





"""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 					_lowerCAmelCase   (     a  ,   unittest.TestCase       ):
       """simple docstring"""




       __magic_name__     :Union[str, Any]		  =					"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""






       def      snake_case						(    self    , __UpperCAmelCase=0      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							np.random.RandomState(__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							{
             '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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[int]											=							self.get_dummy_inputs()
         lowerCAmelCase__			:Union[str, Any]											=							pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:str											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__			:Any											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         lowerCAmelCase__			:Dict											=							PNDMScheduler.from_config(pipe.scheduler.config    , skip_prk_steps=__UpperCAmelCase      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:Union[str, Any]											=							self.get_dummy_inputs()
         lowerCAmelCase__			:Tuple											=							pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:Optional[Any]											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__			:List[Any]											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         lowerCAmelCase__			:Any											=							LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:int											=							self.get_dummy_inputs()
         lowerCAmelCase__			:List[Any]											=							pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:List[Any]											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__			:Optional[Any]											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         lowerCAmelCase__			:str											=							EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							self.get_dummy_inputs()
         lowerCAmelCase__			:List[Any]											=							pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:List[str]											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__			:Union[str, Any]											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         lowerCAmelCase__			:Optional[int]											=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							self.get_dummy_inputs()
         lowerCAmelCase__			:Union[str, Any]											=							pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:int											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__			:Tuple											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         lowerCAmelCase__			:int											=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:Union[str, Any]											=							self.get_dummy_inputs()
         lowerCAmelCase__			:Dict											=							pipe(**__UpperCAmelCase      ).images
         lowerCAmelCase__			:Optional[Any]											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         lowerCAmelCase__			:List[Any]											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							self.get_dummy_inputs()
         lowerCAmelCase__			:List[str]											=							3 * [inputs['prompt']]

         # forward
         lowerCAmelCase__			:Dict											=							pipe(**__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							output.images[0, -3:, -3:, -1]

         lowerCAmelCase__			:List[Any]											=							self.get_dummy_inputs()
         lowerCAmelCase__			:Optional[Any]											=							3 * [inputs.pop('prompt'      )]

         lowerCAmelCase__			:Tuple											=							pipe.tokenizer(
             __UpperCAmelCase    , padding='max_length'    , max_length=pipe.tokenizer.model_max_length    , truncation=__UpperCAmelCase    , return_tensors='np'    , )
         lowerCAmelCase__			:Optional[Any]											=							text_inputs['input_ids']

         lowerCAmelCase__			:Optional[Any]											=							pipe.text_encoder(input_ids=text_inputs.astype(np.intaa      )      )[0]

         lowerCAmelCase__			:Tuple											=							prompt_embeds

         # forward
         lowerCAmelCase__			:Any											=							pipe(**__UpperCAmelCase      )
         lowerCAmelCase__			:List[str]											=							output.images[0, -3:, -3:, -1]

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint    , provider='CPUExecutionProvider'      )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[Any]											=							self.get_dummy_inputs()
         lowerCAmelCase__			:Optional[Any]											=							3 * ['this is a negative prompt']
         lowerCAmelCase__			:Optional[Any]											=							negative_prompt
         lowerCAmelCase__			:List[Any]											=							3 * [inputs['prompt']]

         # forward
         lowerCAmelCase__			:Optional[Any]											=							pipe(**__UpperCAmelCase      )
         lowerCAmelCase__			:List[Any]											=							output.images[0, -3:, -3:, -1]

         lowerCAmelCase__			:str											=							self.get_dummy_inputs()
         lowerCAmelCase__			:int											=							3 * [inputs.pop('prompt'      )]

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

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

         lowerCAmelCase__							,				lowerCAmelCase__			:str											=							embeds

         # forward
         lowerCAmelCase__			:str											=							pipe(**__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

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






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							ort.SessionOptions()
         lowerCAmelCase__			:str											=							False
         return options






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:Optional[Any]											=							'A painting of a squirrel eating a burger'
         np.random.seed(0      )
         lowerCAmelCase__			:Optional[Any]											=							sd_pipe([prompt]    , guidance_scale=6.0    , num_inference_steps=1_0    , output_type='np'      )
         lowerCAmelCase__			:Optional[Any]											=							output.images

         lowerCAmelCase__			:Optional[int]											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__			:Tuple											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							DDIMScheduler.from_pretrained(
             'runwayml/stable-diffusion-v1-5'    , subfolder='scheduler'    , revision='onnx'      )
         lowerCAmelCase__			:List[str]											=							OnnxStableDiffusionPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'    , revision='onnx'    , scheduler=__UpperCAmelCase    , safety_checker=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase    , provider=self.gpu_provider    , sess_options=self.gpu_options    , )
         sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							'open neural network exchange'
         lowerCAmelCase__			:Any											=							np.random.RandomState(0      )
         lowerCAmelCase__			:Optional[int]											=							sd_pipe([prompt]    , guidance_scale=7.5    , num_inference_steps=1_0    , generator=__UpperCAmelCase    , output_type='np'      )
         lowerCAmelCase__			:int											=							output.images
         lowerCAmelCase__			:Union[str, Any]											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__			:Dict											=							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      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:Union[str, Any]											=							'open neural network exchange'
         lowerCAmelCase__			:str											=							np.random.RandomState(0      )
         lowerCAmelCase__			:Optional[Any]											=							sd_pipe([prompt]    , guidance_scale=7.5    , num_inference_steps=1_0    , generator=__UpperCAmelCase    , output_type='np'      )
         lowerCAmelCase__			:Any											=							output.images
         lowerCAmelCase__			:Optional[int]											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__			:List[str]											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							0

         def test_callback_fn(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ) -> None:
           lowerCAmelCase__			:Optional[int]											=							True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 0:
             assert latents.shape == (1, 4, 6_4, 6_4)
             lowerCAmelCase__			:Optional[Any]											=							latents[0, -3:, -3:, -1]
             lowerCAmelCase__			:Optional[int]											=							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, 6_4, 6_4)
             lowerCAmelCase__			:List[str]											=							latents[0, -3:, -3:, -1]
             lowerCAmelCase__			:Union[str, Any]											=							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

         lowerCAmelCase__			:Any											=							False

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

         lowerCAmelCase__			:Optional[Any]											=							'Andromeda galaxy in a bottle'

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							OnnxStableDiffusionPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'    , revision='onnx'    , safety_checker=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase    , provider=self.gpu_provider    , sess_options=self.gpu_options    , )
         assert isinstance(__UpperCAmelCase    , __UpperCAmelCase      )
         assert pipe.safety_checker is None

         lowerCAmelCase__			:int											=							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(__UpperCAmelCase      )
           lowerCAmelCase__			:Tuple											=							OnnxStableDiffusionPipeline.from_pretrained(__UpperCAmelCase      )

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







"""simple docstring"""





def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->float:





  """simple docstring"""


  if discount_rate < 0:
    raise ValueError('Discount rate cannot be negative'			)
  if not cash_flows:
    raise ValueError('Cash flows list cannot be empty'			)
  lowerCAmelCase__			:Union[str, Any]											=							sum(
      cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_SCREAMING_SNAKE_CASE			)			)
  return round(_SCREAMING_SNAKE_CASE     ,   ndigits=2			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
