



"""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


lowerCAmelCase_   						=   False
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		pass





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







		'''simple docstring'''





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






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
				pipe.to(_snake_case						)
				pipe.set_progress_bar_config(disable=_snake_case						)

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

				lowercase__   :    List[Any]				=			image[0, 253:256, 253:256, -1]

				assert image.shape == (1, 512, 512, 3)
				lowercase__   :    List[Any]				=			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





"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_   						=   {'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XGLMConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['XGLMTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['XGLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'XGLMForCausalLM',
							    'XGLMModel',
							    'XGLMPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'FlaxXGLMForCausalLM',
							    'FlaxXGLMModel',
							    'FlaxXGLMPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFXGLMForCausalLM',
							    'TFXGLMModel',
							    'TFXGLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure)
