






"""simple docstring"""







import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort





class   UpperCamelCase_ (       __UpperCAmelCase   ,					unittest.TestCase  ):
  pass





@nightly
@require_onnxruntime
@require_torch_gpu
class   UpperCamelCase_ (       unittest.TestCase  ):



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



      """simple docstring"""





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



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



      """simple docstring"""





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



  def 		UpperCamelCase_				(      self				)			->			Dict:



      """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	    =			OnnxStableDiffusionInpaintPipeline.from_pretrained(
          """runwayml/stable-diffusion-inpainting""" ,		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	    =			"""A red cat sitting on a park bench"""

      UpperCAmelCase	    =			np.random.RandomState(0				)
      UpperCAmelCase	    =			pipe(
          prompt=_lowerCamelCase ,		image=_lowerCamelCase ,		mask_image=_lowerCamelCase ,		guidance_scale=7.5 ,		num_inference_steps=10 ,		generator=_lowerCamelCase ,		output_type="""np""" ,		)
      UpperCAmelCase	    =			output.images
      UpperCAmelCase	    =			images[0, 2_55:2_58, 2_55:2_58, -1]

      assert images.shape == (1, 5_12, 5_12, 3)
      UpperCAmelCase	    =			np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3



  def 		UpperCamelCase_				(      self				)			->			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	    =			LMSDiscreteScheduler.from_pretrained(
          """runwayml/stable-diffusion-inpainting""" ,		subfolder="""scheduler""" ,		revision="""onnx"""				)
      UpperCAmelCase	    =			OnnxStableDiffusionInpaintPipeline.from_pretrained(
          """runwayml/stable-diffusion-inpainting""" ,		revision="""onnx""" ,		scheduler=_lowerCamelCase ,		safety_checker=_lowerCamelCase ,		feature_extractor=_lowerCamelCase ,		provider=self.gpu_provider ,		sess_options=self.gpu_options ,		)
      pipe.set_progress_bar_config(disable=_lowerCamelCase				)

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

      UpperCAmelCase	    =			np.random.RandomState(0				)
      UpperCAmelCase	    =			pipe(
          prompt=_lowerCamelCase ,		image=_lowerCamelCase ,		mask_image=_lowerCamelCase ,		guidance_scale=7.5 ,		num_inference_steps=20 ,		generator=_lowerCamelCase ,		output_type="""np""" ,		)
      UpperCAmelCase	    =			output.images
      UpperCAmelCase	    =			images[0, 2_55:2_58, 2_55:2_58, -1]

      assert images.shape == (1, 5_12, 5_12, 3)
      UpperCAmelCase	    =			np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]				)

      assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-3








"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_	:    int            =      logging.get_logger(__name__)

lowerCAmelCase_	:    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   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'xlm-roberta-xl'



  def __init__(      self ,		snake_case__=25_08_80 ,		snake_case__=25_60 ,		snake_case__=36 ,		snake_case__=32 ,		snake_case__=1_02_40 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=5_14 ,		snake_case__=1 ,		snake_case__=0.02 ,		snake_case__=1e-05 ,		snake_case__=1 ,		snake_case__=0 ,		snake_case__=2 ,		snake_case__="absolute" ,		snake_case__=True ,		snake_case__=None ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)
         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   UpperCamelCase_ (       a_  ):



  @property
  def 		UpperCamelCase_				(      self				)			->			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),
             ]				)
