



"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''roberta'''



       def __init__(							self      :  int		,    _UpperCAmelCase      :  List[Any]=50265		,    _UpperCAmelCase      :  str=768		,    _UpperCAmelCase      :  List[str]=12		,    _UpperCAmelCase      :  int=12		,    _UpperCAmelCase      :  Tuple=3072		,    _UpperCAmelCase      :  Dict="gelu"		,    _UpperCAmelCase      :  int=0.1		,    _UpperCAmelCase      :  Optional[int]=0.1		,    _UpperCAmelCase      :  Optional[int]=512		,    _UpperCAmelCase      :  Optional[Any]=2		,    _UpperCAmelCase      :  Optional[int]=0.02		,    _UpperCAmelCase      :  Optional[Any]=1e-12		,    _UpperCAmelCase      :  Dict=1		,    _UpperCAmelCase      :  str=0		,    _UpperCAmelCase      :  int=2		,    _UpperCAmelCase      :  Tuple="absolute"		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  int=None		,    **_UpperCAmelCase      :  List[str]		,    )			->						Any:


             '''simple docstring'''




             super().__init__(pad_token_id=_UpperCAmelCase		,    bos_token_id=_UpperCAmelCase		,    eos_token_id=_UpperCAmelCase		,    **_UpperCAmelCase   )

             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							type_vocab_size
             UpperCAmelCase_      			=							initializer_range
             UpperCAmelCase_      			=							layer_norm_eps
             UpperCAmelCase_      			=							position_embedding_type
             UpperCAmelCase_      			=							use_cache
             UpperCAmelCase_      			=							classifier_dropout



class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  List[Any]   )			->						Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_      			=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_      			=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]   )









"""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				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




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



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ort.SessionOptions()
             UpperCAmelCase_      			=							False
             return options







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png"   )
             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"   )
             UpperCAmelCase_      			=							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_      			=							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   )

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

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

             UpperCAmelCase_      			=							output.images[0]

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




