




'''simple docstring'''





from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_SCREAMING_SNAKE_CASE							:   Tuple      		= {"""UserAgent""": UserAgent().random}


def      UpperCamelCase_(	snake_case							:       int     ):

							'''simple docstring'''






							snake_case_						=      script.contents[0]
							snake_case_						=      json.loads(data[data.find("{\"config\""     ) : -1]     )
							return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]




class  _snake_case		:




			def __init__(					self				,							a__							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      F'https://www.instagram.com/{username}/'
										snake_case_						=      self.get_json()




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      requests.get(self.url				,							headers=a__							).text
										snake_case_						=      BeautifulSoup(a__				,							"html.parser"							).find_all("script"							)
										try:
																	return extract_user_profile(scripts[4]							)
										except (json.decoder.JSONDecodeError, KeyError):
																	return extract_user_profile(scripts[3]							)




			def __repr__(					self							)				->				int:

										'''simple docstring'''




										return F'{self.__class__.__name__}(\'{self.username}\')'




			def __str__(					self							)				->				int:

										'''simple docstring'''




										return F'{self.fullname} ({self.username}) is {self.biography}'




			@property
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										return self.user_data["username"]




			@property
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										return self.user_data["full_name"]




			@property
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										return self.user_data["biography"]




			@property
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										return self.user_data["business_email"]




			@property
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										return self.user_data["external_url"]




			@property
			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										return self.user_data["edge_followed_by"]["count"]




			@property
			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										return self.user_data["edge_follow"]["count"]




			@property
			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										return self.user_data["edge_owner_to_timeline_media"]["count"]




			@property
			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										return self.user_data["profile_pic_url_hd"]




			@property
			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										return self.user_data["is_verified"]




			@property
			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										return self.user_data["is_private"]


def      UpperCamelCase_(	snake_case							:       Tuple = "github"     ):

							'''simple docstring'''






							import os

							if os.environ.get("CI"     ):
														return  # test failing on GitHub Actions
							snake_case_						=      InstagramUser(SCREAMING_SNAKE_CASE__     )
							assert instagram_user.user_data
							assert isinstance(instagram_user.user_data							,				SCREAMING_SNAKE_CASE__     )
							assert instagram_user.username == username
							if username != "github":
														return
							assert instagram_user.fullname == "GitHub"
							assert instagram_user.biography == "Built for developers."
							assert instagram_user.number_of_posts > 1_5_0
							assert instagram_user.number_of_followers > 1_2_0_0_0_0
							assert instagram_user.number_of_followings > 1_5
							assert instagram_user.email == "support@github.com"
							assert instagram_user.website == "https://github.com/readme"
							assert instagram_user.profile_picture_url.startswith("https://instagram."     )
							assert instagram_user.is_verified is True
							assert instagram_user.is_private is False


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		_SCREAMING_SNAKE_CASE							:   List[str]      		= InstagramUser("github")
		print(instagram_user)
		print(F"{instagram_user.number_of_posts = }")
		print(F"{instagram_user.number_of_followers = }")
		print(F"{instagram_user.number_of_followings = }")
		print(F"{instagram_user.email = }")
		print(F"{instagram_user.website = }")
		print(F"{instagram_user.profile_picture_url = }")
		print(F"{instagram_user.is_verified = }")
		print(F"{instagram_user.is_private = }")






from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowercase  :					Optional[Any]						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,**snake_case      ):



           '''simple docstring'''







           super().__init__(**snake_case      )

           if self.framework != "pt":
                  raise ValueError(f"The {self.__class__} is only available in PyTorch."      )
           # No specific FOR_XXX available yet
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      {}
           if "candidate_labels" in kwargs:
                  lowercase							:     List[str]       	=      kwargs["""candidate_labels"""]
           if "hypothesis_template" in kwargs:
                  lowercase							:     Dict       	=      kwargs["""hypothesis_template"""]

           return preprocess_params, {}, {}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None		,snake_case="This is a sound of {}."      ):



           '''simple docstring'''







           if isinstance(snake_case		,snake_case      ):
                  if audio.startswith("""http://"""      ) or audio.startswith("""https://"""      ):
                         # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                         # like http_huggingface_co.png
                         lowercase							:     Optional[Any]       	=      requests.get(snake_case      ).content
                  else:
                         with open(snake_case		,"""rb"""      ) as f:
                                lowercase							:     Union[str, Any]       	=      f.read()

           if isinstance(snake_case		,snake_case      ):
                  lowercase							:     int       	=      ffmpeg_read(snake_case		,self.feature_extractor.sampling_rate      )

           if not isinstance(snake_case		,np.ndarray      ):
                  raise ValueError("""We expect a numpy ndarray as input"""      )
           if len(audio.shape      ) != 1:
                  raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline"""      )

           lowercase							:     Dict       	=      self.feature_extractor(
               [audio]		,sampling_rate=self.feature_extractor.sampling_rate		,return_tensors="""pt"""      )
           lowercase							:     Tuple       	=      candidate_labels
           lowercase							:     Tuple       	=      [hypothesis_template.format(snake_case      ) for x in candidate_labels]
           lowercase							:     Optional[Any]       	=      self.tokenizer(snake_case		,return_tensors=self.framework		,padding=snake_case      )
           lowercase							:     Optional[Any]       	=      [text_inputs]
           return inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      model_inputs.pop("""candidate_labels"""      )
           lowercase							:     Dict       	=      model_inputs.pop("""text_inputs"""      )
           if isinstance(text_inputs[0]		,snake_case      ):
                  lowercase							:     List[Any]       	=      text_inputs[0]
           else:
                  # Batching case.
                  lowercase							:     Dict       	=      text_inputs[0][0]

           lowercase							:     Optional[Any]       	=      self.model(**snake_case		,**snake_case      )

           lowercase							:     Any       	=      {
               """candidate_labels""": candidate_labels,
               """logits""": outputs.logits_per_audio,
           }
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      model_outputs.pop("""candidate_labels"""      )
           lowercase							:     Any       	=      model_outputs["""logits"""][0]

           if self.framework == "pt":
                  lowercase							:     Any       	=      logits.softmax(dim=0      )
                  lowercase							:     Tuple       	=      probs.tolist()
           else:
                  raise ValueError("""`tf` framework not supported."""      )

           lowercase							:     Tuple       	=      [
               {"""score""": score, """label""": candidate_label}
               for score, candidate_label in sorted(zip(snake_case		,snake_case      )		,key=lambda snake_case      : -x[0]      )
           ]
           return result
