




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A        =    {
    "configuration_xlm_roberta_xl": [
        "XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaXLConfig",
        "XLMRobertaXLOnnxConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__A        =    [
					    "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMRobertaXLForCausalLM",
					    "XLMRobertaXLForMaskedLM",
					    "XLMRobertaXLForMultipleChoice",
					    "XLMRobertaXLForQuestionAnswering",
					    "XLMRobertaXLForSequenceClassification",
					    "XLMRobertaXLForTokenClassification",
					    "XLMRobertaXLModel",
					    "XLMRobertaXLPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta_xl import (
					    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaXLConfig,
					    XLMRobertaXLOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta_xl import (
										    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaXLForCausalLM,
										    XLMRobertaXLForMaskedLM,
										    XLMRobertaXLForMultipleChoice,
										    XLMRobertaXLForQuestionAnswering,
										    XLMRobertaXLForSequenceClassification,
										    XLMRobertaXLForTokenClassification,
										    XLMRobertaXLModel,
										    XLMRobertaXLPreTrainedModel,
										)

else:
					import sys

					__A        =    _LazyModule(__name__, globals()["__file__"], _import_structure)





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


_lowerCAmelCase					:			Optional[Any]  				=			False






class 	__magic_name__    (			unittest.TestCase      ):
			pass






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





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

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

						__a        =image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						__a        =np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945]					)

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






