import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort



class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							# FIXME: add fast tests
							pass




@nightly
@require_onnxruntime
@require_torch_gpu
class        lowercase_   (					unittest.TestCase		):



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



							@property
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   ort.SessionOptions()
													_snake_case						:					List[Any]    =   False
													return options



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
													_snake_case						:					str    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
													_snake_case						:					Any    =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"				,    revision="onnx"				,    safety_checker=lowercase_				,    feature_extractor=lowercase_				,    provider=self.gpu_provider				,    sess_options=self.gpu_options				,    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					Optional[int]    =   "A red cat sitting on a park bench"

													_snake_case						:					List[Any]    =   np.random.RandomState(0    )
													_snake_case						:					List[Any]    =   pipe(
													    prompt=lowercase_				,    image=lowercase_				,    mask_image=lowercase_				,    guidance_scale=7.5				,    num_inference_steps=10				,    generator=lowercase_				,    output_type="np"				,    )
													_snake_case						:					List[Any]    =   output.images
													_snake_case						:					str    =   images[0, 255:258, 255:258, -1]

													assert images.shape == (1, 512, 512, 3)
													_snake_case						:					int    =   np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464]    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo.png"    )
													_snake_case						:					List[Any]    =   load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"    )
													_snake_case						:					Any    =   LMSDiscreteScheduler.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"				,    subfolder="scheduler"				,    revision="onnx"    )
													_snake_case						:					int    =   OnnxStableDiffusionInpaintPipeline.from_pretrained(
													    "runwayml/stable-diffusion-inpainting"				,    revision="onnx"				,    scheduler=lowercase_				,    safety_checker=lowercase_				,    feature_extractor=lowercase_				,    provider=self.gpu_provider				,    sess_options=self.gpu_options				,    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					str    =   "A red cat sitting on a park bench"

													_snake_case						:					str    =   np.random.RandomState(0    )
													_snake_case						:					Tuple    =   pipe(
													    prompt=lowercase_				,    image=lowercase_				,    mask_image=lowercase_				,    guidance_scale=7.5				,    num_inference_steps=20				,    generator=lowercase_				,    output_type="np"				,    )
													_snake_case						:					int    =   output.images
													_snake_case						:					Dict    =   images[0, 255:258, 255:258, -1]

													assert images.shape == (1, 512, 512, 3)
													_snake_case						:					Tuple    =   np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125]    )

													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
from __future__ import annotations

import requests

__SCREAMING_SNAKE_CASE						:   Tuple         =			set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)
def  snake_case						(__lowercase ,      __lowercase = 1 ,      __lowercase = "new" ,      __lowercase = None						)	->	dict:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   wanted_data or []
						if invalid_search_terms := ", ".join(sorted(set(__lowercase						) - valid_terms						)						):
												_snake_case						:					List[str]    =   F"""Invalid search term: {invalid_search_terms}"""
												raise ValueError(__lowercase						)
						_snake_case						:					Any    =   requests.get(
						    F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}""" ,      headers={"User-agent": "A random string"} ,      )
						if response.status_code == 429:
												raise requests.HTTPError

						_snake_case						:					Optional[int]    =   response.json()
						if not wanted_data:
												return {id_: data["data"]["children"][id_] for id_ in range(__lowercase						)}

						_snake_case						:					Union[str, Any]    =   {}
						for id_ in range(__lowercase						):
												_snake_case						:					Dict    =   {
												    item: data["data"]["children"][id_]["data"][item] for item in wanted_data
												}
						return data_dict


if __name__ == "__main__":
				# If you get Error 429, that means you are rate limited.Try after some time
				print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))