





'''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 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


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


    @property
    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			ort.SessionOptions()
        _SCREAMING_SNAKE_CASE									=			False
        return options





    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
        _SCREAMING_SNAKE_CASE									=			load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
        _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
            """CompVis/stable-diffusion-v1-4""" ,		revision="""onnx""" ,		safety_checker=A ,		feature_extractor=A ,		provider=self.gpu_provider ,		sess_options=self.gpu_options ,		)
        pipe.set_progress_bar_config(disable=A       )

        _SCREAMING_SNAKE_CASE									=			"""A red cat sitting on a park bench"""

        _SCREAMING_SNAKE_CASE									=			np.random.RandomState(0       )
        _SCREAMING_SNAKE_CASE									=			pipe(
            prompt=A ,		image=A ,		mask_image=A ,		strength=0.75 ,		guidance_scale=7.5 ,		num_inference_steps=15 ,		generator=A ,		output_type="""np""" ,		)

        _SCREAMING_SNAKE_CASE									=			output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image       ).max() < 1e-2







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



