCase_      :   Any      =	image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_      :   Optional[Any]      =	np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9] )

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










'''simple docstring'''

import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    CustomConfig




					def __init__(	self					:						int  ,	UpperCamelCase_					:						List[str]       )	->							int:



						'''simple docstring'''





						super().__init__(UpperCamelCase_       )
						_lowercase       :							List[Any]  	=		torch.nn.Linear(config.hidden_size  ,	config.hidden_size       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Any       )	->							Union[str, Any]:



						'''simple docstring'''





						return self.linear(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[Any]       )	->							str:



						'''simple docstring'''





						pass






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    NoSuperInitConfig




					def __init__(	self					:						Optional[int]  ,	UpperCamelCase_					:						Dict       )	->							Optional[Any]:



						'''simple docstring'''





						super().__init__(UpperCamelCase_       )
						_lowercase       :							List[str]  	=		torch.nn.Linear(config.attribute  ,	config.attribute       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





						return self.linear(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						int       )	->							List[str]:



						'''simple docstring'''





						pass
