



from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
		import numpy as np
		from decord import VideoReader


if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

_lowercase		:				int      		=logging.get_logger(__name__)

@add_end_docstrings(A__		)
class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       *__lowercase ,       **__lowercase  ) ->				str:
									"""simple docstring"""




									super().__init__(*__lowercase ,       **__lowercase  )
									requires_backends(self ,       """decord"""  )
									self.check_model_type(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase=None ,       __lowercase=None ,       __lowercase=None  ) ->				str:
									"""simple docstring"""




									a__       :						str							=  {}
									if frame_sampling_rate is not None:
												a__       :						str							=  frame_sampling_rate
									if num_frames is not None:
												a__       :						Optional[int]							=  num_frames

									a__       :						Tuple							=  {}
									if top_k is not None:
												a__       :						Union[str, Any]							=  top_k
									return preprocess_params, {}, postprocess_params

						def __call__(       self ,       __lowercase ,       **__lowercase  ) ->				Dict:
									"""simple docstring"""




									return super().__call__(__lowercase ,       **__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=None ,       __lowercase=1  ) ->				Optional[Any]:
									"""simple docstring"""




									if num_frames is None:
												a__       :						str							=  self.model.config.num_frames

									if video.startswith("""http://"""  ) or video.startswith("""https://"""  ):
												a__       :						Tuple							=  BytesIO(requests.get(__lowercase  ).content  )

									a__       :						Any							=  VideoReader(__lowercase  )
									videoreader.seek(0  )

									a__       :						int							=  0
									a__       :						int							=  num_frames * frame_sampling_rate - 1
									a__       :						Optional[Any]							=  np.linspace(__lowercase ,       __lowercase ,       num=__lowercase ,       dtype=np.intaa  )

									a__       :						Any							=  videoreader.get_batch(__lowercase  ).asnumpy()
									a__       :						Optional[Any]							=  list(__lowercase  )

									a__       :						Optional[Any]							=  self.image_processor(__lowercase ,       return_tensors=self.framework  )
									return model_inputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						Any							=  self.model(**__lowercase  )
									return model_outputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=5  ) ->				Tuple:
									"""simple docstring"""




									if top_k > self.model.config.num_labels:
												a__       :						str							=  self.model.config.num_labels

									if self.framework == "pt":
												a__       :						List[Any]							=  model_outputs.logits.softmax(-1  )[0]
												a__       :						int							=  probs.topk(__lowercase  )
									else:
												raise ValueError(F'''Unsupported framework: {self.framework}'''  )

									a__       :						Tuple							=  scores.tolist()
									a__       :						Optional[int]							=  ids.tolist()
									return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__lowercase ,       __lowercase  )]









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"]))




