"""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__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

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

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							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




"""simple docstring"""






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


lowerCAmelCase__						:						Any		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						str		  =			{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"ctrl"
					snake_case__       					=				["past_key_values"]
					snake_case__       					=				{
					    "max_position_embeddings": "n_positions",
					    "hidden_size": "n_embd",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__(       self     :			Any					,lowerCamelCase__     :			str=246_534					,lowerCamelCase__     :			List[str]=256					,lowerCamelCase__     :			Optional[int]=1_280					,lowerCamelCase__     :			Any=8_192					,lowerCamelCase__     :			int=48					,lowerCamelCase__     :			Optional[Any]=16					,lowerCamelCase__     :			Union[str, Any]=0.1					,lowerCamelCase__     :			Dict=0.1					,lowerCamelCase__     :			List[str]=1e-6					,lowerCamelCase__     :			List[str]=0.0_2					,lowerCamelCase__     :			Tuple=True					,**lowerCamelCase__     :			Optional[Any]					,):
									UpperCAmelCase__    =							vocab_size
									UpperCAmelCase__    =							n_positions
									UpperCAmelCase__    =							n_embd
									UpperCAmelCase__    =							n_layer
									UpperCAmelCase__    =							n_head
									UpperCAmelCase__    =							dff
									UpperCAmelCase__    =							resid_pdrop
									UpperCAmelCase__    =							embd_pdrop
									UpperCAmelCase__    =							layer_norm_epsilon
									UpperCAmelCase__    =							initializer_range

									UpperCAmelCase__    =							use_cache

									super().__init__(**lowerCamelCase__    )



