




'''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						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
       def 					A   (			self		:			Dict     ,       lowercase		:			List[str]=0       ):



           '''simple docstring'''

           UpperCAmelCase     =	np.random.RandomState(__UpperCamelCase       )
           UpperCAmelCase     =	{
               '''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]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''       )
           pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

           assert image.shape == (1, 128, 128, 3)
           UpperCAmelCase     =	np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			str       ):



           '''simple docstring'''

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

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

           assert image.shape == (1, 128, 128, 3)
           UpperCAmelCase     =	np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			str       ):



           '''simple docstring'''

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

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

           assert image.shape == (1, 128, 128, 3)
           UpperCAmelCase     =	np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

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

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

           assert image.shape == (1, 128, 128, 3)
           UpperCAmelCase     =	np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

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

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

           assert image.shape == (1, 128, 128, 3)
           UpperCAmelCase     =	np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

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

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

           assert image.shape == (1, 128, 128, 3)
           UpperCAmelCase     =	np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''       )
           pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

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

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

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

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

           UpperCAmelCase     =	prompt_embeds

           # forward
           UpperCAmelCase     =	pipe(**__UpperCamelCase       )
           UpperCAmelCase     =	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]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='''CPUExecutionProvider'''       )
           pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

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

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

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

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

           UpperCAmelCase ,  UpperCAmelCase     =	embeds

           # forward
           UpperCAmelCase     =	pipe(**__UpperCamelCase       )
           UpperCAmelCase     =	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       ):



           '''simple docstring'''

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )
       @property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	ort.SessionOptions()
           UpperCAmelCase     =	False
           return options
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	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       )

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

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

           assert image.shape == (1, 512, 512, 3)
           UpperCAmelCase     =	np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	DDIMScheduler.from_pretrained(
               '''runwayml/stable-diffusion-v1-5'''     ,       subfolder='''scheduler'''     ,       revision='''onnx'''       )
           UpperCAmelCase     =	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       )

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

           assert image.shape == (1, 512, 512, 3)
           UpperCAmelCase     =	np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	LMSDiscreteScheduler.from_pretrained(
               '''runwayml/stable-diffusion-v1-5'''     ,       subfolder='''scheduler'''     ,       revision='''onnx'''       )
           UpperCAmelCase     =	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       )

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

           assert image.shape == (1, 512, 512, 3)
           UpperCAmelCase     =	np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	0

           def test_callback_fn(lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			np.ndarray       ) -> None:
               UpperCAmelCase     =	True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 0:
                   assert latents.shape == (1, 4, 64, 64)
                   UpperCAmelCase     =	latents[0, -3:, -3:, -1]
                   UpperCAmelCase     =	np.array(
                       [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]       )

                   assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 1E-3
               elif step == 5:
                   assert latents.shape == (1, 4, 64, 64)
                   UpperCAmelCase     =	latents[0, -3:, -3:, -1]
                   UpperCAmelCase     =	np.array(
                       [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]       )

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

           UpperCAmelCase     =	False

           UpperCAmelCase     =	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       )

           UpperCAmelCase     =	'''Andromeda galaxy in a bottle'''

           UpperCAmelCase     =	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 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	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

           UpperCAmelCase     =	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       )
               UpperCAmelCase     =	OnnxStableDiffusionPipeline.from_pretrained(__UpperCamelCase       )

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









'''simple docstring'''







from __future__ import annotations

import requests





def 		snake_case_							(_a			:							str  ):
    UpperCAmelCase     =	F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
    return requests.get(_a  ).json()





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    UpperCAmelCase     =	requests.get(_a  ).json()[:max_stories]
    return [get_hackernews_story(_a  ) for story_id in story_ids]





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	hackernews_top_stories(_a  )
    return "\n".join('''* [{title}]({url})'''.format(**_a  ) for story in stories  )


if __name__ == "__main__":
   print(hackernews_top_stories_as_markdown())




