"""simple docstring"""




import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__lowerCamelCase											=  logging.get_logger(__name__)  # pylint: disable=invalid-name
class 			UpperCamelCase__(			__A ):





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase=7_68     )					->				Tuple:
													super().__init__(__UpperCAmelCase     )
													A__	     =							proj_size

													A__	     =							CLIPVisionModel(__UpperCAmelCase     )
													A__	     =							PaintByExampleMapper(__UpperCAmelCase     )
													A__	     =							nn.LayerNorm(config.hidden_size     )
													A__	     =							nn.Linear(config.hidden_size		,self.proj_size     )

													# uncondition for scaling
													A__	     =							nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=False     )					->				Dict:
													A__	     =							self.model(pixel_values=__UpperCAmelCase     )
													A__	     =							clip_output.pooler_output
													A__	     =							self.mapper(latent_states[:, None]     )
													A__	     =							self.final_layer_norm(__UpperCAmelCase     )
													A__	     =							self.proj_out(__UpperCAmelCase     )
													if return_uncond_vector:
																				return latent_states, self.uncond_vector

													return latent_states






class 			UpperCamelCase__(			nn.Module ):





						def __init__(       self		,__UpperCAmelCase     )					->				str:
													super().__init__()
													A__	     =							(config.num_hidden_layers + 1) // 5
													A__	     =							config.hidden_size
													A__	     =							1
													A__	     =							nn.ModuleList(
													    [
													        BasicTransformerBlock(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,activation_fn='gelu'		,attention_bias=__UpperCAmelCase     )
													        for _ in range(__UpperCAmelCase     )
													    ]     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				List[Any]:
													for block in self.blocks:
																				A__	     =							block(__UpperCAmelCase     )

													return hidden_states






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
				import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class     UpperCamelCase__(			unittest.TestCase ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'     )

													# using the PNDM scheduler by default
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A red cat sitting on a park bench'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





