





from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase__  :     Optional[Any]   			={'UserAgent': UserAgent().random}





def        _lowercase   (  _UpperCAmelCase			)  ->					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				,	UpperCAmelCase_							):
								lowerCamelCase			=f"""https://www.instagram.com/{username}/"""
								lowerCamelCase			=self.get_json()


				def _snake_case    (						self							):
								lowerCamelCase			=requests.get(self.url				,	headers=__a							).text
								lowerCamelCase			=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							):
								return f"""{self.__class__.__name__}('{self.username}')"""


				def __str__(						self							):
								return f"""{self.fullname} ({self.username}) is {self.biography}"""


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


				@property
				def _snake_case    (						self							):
								return self.user_data["full_name"]


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


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


				@property
				def _snake_case    (						self							):
								return self.user_data["external_url"]


				@property
				def _snake_case    (						self							):
								return self.user_data["edge_followed_by"]["count"]


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


				@property
				def _snake_case    (						self							):
								return self.user_data["edge_owner_to_timeline_media"]["count"]


				@property
				def _snake_case    (						self							):
								return self.user_data["profile_pic_url_hd"]


				@property
				def _snake_case    (						self							):
								return self.user_data["is_verified"]


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





def        _lowercase   (  _UpperCAmelCase = "github"			)  ->					None:
				import os

				if os.environ.get("""CI"""			):
								return  # test failing on GitHub Actions
				lowerCamelCase			=InstagramUser(snake_case_			)
				assert instagram_user.user_data
				assert isinstance(instagram_user.user_data     , 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_50
				assert instagram_user.number_of_followers > 12_00_00
				assert instagram_user.number_of_followings > 15
				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()
			UpperCAmelCase__  :     int   			=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 typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCAmelCase__  :     Dict   			=logging.get_logger(__name__)  # pylint: disable=invalid-name

class  __A       (  a      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								super().__init__()
								self.register_modules(unet=UpperCAmelCase_				,	scheduler=UpperCAmelCase_							)


				@torch.no_grad()
				def __call__(						self				,	UpperCAmelCase_ = 1				,	UpperCAmelCase_ = 100				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = True				,	):

								if audio_length_in_s is None:
												lowerCamelCase			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase			=2 ** len(self.unet.up_blocks							)
								if sample_size < 3 * down_scale_factor:
												raise ValueError(
												    f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
												    f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""							)

								lowerCamelCase			=int(UpperCAmelCase_							)
								if sample_size % down_scale_factor != 0:
												lowerCamelCase			=(
												    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
												) * down_scale_factor
												logger.info(
												    f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
												    f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
												    """ process."""							)
								lowerCamelCase			=int(UpperCAmelCase_							)

								lowerCamelCase			=next(iter(self.unet.parameters()							)							).dtype
								lowerCamelCase			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(UpperCAmelCase_				,	UpperCAmelCase_							) and len(UpperCAmelCase_							) != batch_size:
												raise ValueError(
												    f"""You have passed a list of generators of length {len(UpperCAmelCase_							)}, but requested an effective batch"""
												    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

								lowerCamelCase			=randn_tensor(UpperCAmelCase_				,	generator=UpperCAmelCase_				,	device=self.device				,	dtype=UpperCAmelCase_							)

								# set step values
								self.scheduler.set_timesteps(UpperCAmelCase_				,	device=audio.device							)
								lowerCamelCase			=self.scheduler.timesteps.to(UpperCAmelCase_							)

								for t in self.progress_bar(self.scheduler.timesteps							):
												# 1. predict noise model_output
												lowerCamelCase			=self.unet(UpperCAmelCase_				,	UpperCAmelCase_							).sample

												# 2. compute previous image: x_t -> t_t-1
												lowerCamelCase			=self.scheduler.step(UpperCAmelCase_				,	UpperCAmelCase_				,	UpperCAmelCase_							).prev_sample

								lowerCamelCase			=audio.clamp(-1				,	1							).float().cpu().numpy()

								lowerCamelCase			=audio[:, :, :original_sample_size]

								if not return_dict:
												return (audio,)

								return AudioPipelineOutput(audios=UpperCAmelCase_							)





