
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   ):





   '''simple docstring'''
   # FIXME: add fast tests
   pass






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





   '''simple docstring'''


   @property
   def  __magic_name__				(					self     :							str     ):

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


   @property
   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Tuple					=       ort.SessionOptions()
      _A:  int					=       False
      return options


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Optional[int]					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/in_paint/overture-creations-5sI6fQgYIuo.png'''     )
      _A:  int					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''     )
      _A:  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_     )

      _A:  List[Any]					=       'A red cat sitting on a park bench'

      _A:  Dict					=       np.random.RandomState(0     )
      _A:  Optional[int]					=       pipe(
          prompt=lowerCAmelCase_			,			image=lowerCAmelCase_			,			mask_image=lowerCAmelCase_			,			guidance_scale=7.5			,			num_inference_steps=1_0			,			generator=lowerCAmelCase_			,			output_type='''np'''			,			)
      _A:  Any					=       output.images
      _A:  Union[str, 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)
      _A:  Union[str, Any]					=       np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]     )

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


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Any					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/in_paint/overture-creations-5sI6fQgYIuo.png'''     )
      _A:  Optional[Any]					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''     )
      _A:  Tuple					=       LMSDiscreteScheduler.from_pretrained(
          '''runwayml/stable-diffusion-inpainting'''			,			subfolder='''scheduler'''			,			revision='''onnx'''     )
      _A:  str					=       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_     )

      _A:  str					=       'A red cat sitting on a park bench'

      _A:  Tuple					=       np.random.RandomState(0     )
      _A:  int					=       pipe(
          prompt=lowerCAmelCase_			,			image=lowerCAmelCase_			,			mask_image=lowerCAmelCase_			,			guidance_scale=7.5			,			num_inference_steps=2_0			,			generator=lowerCAmelCase_			,			output_type='''np'''			,			)
      _A:  str					=       output.images
      _A:  Tuple					=       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)
      _A:  Dict					=       np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]     )

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










from __future__ import annotations


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     list[tuple[int, int]]:
    lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		position
    lowerCamelCase__    :      Optional[Any]					 =		[
        (y + 1, x + 2),
        (y - 1, x + 2),
        (y + 1, x - 2),
        (y - 1, x - 2),
        (y + 2, x + 1),
        (y + 2, x - 1),
        (y - 2, x + 1),
        (y - 2, x - 1),
    ]
    lowerCamelCase__    :      Dict					 =		[]

    for position in positions:
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(_UpperCAmelCase )

    return permissible_positions


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:
    return not any(elem == 0 for row in board for elem in row )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     bool:

    if is_complete(_UpperCAmelCase ):
        return True

    for position in get_valid_pos(_UpperCAmelCase						,	len(_UpperCAmelCase ) ):
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position

        if board[y][x] == 0:
            lowerCamelCase__    :      List[Any]					 =		curr + 1
            if open_knight_tour_helper(_UpperCAmelCase						,	_UpperCAmelCase						,	curr + 1 ):
                return True
            lowerCamelCase__    :      Optional[Any]					 =		0

    return False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[list[int]]:
    lowerCamelCase__    :      Any					 =		[[0 for i in range(_UpperCAmelCase )] for j in range(_UpperCAmelCase )]

    for i in range(_UpperCAmelCase ):
        for j in range(_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[int]					 =		1
            if open_knight_tour_helper(_UpperCAmelCase						,	(i, j)						,	1 ):
                return board
            lowerCamelCase__    :      Optional[Any]					 =		0

    lowerCamelCase__    :      Any					 =		F"""Open Kight Tour cannot be performed on a board of size {n}"""
    raise ValueError(_UpperCAmelCase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





