



import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
		import jax
		import jax.numpy as jnp
		from flax.jax_utils import replicate
		from flax.training.common_utils import shard

@slow
@require_flax
class        snake_case__    (unittest.TestCase		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									super().tearDown()
									gc.collect()

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						int							=  FlaxStableDiffusionPipeline.from_pretrained(
									    """stabilityai/stable-diffusion-2""" ,       revision="""bf16""" ,       dtype=jnp.bfloataa ,       )

									a__       :						Any							=  """A painting of a squirrel eating a burger"""
									a__       :						Union[str, Any]							=  jax.device_count()
									a__       :						List[str]							=  num_samples * [prompt]
									a__       :						Optional[Any]							=  sd_pipe.prepare_inputs(_SCREAMING_SNAKE_CASE  )

									a__       :						List[str]							=  replicate(_SCREAMING_SNAKE_CASE  )
									a__       :						Any							=  shard(_SCREAMING_SNAKE_CASE  )

									a__       :						int							=  jax.random.PRNGKey(0  )
									a__       :						Tuple							=  jax.random.split(_SCREAMING_SNAKE_CASE ,       jax.device_count()  )

									a__       :						Dict							=  sd_pipe(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       num_inference_steps=2_5 ,       jit=_SCREAMING_SNAKE_CASE  )[0]
									assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

									a__       :						Union[str, Any]							=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
									a__       :						int							=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									a__       :						Any							=  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
									a__       :						str							=  jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]  )
									print(F'''output_slice: {output_slice}'''  )
									assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  """stabilityai/stable-diffusion-2"""
									a__       :						Dict							=  FlaxDPMSolverMultistepScheduler.from_pretrained(_SCREAMING_SNAKE_CASE ,       subfolder="""scheduler"""  )
									a__       :						Optional[Any]							=  FlaxStableDiffusionPipeline.from_pretrained(
									    _SCREAMING_SNAKE_CASE ,       scheduler=_SCREAMING_SNAKE_CASE ,       revision="""bf16""" ,       dtype=jnp.bfloataa ,       )
									a__       :						Optional[Any]							=  scheduler_params

									a__       :						int							=  """A painting of a squirrel eating a burger"""
									a__       :						Optional[Any]							=  jax.device_count()
									a__       :						Optional[Any]							=  num_samples * [prompt]
									a__       :						Any							=  sd_pipe.prepare_inputs(_SCREAMING_SNAKE_CASE  )

									a__       :						Optional[int]							=  replicate(_SCREAMING_SNAKE_CASE  )
									a__       :						Dict							=  shard(_SCREAMING_SNAKE_CASE  )

									a__       :						int							=  jax.random.PRNGKey(0  )
									a__       :						Tuple							=  jax.random.split(_SCREAMING_SNAKE_CASE ,       jax.device_count()  )

									a__       :						List[str]							=  sd_pipe(_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       num_inference_steps=2_5 ,       jit=_SCREAMING_SNAKE_CASE  )[0]
									assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

									a__       :						Dict							=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
									a__       :						Optional[Any]							=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									a__       :						Any							=  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
									a__       :						List[Any]							=  jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]  )
									print(F'''output_slice: {output_slice}'''  )
									assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2









from __future__ import annotations

import requests

_lowercase		:				Dict      		=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      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       int = 1						,       _lowercase    :       str = "new"						,       _lowercase    :       list | None = None)							->	dict:







			"""simple docstring"""
			a__       :						Optional[Any]							=  wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(_lowercase) - valid_terms)):
						a__       :						Any							=  F'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(_lowercase)
			a__       :						int							=  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

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

			a__       :						int							=  {}
			for id_ in range(_lowercase):
						a__       :						List[Any]							=  {
						    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"]))




