



'''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 							lowercase__     (							unittest.TestCase       ):





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)





	@property
	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       ort.SessionOptions()
							_UpperCamelCase :		Optional[Any]			    =       False
							return options





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/in_paint/overture-creations-5sI6fQgYIuo.png'			)
							_UpperCamelCase :		Tuple			    =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'			)
							_UpperCamelCase :		Optional[int]			    =       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
							_UpperCamelCase :		List[Any]			    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'			,revision='onnx'			,safety_checker=lowerCamelCase__			,feature_extractor=lowerCamelCase__			,provider=self.gpu_provider			,sess_options=self.gpu_options			,)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Any			    =       'A red cat sitting on a park bench'

							_UpperCamelCase :		List[str]			    =       np.random.RandomState(0			)
							_UpperCamelCase :		str			    =       pipe(
							    prompt=lowerCamelCase__			,image=lowerCamelCase__			,mask_image=lowerCamelCase__			,strength=0.7_5			,guidance_scale=7.5			,num_inference_steps=15			,generator=lowerCamelCase__			,output_type='np'			,)

							_UpperCamelCase :		Union[str, Any]			    =       output.images[0]

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








'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_			:     Optional[int]  	=  logging.get_logger(__name__)

snake_case_			:     List[Any]  	=  {
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """xlm-roberta-xl"""





	def __init__(			self :    Optional[int]			,lowerCamelCase__ :    Optional[Any]=250880			,lowerCamelCase__ :    Tuple=2560			,lowerCamelCase__ :    Union[str, Any]=36			,lowerCamelCase__ :    List[str]=32			,lowerCamelCase__ :    Optional[Any]=10240			,lowerCamelCase__ :    Tuple="gelu"			,lowerCamelCase__ :    int=0.1			,lowerCamelCase__ :    int=0.1			,lowerCamelCase__ :    Optional[int]=514			,lowerCamelCase__ :    List[str]=1			,lowerCamelCase__ :    Dict=0.0_2			,lowerCamelCase__ :    Any=1E-05			,lowerCamelCase__ :    Union[str, Any]=1			,lowerCamelCase__ :    str=0			,lowerCamelCase__ :    Tuple=2			,lowerCamelCase__ :    Union[str, Any]="absolute"			,lowerCamelCase__ :    Optional[Any]=True			,lowerCamelCase__ :    List[str]=None			,**lowerCamelCase__ :    Dict			,):







							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__			,bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,**lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       hidden_size
							_UpperCamelCase :		str			    =       num_hidden_layers
							_UpperCamelCase :		str			    =       num_attention_heads
							_UpperCamelCase :		Any			    =       hidden_act
							_UpperCamelCase :		Dict			    =       intermediate_size
							_UpperCamelCase :		Optional[int]			    =       hidden_dropout_prob
							_UpperCamelCase :		Any			    =       attention_probs_dropout_prob
							_UpperCamelCase :		List[Any]			    =       max_position_embeddings
							_UpperCamelCase :		str			    =       type_vocab_size
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		Optional[int]			    =       layer_norm_eps
							_UpperCamelCase :		Optional[int]			    =       position_embedding_type
							_UpperCamelCase :		Optional[Any]			    =       use_cache
							_UpperCamelCase :		Optional[Any]			    =       classifier_dropout






class 							lowercase__     (							lowercase       ):





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							if self.task == "multiple-choice":
													_UpperCamelCase :		Union[str, Any]			    =       {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
													_UpperCamelCase :		Optional[Any]			    =       {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]			)


