






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







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       List[str]=0						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  np.random.RandomState(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       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  __lowercase (		self :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      UpperCamelCase__      :       Any	    =  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  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      UpperCamelCase__      :       str	    =  PNDMScheduler.from_config(pipe.scheduler.config							,       skip_prk_steps=SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      UpperCamelCase__      :       Union[str, Any]	    =  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  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      UpperCamelCase__      :       Optional[Any]	    =  LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       int	    =  self.get_dummy_inputs()
      UpperCamelCase__      :       Optional[Any]	    =  pipe(**SCREAMING_SNAKE_CASE						).images
      UpperCamelCase__      :       Any	    =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      UpperCamelCase__      :       Dict	    =  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  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      UpperCamelCase__      :       str	    =  EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       List[str]	    =  self.get_dummy_inputs()
      UpperCamelCase__      :       int	    =  pipe(**SCREAMING_SNAKE_CASE						).images
      UpperCamelCase__      :       Optional[int]	    =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      UpperCamelCase__      :       List[str]	    =  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  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      UpperCamelCase__      :       List[Any]	    =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  self.get_dummy_inputs()
      UpperCamelCase__      :       Optional[int]	    =  pipe(**SCREAMING_SNAKE_CASE						).images
      UpperCamelCase__      :       Any	    =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_28, 1_28, 3)
      UpperCamelCase__      :       Optional[int]	    =  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  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      UpperCamelCase__      :       Any	    =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

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

      assert image.shape == (1, 1_28, 1_28, 3)
      UpperCamelCase__      :       int	    =  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  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  self.get_dummy_inputs()
      UpperCamelCase__      :       int	    =  3 * [inputs["prompt"]]

      # forward
      UpperCamelCase__      :       int	    =  pipe(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       int	    =  output.images[0, -3:, -3:, -1]

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

      UpperCamelCase__      :       Tuple	    =  pipe.tokenizer(
          SCREAMING_SNAKE_CASE							,       padding="max_length"							,       max_length=pipe.tokenizer.model_max_length							,       truncation=SCREAMING_SNAKE_CASE							,       return_tensors="np"							,       )
      UpperCamelCase__      :       Dict	    =  text_inputs["input_ids"]

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

      UpperCamelCase__      :       Optional[Any]	    =  prompt_embeds

      # forward
      UpperCamelCase__      :       Optional[int]	    =  pipe(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[int]	    =  output.images[0, -3:, -3:, -1]

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







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[Any]	    =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,       provider="CPUExecutionProvider"						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       str	    =  self.get_dummy_inputs()
      UpperCamelCase__      :       Any	    =  3 * ["this is a negative prompt"]
      UpperCamelCase__      :       Optional[Any]	    =  negative_prompt
      UpperCamelCase__      :       Union[str, Any]	    =  3 * [inputs["prompt"]]

      # forward
      UpperCamelCase__      :       Union[str, Any]	    =  pipe(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  output.images[0, -3:, -3:, -1]

      UpperCamelCase__      :       List[str]	    =  self.get_dummy_inputs()
      UpperCamelCase__      :       Union[str, Any]	    =  3 * [inputs.pop("prompt"						)]

      UpperCamelCase__      :       List[Any]	    =  []
      for p in [prompt, negative_prompt]:
           UpperCamelCase__      :       Optional[Any]	    =  pipe.tokenizer(
               SCREAMING_SNAKE_CASE							,       padding="max_length"							,       max_length=pipe.tokenizer.model_max_length							,       truncation=SCREAMING_SNAKE_CASE							,       return_tensors="np"							,       )
           UpperCamelCase__      :       Optional[Any]	    =  text_inputs["input_ids"]

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

      UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  embeds

      # forward
      UpperCamelCase__      :       Dict	    =  pipe(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       List[Any]	    =  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  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






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







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  ort.SessionOptions()
      UpperCamelCase__      :       int	    =  False
      return options







 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  OnnxStableDiffusionPipeline.from_pretrained(
          "CompVis/stable-diffusion-v1-4"							,       revision="onnx"							,       safety_checker=SCREAMING_SNAKE_CASE							,       feature_extractor=SCREAMING_SNAKE_CASE							,       provider=self.gpu_provider							,       sess_options=self.gpu_options							,       )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

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

      UpperCamelCase__      :       Union[str, Any]	    =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Optional[Any]	    =  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  __lowercase (		self :       Any						):


      '''simple docstring'''






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

      UpperCamelCase__      :       Any	    =  "open neural network exchange"
      UpperCamelCase__      :       int	    =  np.random.RandomState(0						)
      UpperCamelCase__      :       str	    =  sd_pipe([prompt]							,       guidance_scale=7.5							,       num_inference_steps=10							,       generator=SCREAMING_SNAKE_CASE							,       output_type="np"						)
      UpperCamelCase__      :       Dict	    =  output.images
      UpperCamelCase__      :       Any	    =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       Tuple	    =  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  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  LMSDiscreteScheduler.from_pretrained(
          "runwayml/stable-diffusion-v1-5"							,       subfolder="scheduler"							,       revision="onnx"						)
      UpperCamelCase__      :       Optional[Any]	    =  OnnxStableDiffusionPipeline.from_pretrained(
          "runwayml/stable-diffusion-v1-5"							,       revision="onnx"							,       scheduler=SCREAMING_SNAKE_CASE							,       safety_checker=SCREAMING_SNAKE_CASE							,       feature_extractor=SCREAMING_SNAKE_CASE							,       provider=self.gpu_provider							,       sess_options=self.gpu_options							,       )
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       int	    =  "open neural network exchange"
      UpperCamelCase__      :       List[str]	    =  np.random.RandomState(0						)
      UpperCamelCase__      :       Optional[Any]	    =  sd_pipe([prompt]							,       guidance_scale=7.5							,       num_inference_steps=10							,       generator=SCREAMING_SNAKE_CASE							,       output_type="np"						)
      UpperCamelCase__      :       Any	    =  output.images
      UpperCamelCase__      :       Dict	    =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__      :       int	    =  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  __lowercase (		self :       str						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  0

      def test_callback_fn(SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       np.ndarray						) -> None:
           UpperCamelCase__      :       Union[str, Any]	    =  True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 0:
                assert latents.shape == (1, 4, 64, 64)
                UpperCamelCase__      :       int	    =  latents[0, -3:, -3:, -1]
                UpperCamelCase__      :       Optional[Any]	    =  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)
                UpperCamelCase__      :       List[Any]	    =  latents[0, -3:, -3:, -1]
                UpperCamelCase__      :       Optional[int]	    =  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

      UpperCamelCase__      :       List[str]	    =  False

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

      UpperCamelCase__      :       str	    =  "Andromeda galaxy in a bottle"

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







 def  __lowercase (		self :       str						):


      '''simple docstring'''






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

      UpperCamelCase__      :       str	    =  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(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__      :       Optional[int]	    =  OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE						)

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       int	    =  1
     for i in range(1 ,   num + 1      ):
          fact *= i
     return fact







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     int:
     UpperCamelCase__      :       List[Any]	    =  0
     while number > 0:
          UpperCamelCase__      :       List[Any]	    =  number % 10
          sum_of_digits += last_digit
          UpperCamelCase__      :       int	    =  number // 10  # Removing the last_digit from the given number
     return sum_of_digits







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase = 100      )			->     int:
     UpperCamelCase__      :       Optional[Any]	    =  factorial(__lowerCAmelCase      )
     UpperCamelCase__      :       Optional[int]	    =  split_and_add(__lowerCAmelCase      )
     return result


if __name__ == "__main__":
 print(solution(int(input('''Enter the Number: ''').strip())))