
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   =	logging.get_logger(__name__)





@add_end_docstrings(UpperCAmelCase							)
class    __A(					UpperCAmelCase							):

					def __init__(						self       :  Union[str, Any] ,       **__UpperCamelCase       :  int ):
												super().__init__(**__UpperCamelCase )

												if self.framework != "pt":
																			raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )
												# No specific FOR_XXX available yet

					def __call__(						self       :  Tuple ,       __UpperCamelCase       :  Union[np.ndarray, bytes, str] ,       **__UpperCamelCase       :  Any ):
												return super().__call__(__UpperCamelCase ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ,       **__UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       {}
												if "candidate_labels" in kwargs:
																			lowerCamelCase_					=       kwargs["""candidate_labels"""]
												if "hypothesis_template" in kwargs:
																			lowerCamelCase_					=       kwargs["""hypothesis_template"""]

												return preprocess_params, {}, {}

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  List[str]=None ,       __UpperCamelCase       :  Any="This is a sound of {}." ):
												if isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																			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
																										lowerCamelCase_					=       requests.get(__UpperCamelCase ).content
																			else:
																										with open(__UpperCamelCase ,       """rb""" ) as f:
																																	lowerCamelCase_					=       f.read()

												if isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																			lowerCamelCase_					=       ffmpeg_read(__UpperCamelCase ,       self.feature_extractor.sampling_rate )

												if not isinstance(__UpperCamelCase ,       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""" )

												lowerCamelCase_					=       self.feature_extractor(
												    [audio] ,       sampling_rate=self.feature_extractor.sampling_rate ,       return_tensors="""pt""" )
												lowerCamelCase_					=       candidate_labels
												lowerCamelCase_					=       [hypothesis_template.format(__UpperCamelCase ) for x in candidate_labels]
												lowerCamelCase_					=       self.tokenizer(__UpperCamelCase ,       return_tensors=self.framework ,       padding=__UpperCamelCase )
												lowerCamelCase_					=       [text_inputs]
												return inputs

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  int ):
												lowerCamelCase_					=       model_inputs.pop("""candidate_labels""" )
												lowerCamelCase_					=       model_inputs.pop("""text_inputs""" )
												if isinstance(text_inputs[0] ,       __UpperCamelCase ):
																			lowerCamelCase_					=       text_inputs[0]
												else:
																			# Batching case.
																			lowerCamelCase_					=       text_inputs[0][0]

												lowerCamelCase_					=       self.model(**__UpperCamelCase ,       **__UpperCamelCase )

												lowerCamelCase_					=       {
												    """candidate_labels""": candidate_labels,
												    """logits""": outputs.logits_per_audio,
												}
												return model_outputs



					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       model_outputs.pop("""candidate_labels""" )
												lowerCamelCase_					=       model_outputs["""logits"""][0]

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

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







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase   =	{'''UserAgent''': UserAgent().random}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     dict:
							lowerCamelCase_					=       script.contents[0]
							lowerCamelCase_					=       json.loads(data[data.find("""{\"config\""""							) : -1]							)
							return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class    __A:

					def __init__(						self       :  int ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       F'''https://www.instagram.com/{username}/'''
												lowerCamelCase_					=       self.get_json()

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       requests.get(self.url ,       headers=__UpperCamelCase ).text
												lowerCamelCase_					=       BeautifulSoup(__UpperCamelCase ,       """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       :  Dict ):
												return F'''{self.__class__.__name__}(\'{self.username}\')'''

					def __str__(						self       :  Optional[Any] ):
												return F'''{self.fullname} ({self.username}) is {self.biography}'''

					@property
					def  lowercase__   (						self       :  Dict ):
												return self.user_data["username"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["full_name"]

					@property
					def  lowercase__   (						self       :  Any ):
												return self.user_data["biography"]

					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["business_email"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["external_url"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["edge_followed_by"]["count"]

					@property
					def  lowercase__   (						self       :  str ):
												return self.user_data["edge_follow"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["edge_owner_to_timeline_media"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["profile_pic_url_hd"]

					@property
					def  lowercase__   (						self       :  Optional[Any] ):
												return self.user_data["is_verified"]



					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["is_private"]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "github"							)	->     None:
							import os

							if os.environ.get("""CI"""							):
														return  # test failing on GitHub Actions
							lowerCamelCase_					=       InstagramUser(UpperCAmelCase__							)
							assert instagram_user.user_data
							assert isinstance(instagram_user.user_data      ,					UpperCAmelCase__							)
							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()
		lowercase   =	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 = }""")





