

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


A_							:			List[Any]											= logging.get_logger(__name__)

A_							:			Optional[Any]											= {
    "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    lowerCamelCase						(A__			):
   lowerCamelCase__   :		Optional[Any]							=							'roberta'

   def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						Dict=5_0_2_6_5    ,				__UpperCAmelCase				:						List[Any]=7_6_8    ,				__UpperCAmelCase				:						Tuple=1_2    ,				__UpperCAmelCase				:						List[str]=1_2    ,				__UpperCAmelCase				:						Optional[int]=3_0_7_2    ,				__UpperCAmelCase				:						Any="gelu"    ,				__UpperCAmelCase				:						Tuple=0.1    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=5_1_2    ,				__UpperCAmelCase				:						Union[str, Any]=2    ,				__UpperCAmelCase				:						str=0.02    ,				__UpperCAmelCase				:						Optional[Any]=1e-12    ,				__UpperCAmelCase				:						int=1    ,				__UpperCAmelCase				:						int=0    ,				__UpperCAmelCase				:						Optional[Any]=2    ,				__UpperCAmelCase				:						Union[str, Any]="absolute"    ,				__UpperCAmelCase				:						Optional[int]=True    ,				__UpperCAmelCase				:						Tuple=None    ,				**__UpperCAmelCase				:						Any    ,				)   ->    Tuple:
      super().__init__(pad_token_id=__UpperCAmelCase    ,				bos_token_id=__UpperCAmelCase    ,				eos_token_id=__UpperCAmelCase    ,				**__UpperCAmelCase       )

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





class    lowerCamelCase						(A__			):

   @property
   def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE__								=      {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         SCREAMING_SNAKE_CASE__								=      {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    lowerCamelCase						(unittest.TestCase			):

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

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ort.SessionOptions()
						SCREAMING_SNAKE_CASE__								=      False
						return options





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						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=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

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

						SCREAMING_SNAKE_CASE__								=      np.random.RandomState(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(
						    prompt=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				strength=0.75    ,				guidance_scale=7.5    ,				num_inference_steps=1_5    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 1e-2


