




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       str						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				str							=   "openai-gpt"
			lowerCAmelCase_					:				Optional[int]							=   {
			    "max_position_embeddings": "n_positions",
			    "hidden_size": "n_embd",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}




			def __init__( self    ,							a__=40_478    ,							a__=512    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__="gelu"    ,							a__=0.1    ,							a__=0.1    ,							a__=0.1    ,							a__=1e-5    ,							a__=0.0_2    ,							a__="cls_index"    ,							a__=True    ,							a__=None    ,							a__=True    ,							a__=0.1    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				vocab_size
										snake_case_      =				n_positions
										snake_case_      =				n_embd
										snake_case_      =				n_layer
										snake_case_      =				n_head
										snake_case_      =				afn
										snake_case_      =				resid_pdrop
										snake_case_      =				embd_pdrop
										snake_case_      =				attn_pdrop
										snake_case_      =				layer_norm_epsilon
										snake_case_      =				initializer_range
										snake_case_      =				summary_type
										snake_case_      =				summary_use_proj
										snake_case_      =				summary_activation
										snake_case_      =				summary_first_dropout
										snake_case_      =				summary_proj_to_labels
										super().__init__(**a__							)










'''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


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




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




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

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

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

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




