






"""simple docstring"""





def     __lowercase					(    a :				int = 10**9 )					->					int:
      __snake_case      :    List[str]  			=1
      __snake_case      :    Union[str, Any]  			=2

      __snake_case      :    int  			=0
      __snake_case      :    str  			=0
      __snake_case      :    str  			=0
      while perimeter <= max_perimeter:
            perimeters_sum += perimeter

            prev_value += 2 * value
            value += prev_value

            __snake_case      :    Tuple  			=2 * value + 2 if i % 2 == 0 else 2 * value - 2
            i += 1

      return perimeters_sum


if __name__ == "__main__":
    print(F'''{solution() = }''')










"""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						):
      _a   :     Tuple								=   '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


      def _UpperCamelCase						(    self				:	Dict							,   a				:	Optional[Any]=0						):





            """simple docstring"""







            __snake_case      :    List[str]  			=np.random.RandomState(a						)
            __snake_case      :    Union[str, 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 _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            __snake_case      :    Dict  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    Tuple  			=self.get_dummy_inputs()
            __snake_case      :    List[str]  			=pipe(**a						).images
            __snake_case      :    int  			=image[0, -3:, -3:, -1]

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





            """simple docstring"""







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

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Optional[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 _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Any  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    Any  			=LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
            pipe.set_progress_bar_config(disable=a						)

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    int  			=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 _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    Optional[Any]  			=EulerDiscreteScheduler.from_config(pipe.scheduler.config						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    str  			=self.get_dummy_inputs()
            __snake_case      :    int  			=pipe(**a						).images
            __snake_case      :    str  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Union[str, Any]  			=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 _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







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

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Tuple  			=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 _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    Tuple  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            __snake_case      :    Any  			=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
            pipe.set_progress_bar_config(disable=a						)

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

            assert image.shape == (1, 1_2_8, 1_2_8, 3)
            __snake_case      :    Dict  			=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 _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            __snake_case      :    Dict  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            pipe.set_progress_bar_config(disable=a						)

            __snake_case      :    Optional[int]  			=self.get_dummy_inputs()
            __snake_case      :    Any  			=3 * [inputs['''prompt''']]

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

            __snake_case      :    Tuple  			=self.get_dummy_inputs()
            __snake_case      :    Any  			=3 * [inputs.pop('''prompt'''						)]

            __snake_case      :    Optional[Any]  			=pipe.tokenizer(
                a							,   padding='''max_length'''							,   max_length=pipe.tokenizer.model_max_length							,   truncation=a							,   return_tensors='''np'''							,   )
            __snake_case      :    List[Any]  			=text_inputs['''input_ids''']

            __snake_case      :    str  			=pipe.text_encoder(input_ids=text_inputs.astype(np.intaa						)						)[0]

            __snake_case      :    Optional[Any]  			=prompt_embeds

            # forward
            __snake_case      :    Dict  			=pipe(**a						)
            __snake_case      :    Any  			=output.images[0, -3:, -3:, -1]

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







      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    Dict  			=OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,   provider='''CPUExecutionProvider'''						)
            pipe.set_progress_bar_config(disable=a						)

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

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

            __snake_case      :    Tuple  			=self.get_dummy_inputs()
            __snake_case      :    Union[str, Any]  			=3 * [inputs.pop('''prompt'''						)]

            __snake_case      :    Optional[int]  			=[]
            for p in [prompt, negative_prompt]:
                  __snake_case      :    Optional[int]  			=pipe.tokenizer(
                      a							,   padding='''max_length'''							,   max_length=pipe.tokenizer.model_max_length							,   truncation=a							,   return_tensors='''np'''							,   )
                  __snake_case      :    Optional[Any]  			=text_inputs['''input_ids''']

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

            __snake_case			,				__snake_case      :    Optional[Any]  			=embeds

            # forward
            __snake_case      :    Any  			=pipe(**a						)
            __snake_case      :    Union[str, 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      _lowercase (     unittest.TestCase						):


      @property
      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







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


      @property
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=ort.SessionOptions()
            __snake_case      :    Optional[int]  			=False
            return options


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







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

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

            __snake_case      :    Optional[Any]  			=image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            __snake_case      :    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 _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







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

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

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            __snake_case      :    Union[str, Any]  			=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 _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







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

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

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            __snake_case      :    Optional[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 _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=0

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

            __snake_case      :    str  			=False

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

            __snake_case      :    List[Any]  			='''Andromeda galaxy in a bottle'''

            __snake_case      :    Optional[int]  			=np.random.RandomState(0						)
            pipe(
                prompt=a							,   num_inference_steps=5							,   guidance_scale=7.5							,   generator=a							,   callback=a							,   callback_steps=1							,   )
            assert test_callback_fn.has_been_called
            assert number_of_steps == 6







      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







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

            __snake_case      :    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(a						)
                  __snake_case      :    List[Any]  			=OnnxStableDiffusionPipeline.from_pretrained(a						)

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


