



'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length






'''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


a     :    Tuple      						=					False

class     UpperCamelCase_				(							unittest.TestCase    ):
	pass




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





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Any							  =	VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""					)
								pipe.to(A					)
								pipe.set_progress_bar_config(disable=A					)

								UpperCAmelCase  :	List[str]							  =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""					)
								UpperCAmelCase  :	List[Any]							  =	torch.manual_seed(0					)
								UpperCAmelCase  :	List[str]							  =	pipe(
								    image=A	,	generator=A	,	guidance_scale=7.5	,	num_inference_steps=50	,	output_type="""numpy"""	,	).images

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

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase  :	List[str]							  =	np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]					)

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

