import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool



class        lowercase_   (					_lowerCamelCase		):
							_lowerCamelCase						=		'facebook/bart-large-mnli'
							_lowerCamelCase						=		(
							    'This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '
							    'should be the text to classify, and `labels`, which should be the list of labels to use for classification. '
							    'It returns the most likely label in the list of provided `labels` for the input text.'
							)
							_lowerCamelCase						=		'text_classifier'
							_lowerCamelCase						=		AutoTokenizer
							_lowerCamelCase						=		AutoModelForSequenceClassification

							_lowerCamelCase						=		['text', ['text']]
							_lowerCamelCase						=		['text']



							def 	UpperCamelCase	(						self    ):
													super().setup()
													_snake_case						:					List[Any]    =   self.model.config
													_snake_case						:					Optional[int]    =   -1
													for idx, label in config.idalabel.items():
																			if label.lower().startswith("entail"    ):
																									_snake_case						:					Union[str, Any]    =   int(lowercase_    )
													if self.entailment_id == -1:
																			raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Any    =   labels
													return self.pre_processor(
													    [text] * len(lowercase_    )				,    [f"""This example is {label}""" for label in labels]				,    return_tensors="pt"				,    padding="max_length"				,    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					str    =   outputs.logits
													_snake_case						:					Union[str, Any]    =   torch.argmax(logits[:, 2]    ).item()
													return self._labels[label_id]
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']))