'''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 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     pass






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







     """simple docstring"""






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







     @property
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  ort.SessionOptions()
         __UpperCamelCase	:	List[str]		    =  False
         return options







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'    )
         __UpperCamelCase	:	Dict		    =  load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'    )
         __UpperCamelCase	:	List[str]		    =  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	:	str		    =  'A red cat sitting on a park bench'

         __UpperCamelCase	:	Optional[Any]		    =  np.random.RandomState(0    )
         __UpperCamelCase	:	int		    =  pipe(
             prompt=_lowerCamelCase     ,			image=_lowerCamelCase     ,			mask_image=_lowerCamelCase     ,			guidance_scale=7.5     ,			num_inference_steps=1_0     ,			generator=_lowerCamelCase     ,			output_type='np'     ,			)
         __UpperCamelCase	:	str		    =  output.images
         __UpperCamelCase	:	List[Any]		    =  images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __UpperCamelCase	:	Optional[Any]		    =  np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]    )

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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[str]		    =  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	:	Dict		    =  LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'     ,			subfolder='scheduler'     ,			revision='onnx'    )
         __UpperCamelCase	:	List[Any]		    =  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	:	str		    =  'A red cat sitting on a park bench'

         __UpperCamelCase	:	Optional[Any]		    =  np.random.RandomState(0    )
         __UpperCamelCase	:	Union[str, Any]		    =  pipe(
             prompt=_lowerCamelCase     ,			image=_lowerCamelCase     ,			mask_image=_lowerCamelCase     ,			guidance_scale=7.5     ,			num_inference_steps=2_0     ,			generator=_lowerCamelCase     ,			output_type='np'     ,			)
         __UpperCamelCase	:	Optional[int]		    =  output.images
         __UpperCamelCase	:	Dict		    =  images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         __UpperCamelCase	:	Tuple		    =  np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]    )

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




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a=  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



