


import enum
import shutil
import sys


snake_case__ ,snake_case__			 =						shutil.get_terminal_size()

snake_case__			 =						{'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}


class 					lowerCAmelCase_ (       enum.Enum):
						lowerCamelCase_         =     0
						lowerCamelCase_         =     1






def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   Tuple=""				)    ->	Dict:





			"""simple docstring"""
			sys.stdout.write(str(a				) + end				)
			sys.stdout.flush()


def 				lowerCamelCase__	(      a	:   int	,  a	:   Union[str, Any]	,  a	:   Tuple=""				)    ->	str:





			"""simple docstring"""
			forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''	,  a				)


def 				lowerCamelCase__	(      )    ->	Dict:





			"""simple docstring"""
			forceWrite("\r"				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   str				)    ->	str:





			"""simple docstring"""
			forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''				)


def 				lowerCamelCase__	(      )    ->	Optional[int]:





			"""simple docstring"""
			forceWrite(" " * TERMINAL_WIDTH				)
			reset_cursor()







def 				lowerCamelCase__	(      )    ->	List[Any]:





			"""simple docstring"""
			reset_cursor()
			forceWrite("-" * TERMINAL_WIDTH				)






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






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




						def  _snake_case					(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    "A painting of a squirrel eating a burger "
									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     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  _snake_case					(	self						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

									a__ :Tuple						  =    image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]						)

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


