

'''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 				A__       (			__snake_case	,  unittest.TestCase    ):
  A__											=							'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





  def 		A    (						self :      List[str]				,   _a :      Union[str, Any]=0					)      ->       Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =np.random.RandomState(snake_case_					)
       _SCREAMING_SNAKE_CASE				       ={
           '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 :      List[Any]					)      ->       List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       pipe.set_progress_bar_config(disable=snake_case_					)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]					)

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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       _SCREAMING_SNAKE_CASE				       =PNDMScheduler.from_config(pipe.scheduler.config				,   skip_prk_steps=snake_case_					)
       pipe.set_progress_bar_config(disable=snake_case_					)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]					)

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





  def 		A    (						self :      str					)      ->       Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       _SCREAMING_SNAKE_CASE				       =LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=snake_case_					)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]					)

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





  def 		A    (						self :      str					)      ->       Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       _SCREAMING_SNAKE_CASE				       =EulerDiscreteScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=snake_case_					)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]					)

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





  def 		A    (						self :      int					)      ->       int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       _SCREAMING_SNAKE_CASE				       =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=snake_case_					)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]					)

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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       _SCREAMING_SNAKE_CASE				       =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
       pipe.set_progress_bar_config(disable=snake_case_					)

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

       assert image.shape == (1, 128, 128, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]					)

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





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       pipe.set_progress_bar_config(disable=snake_case_					)

       _SCREAMING_SNAKE_CASE				       =self.get_dummy_inputs()
       _SCREAMING_SNAKE_CASE				       =3 * [inputs['prompt']]

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

       _SCREAMING_SNAKE_CASE				       =self.get_dummy_inputs()
       _SCREAMING_SNAKE_CASE				       =3 * [inputs.pop('prompt'					)]

       _SCREAMING_SNAKE_CASE				       =pipe.tokenizer(
           snake_case_				,   padding='max_length'				,   max_length=pipe.tokenizer.model_max_length				,   truncation=snake_case_				,   return_tensors='np'				,   )
       _SCREAMING_SNAKE_CASE				       =text_inputs['input_ids']

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

       _SCREAMING_SNAKE_CASE				       =prompt_embeds

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

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





  def 		A    (						self :      List[Any]					)      ->       Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint				,   provider='CPUExecutionProvider'					)
       pipe.set_progress_bar_config(disable=snake_case_					)

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

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

       _SCREAMING_SNAKE_CASE				       =self.get_dummy_inputs()
       _SCREAMING_SNAKE_CASE				       =3 * [inputs.pop('prompt'					)]

       _SCREAMING_SNAKE_CASE				       =[]
       for p in [prompt, negative_prompt]:
            _SCREAMING_SNAKE_CASE				       =pipe.tokenizer(
                snake_case_				,   padding='max_length'				,   max_length=pipe.tokenizer.model_max_length				,   truncation=snake_case_				,   return_tensors='np'				,   )
            _SCREAMING_SNAKE_CASE				       =text_inputs['input_ids']

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

       _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE				       =embeds

       # forward
       _SCREAMING_SNAKE_CASE				       =pipe(**snake_case_					)
       _SCREAMING_SNAKE_CASE				       =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 				A__       (			unittest.TestCase    ):





  @property
  def 		A    (						self :      Tuple					)      ->       Tuple:





       '''simple docstring'''






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





  @property
  def 		A    (						self :      List[Any]					)      ->       Tuple:





       '''simple docstring'''






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





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





       '''simple docstring'''






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

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

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

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]					)

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





  def 		A    (						self :      Tuple					)      ->       Any:





       '''simple docstring'''






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

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

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]					)

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





  def 		A    (						self :      int					)      ->       List[str]:





       '''simple docstring'''






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

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

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE				       =np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]					)

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





  def 		A    (						self :      Any					)      ->       int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE				       =0

       def test_callback_fn(_a :      List[str]				,   _a :      Any				,   _a :      Tuple					) -> None:
            _SCREAMING_SNAKE_CASE				       =True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 0:
                 assert latents.shape == (1, 4, 64, 64)
                 _SCREAMING_SNAKE_CASE				       =latents[0, -3:, -3:, -1]
                 _SCREAMING_SNAKE_CASE				       =np.array(
                     [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]					)

                 assert np.abs(latents_slice.flatten() - expected_slice					).max() < 1e-3
            elif step == 5:
                 assert latents.shape == (1, 4, 64, 64)
                 _SCREAMING_SNAKE_CASE				       =latents[0, -3:, -3:, -1]
                 _SCREAMING_SNAKE_CASE				       =np.array(
                     [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]					)

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

       _SCREAMING_SNAKE_CASE				       =False

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

       _SCREAMING_SNAKE_CASE				       ='Andromeda galaxy in a bottle'

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





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





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE				       =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(snake_case_					)
            _SCREAMING_SNAKE_CASE				       =OnnxStableDiffusionPipeline.from_pretrained(snake_case_					)

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







from collections.abc import Callable





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    _A     =							a
    _A     =							b
    if function(_SCREAMING_SNAKE_CASE				) == 0:  # one of the a or b is a root for the function
        return a
    elif function(_SCREAMING_SNAKE_CASE				) == 0:
        return b
    elif (
        function(_SCREAMING_SNAKE_CASE				) * function(_SCREAMING_SNAKE_CASE				) > 0
    ):  # if none of these are root and they are both positive or negative,
        # then this algorithm can't find the root
        raise ValueError('could not find root in given interval.'				)
    else:
        _A     =							start + (end - start) / 2.0
        while abs(start - mid				) > 10**-7:  # until precisely equals to 10^-7
            if function(_SCREAMING_SNAKE_CASE				) == 0:
                return mid
            elif function(_SCREAMING_SNAKE_CASE				) * function(_SCREAMING_SNAKE_CASE				) < 0:
                _A     =							mid
            else:
                _A     =							mid
            _A     =							start + (end - start) / 2.0
        return mid





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return x**3 - 2 * x - 5


if __name__ == "__main__":
 print(bisection(f, 1, 1_000))

 import doctest

 doctest.testmod()





