
"""simple docstring"""
import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def 				a__   (   a   :				Dict		):

  """simple docstring"""







  return 1.0 / (1.0 + np.exp(-_outputs		))
def 				a__   (   a   :				List[str]		):

  """simple docstring"""







  _snake_case     :						Tuple						=   np.max(_outputs       ,     axis=-1       ,     keepdims=a		)
  _snake_case     :						Tuple						=   np.exp(_outputs - maxes		)
  return shifted_exp / shifted_exp.sum(axis=-1       ,     keepdims=a		)






class      _UpperCAmelCase				(					_snake_case):
       __lowercase							: Dict  =					"""sigmoid"""
       __lowercase							: Any  =					"""softmax"""
       __lowercase							: Optional[int]  =					"""none"""






@add_end_docstrings(
    _snake_case		,					r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """		,					)
class      _UpperCAmelCase				(					_snake_case):
       __lowercase							: Any  =					False
       __lowercase							: Dict  =					ClassificationFunction.NONE
       def __init__(    self						,    **snake_case_     ):
         super().__init__(**snake_case_     )

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == "tf"
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING     )
       def 					lowerCamelCase__	(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_=""						,    **snake_case_     ):
         # Using "" as default argument because we're going to use `top_k=None` in user code to declare
         # "No top_k"
         _snake_case     :						str						=   tokenizer_kwargs

         _snake_case     :						str						=   {}
         if hasattr(self.model.config						,    "return_all_scores"     ) and return_all_scores is None:
           _snake_case     :						str						=   self.model.config.return_all_scores

         if isinstance(snake_case_						,    snake_case_     ) or top_k is None:
           _snake_case     :						Optional[Any]						=   top_k
           _snake_case     :						List[str]						=   False
         elif return_all_scores is not None:
           warnings.warn(
               "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
               " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."						,    snake_case_						,    )
           if return_all_scores:
             _snake_case     :						Optional[int]						=   None
           else:
             _snake_case     :						Dict						=   1

         if isinstance(snake_case_						,    snake_case_     ):
           _snake_case     :						List[Any]						=   ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
           _snake_case     :						List[str]						=   function_to_apply
         return preprocess_params, {}, postprocess_params
       def __call__(    self						,    *snake_case_						,    **snake_case_     ):
         _snake_case     :						Dict						=   super().__call__(*snake_case_						,    **snake_case_     )
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         _snake_case     :						Tuple						=   "top_k" not in kwargs
         if isinstance(args[0]						,    snake_case_     ) and _legacy:
           # This pipeline is odd, and return a list when single item is run
           return [result]
         else:
           return result
       def 					lowerCamelCase__	(    self						,    snake_case_						,    **snake_case_     ):
         _snake_case     :						str						=   self.framework
         if isinstance(snake_case_						,    snake_case_     ):
           return self.tokenizer(**snake_case_						,    return_tensors=snake_case_						,    **snake_case_     )
         elif isinstance(snake_case_						,    snake_case_     ) and len(snake_case_     ) == 1 and isinstance(inputs[0]						,    snake_case_     ) and len(inputs[0]     ) == 2:
           # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
           return self.tokenizer(
               text=inputs[0][0]						,    text_pair=inputs[0][1]						,    return_tensors=snake_case_						,    **snake_case_     )
         elif isinstance(snake_case_						,    snake_case_     ):
           # This is likely an invalid usage of the pipeline attempting to pass text pairs.
           raise ValueError(
               "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
               " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."     )
         return self.tokenizer(snake_case_						,    return_tensors=snake_case_						,    **snake_case_     )
       def 					lowerCamelCase__	(    self						,    snake_case_     ):
         return self.model(**snake_case_     )


       def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_=None						,    snake_case_=1						,    snake_case_=True     ):
         # `_legacy` is used to determine if we're running the naked pipeline and in backward
         # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
         # the more natural result containing the list.
         # Default value before `set_parameters`
         if function_to_apply is None:
           if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
             _snake_case     :						Dict						=   ClassificationFunction.SIGMOID
           elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
             _snake_case     :						Union[str, Any]						=   ClassificationFunction.SOFTMAX
           elif hasattr(self.model.config						,    "function_to_apply"     ) and function_to_apply is None:
             _snake_case     :						Any						=   self.model.config.function_to_apply
           else:
             _snake_case     :						str						=   ClassificationFunction.NONE

         _snake_case     :						Optional[Any]						=   model_outputs["logits"][0]
         _snake_case     :						Optional[Any]						=   outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
           _snake_case     :						Optional[Any]						=   sigmoid(snake_case_     )
         elif function_to_apply == ClassificationFunction.SOFTMAX:
           _snake_case     :						Tuple						=   softmax(snake_case_     )
         elif function_to_apply == ClassificationFunction.NONE:
           _snake_case     :						Dict						=   outputs
         else:
           raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}'     )

         if top_k == 1 and _legacy:
           return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         _snake_case     :						List[Any]						=   [
             {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(snake_case_     )
         ]
         if not _legacy:
           dict_scores.sort(key=lambda snake_case_     : x["score"]						,    reverse=snake_case_     )
           if top_k is not None:
             _snake_case     :						int						=   dict_scores[:top_k]
         return dict_scores



"""simple docstring"""
from __future__ import annotations

import requests

_a      :			List[str]					=							set(
    """approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports""".split()
)
def 				a__   (   a   :				str       ,     a   :				int = 1       ,     a   :				str = "new"       ,     a   :				list | None = None		):

  """simple docstring"""







  _snake_case     :						Any						=   wanted_data or []
  if invalid_search_terms := ", ".join(sorted(set(a		) - valid_terms		)		):
    _snake_case     :						Optional[int]						=   f'Invalid search term: {invalid_search_terms}'
    raise ValueError(a		)
  _snake_case     :						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

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

  _snake_case     :						Tuple						=   {}
  for id_ in range(a		):
    _snake_case     :						List[str]						=   {
        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"""]))

