






'''simple docstring'''





import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, 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'''

    lowerCAmelCase__      				=							"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]=0     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     np.random.RandomState(__lowerCamelCase     )
     lowerCamelCase__  =     {
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 7.5,
         "output_type": "numpy",
     }
     return inputs

    def    a__				(					self     :     Optional[Any]     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     pipe(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowerCamelCase__  =     np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    def    a__				(					self     :     str     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     lowerCamelCase__  =     PNDMScheduler.from_config(pipe.scheduler.config				,     skip_prk_steps=__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     pipe(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowerCamelCase__  =     np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    def    a__				(					self     :     Any     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     lowerCamelCase__  =     LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     pipe(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowerCamelCase__  =     np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    def    a__				(					self     :     List[str]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     lowerCamelCase__  =     EulerDiscreteScheduler.from_config(pipe.scheduler.config     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     pipe(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowerCamelCase__  =     np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    def    a__				(					self     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     lowerCamelCase__  =     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     pipe(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowerCamelCase__  =     np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    def    a__				(					self     :     Dict     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     lowerCamelCase__  =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     pipe(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     lowerCamelCase__  =     np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2

    def    a__				(					self     :     str     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     3 * [inputs["prompt"]]

     # forward
     lowerCamelCase__  =     pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.images[0, -3:, -3:, -1]

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     3 * [inputs.pop("prompt"     )]

     lowerCamelCase__  =     pipe.tokenizer(
         __lowerCamelCase				,     padding="max_length"				,     max_length=pipe.tokenizer.model_max_length				,     truncation=__lowerCamelCase				,     return_tensors="np"				,     )
     lowerCamelCase__  =     text_inputs["input_ids"]

     lowerCamelCase__  =     pipe.text_encoder(input_ids=text_inputs.astype(np.intaa     )     )[0]

     lowerCamelCase__  =     prompt_embeds

     # forward
     lowerCamelCase__  =     pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.images[0, -3:, -3:, -1]

     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()     ).max() < 1E-4




    def    a__				(					self     :     Tuple     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,     provider="CPUExecutionProvider"     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     3 * ["this is a negative prompt"]
     lowerCamelCase__  =     negative_prompt
     lowerCamelCase__  =     3 * [inputs["prompt"]]

     # forward
     lowerCamelCase__  =     pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.images[0, -3:, -3:, -1]

     lowerCamelCase__  =     self.get_dummy_inputs()
     lowerCamelCase__  =     3 * [inputs.pop("prompt"     )]

     lowerCamelCase__  =     []
     for p in [prompt, negative_prompt]:
      lowerCamelCase__  =     pipe.tokenizer(
          __lowerCamelCase				,     padding="max_length"				,     max_length=pipe.tokenizer.model_max_length				,     truncation=__lowerCamelCase				,     return_tensors="np"				,     )
      lowerCamelCase__  =     text_inputs["input_ids"]

      embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa     )     )[0]     )

     lowerCamelCase__  ,	lowerCamelCase__  =     embeds

     # forward
     lowerCamelCase__  =     pipe(**__lowerCamelCase     )
     lowerCamelCase__  =     output.images[0, -3:, -3:, -1]

     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()     ).max() < 1E-4
@nightly
@require_onnxruntime
@require_torch_gpu
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




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

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

     '''simple docstring'''




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

    def    a__				(					self     :     Union[str, Any]     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
         "CompVis/stable-diffusion-v1-4"				,     revision="onnx"				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     "A painting of a squirrel eating a burger"
     np.random.seed(0     )
     lowerCamelCase__  =     sd_pipe([prompt]				,     guidance_scale=6.0				,     num_inference_steps=10				,     output_type="np"     )
     lowerCamelCase__  =     output.images

     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__  =     np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]     )

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

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

     '''simple docstring'''




     lowerCamelCase__  =     DDIMScheduler.from_pretrained(
         "runwayml/stable-diffusion-v1-5"				,     subfolder="scheduler"				,     revision="onnx"     )
     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     scheduler=__lowerCamelCase				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     "open neural network exchange"
     lowerCamelCase__  =     np.random.RandomState(0     )
     lowerCamelCase__  =     sd_pipe([prompt]				,     guidance_scale=7.5				,     num_inference_steps=10				,     generator=__lowerCamelCase				,     output_type="np"     )
     lowerCamelCase__  =     output.images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__  =     np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]     )

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

    def    a__				(					self     :     List[str]     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     LMSDiscreteScheduler.from_pretrained(
         "runwayml/stable-diffusion-v1-5"				,     subfolder="scheduler"				,     revision="onnx"     )
     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     scheduler=__lowerCamelCase				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     "open neural network exchange"
     lowerCamelCase__  =     np.random.RandomState(0     )
     lowerCamelCase__  =     sd_pipe([prompt]				,     guidance_scale=7.5				,     num_inference_steps=10				,     generator=__lowerCamelCase				,     output_type="np"     )
     lowerCamelCase__  =     output.images
     lowerCamelCase__  =     image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     lowerCamelCase__  =     np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]     )

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

    def    a__				(					self     :     Optional[int]     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     0

     def test_callback_fn(__lowerCamelCase     :     int				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     np.ndarray     ) -> None:
      lowerCamelCase__  =     True
      nonlocal number_of_steps
      number_of_steps += 1
      if step == 0:
       assert latents.shape == (1, 4, 64, 64)
       lowerCamelCase__  =     latents[0, -3:, -3:, -1]
       lowerCamelCase__  =     np.array(
           [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]     )

       assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 1E-3
      elif step == 5:
       assert latents.shape == (1, 4, 64, 64)
       lowerCamelCase__  =     latents[0, -3:, -3:, -1]
       lowerCamelCase__  =     np.array(
           [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]     )

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

     lowerCamelCase__  =     False

     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     "Andromeda galaxy in a bottle"

     lowerCamelCase__  =     np.random.RandomState(0     )
     pipe(
         prompt=__lowerCamelCase				,     num_inference_steps=5				,     guidance_scale=7.5				,     generator=__lowerCamelCase				,     callback=__lowerCamelCase				,     callback_steps=1				,     )
     assert test_callback_fn.has_been_called
     assert number_of_steps == 6




    def    a__				(					self     :     int     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(
         "runwayml/stable-diffusion-v1-5"				,     revision="onnx"				,     safety_checker=__lowerCamelCase				,     feature_extractor=__lowerCamelCase				,     provider=self.gpu_provider				,     sess_options=self.gpu_options				,     )
     assert isinstance(__lowerCamelCase				,     __lowerCamelCase     )
     assert pipe.safety_checker is None

     lowerCamelCase__  =     pipe("example prompt"				,     num_inference_steps=2     ).images[0]
     assert image is not None

     # check that there's no error when saving a pipeline with one of the models being None
     with tempfile.TemporaryDirectory() as tmpdirname:
      pipe.save_pretrained(__lowerCamelCase     )
      lowerCamelCase__  =     OnnxStableDiffusionPipeline.from_pretrained(__lowerCamelCase     )

     # sanity check that the pipeline still works
     assert pipe.safety_checker is None
     lowerCamelCase__  =     pipe("example prompt"				,     num_inference_steps=2     ).images[0]
     assert image is not None









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 def update_area_of_max_square(lowercase__				,     lowercase__) -> int:
  # BASE CASE
  if row >= rows or col >= cols:
   return 0

  lowerCamelCase__  =     update_area_of_max_square(lowercase__				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     lowercase__)

  if mat[row][col]:
   lowerCamelCase__  =     1 + min([right, diagonal, down])
   lowerCamelCase__  =     max(largest_square_area[0]				,     lowercase__)
   return sub_problem_sol
  else:
   return 0

 lowerCamelCase__  =     [0]
 update_area_of_max_square(0				,     0)
 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 def update_area_of_max_square_using_dp_array(
     lowercase__				,     lowercase__				,     lowercase__) -> int:
  if row >= rows or col >= cols:
   return 0
  if dp_array[row][col] != -1:
   return dp_array[row][col]

  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(lowercase__				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     lowercase__				,     lowercase__)

  if mat[row][col]:
   lowerCamelCase__  =     1 + min([right, diagonal, down])
   lowerCamelCase__  =     max(largest_square_area[0]				,     lowercase__)
   lowerCamelCase__  =     sub_problem_sol
   return sub_problem_sol
  else:
   return 0

 lowerCamelCase__  =     [0]
 lowerCamelCase__  =     [[-1] * cols for _ in range(lowercase__)]
 update_area_of_max_square_using_dp_array(0				,     0				,     lowercase__)

 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [[0] * (cols + 1) for _ in range(rows + 1)]
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     dp_array[row][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(dp_array[row][col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0

 return largest_square_area






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     current_row[col + 1]
   lowerCamelCase__  =     next_row[col + 1]
   lowerCamelCase__  =     next_row[col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(current_row[col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0
  lowerCamelCase__  =     current_row

 return largest_square_area


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

