


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__UpperCAmelCase					       =							{'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase					       =							['EncoderDecoderModel']

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase					       =							['TFEncoderDecoderModel']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase					       =							['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
							from .configuration_encoder_decoder import EncoderDecoderConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encoder_decoder import EncoderDecoderModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
							import sys

							__UpperCAmelCase					       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase					       =							False




class 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''     )
														pipe.to(lowerCamelCase_     )
														pipe.set_progress_bar_config(disable=lowerCamelCase_     )

														lowerCAmelCase__      =			load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''     )
														lowerCAmelCase__      =			torch.manual_seed(0     )
														lowerCAmelCase__      =			pipe(
														    image=lowerCamelCase_      ,					generator=lowerCamelCase_      ,					guidance_scale=7.5      ,					num_inference_steps=50      ,					output_type='''numpy'''      ,					).images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__      =			np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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