



import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    Adafactor,
			    AdamW,
			    get_constant_schedule,
			    get_constant_schedule_with_warmup,
			    get_cosine_schedule_with_warmup,
			    get_cosine_with_hard_restarts_schedule_with_warmup,
			    get_inverse_sqrt_schedule,
			    get_linear_schedule_with_warmup,
			    get_polynomial_decay_schedule_with_warmup,
			)







def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE=10			)		->				Optional[int]:
		UpperCAmelCase_						       =					[]
		for _ in range(A__			):
				lrs.append(scheduler.get_lr()[0]			)
				scheduler.step()
		return lrs







def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE=10			)		->				str:
		UpperCAmelCase_						       =					[]
		for step in range(A__			):
				lrs.append(scheduler.get_lr()[0]			)
				scheduler.step()
				if step == num_steps // 2:
						with tempfile.TemporaryDirectory() as tmpdirname:
								UpperCAmelCase_						       =					os.path.join(A__      ,  "schedule.bin"			)
								torch.save(scheduler.state_dict()      ,  A__			)

								UpperCAmelCase_						       =					torch.load(A__			)
								scheduler.load_state_dict(A__			)
		return lrs



@require_torch
class 						lowerCamelCase   (    unittest.TestCase	):







					'''simple docstring'''







					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase  ):
							self.assertEqual(len(__lowerCamelCase  )     ,						len(__lowerCamelCase  )  )
							for a, b in zip(__lowerCamelCase     ,						__lowerCamelCase  ):
									self.assertAlmostEqual(__lowerCamelCase     ,						__lowerCamelCase     ,						delta=__lowerCamelCase  )



					def  A__		(      self  ):
							UpperCAmelCase_						       =					torch.tensor([0.1, -0.2, -0.1]     ,						requires_grad=__lowerCamelCase  )
							UpperCAmelCase_						       =					torch.tensor([0.4, 0.2, -0.5]  )
							UpperCAmelCase_						       =					nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							UpperCAmelCase_						       =					AdamW(params=[w]     ,						lr=2e-1     ,						weight_decay=0.0  )
							for _ in range(100  ):
									UpperCAmelCase_						       =					criterion(__lowerCamelCase     ,						__lowerCamelCase  )
									loss.backward()
									optimizer.step()
									w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
									w.grad.zero_()
							self.assertListAlmostEqual(w.tolist()     ,						[0.4, 0.2, -0.5]     ,						tol=1e-2  )



					def  A__		(      self  ):
							UpperCAmelCase_						       =					torch.tensor([0.1, -0.2, -0.1]     ,						requires_grad=__lowerCamelCase  )
							UpperCAmelCase_						       =					torch.tensor([0.4, 0.2, -0.5]  )
							UpperCAmelCase_						       =					nn.MSELoss()
							# No warmup, constant schedule, no gradient clipping
							UpperCAmelCase_						       =					Adafactor(
							    params=[w]     ,						lr=1e-2     ,						eps=(1e-3_0, 1e-3)     ,						clip_threshold=1.0     ,						decay_rate=-0.8     ,						betaa=__lowerCamelCase     ,						weight_decay=0.0     ,						relative_step=__lowerCamelCase     ,						scale_parameter=__lowerCamelCase     ,						warmup_init=__lowerCamelCase     ,						)
							for _ in range(1000  ):
									UpperCAmelCase_						       =					criterion(__lowerCamelCase     ,						__lowerCamelCase  )
									loss.backward()
									optimizer.step()
									w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
									w.grad.zero_()
							self.assertListAlmostEqual(w.tolist()     ,						[0.4, 0.2, -0.5]     ,						tol=1e-2  )





@require_torch
class 						lowerCamelCase   (    unittest.TestCase	):







					'''simple docstring'''







					lowerCAmelCase_					:     Dict					       =	nn.Linear(50,  50	) if is_torch_available() else None
					lowerCAmelCase_					:     int					       =	AdamW(m.parameters(),  lr=1_0.0	) if is_torch_available() else None
					lowerCAmelCase_					:     Optional[Any]					       =	10



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase     ,						lowerCAmelCase=None  ):
							self.assertEqual(len(__lowerCamelCase  )     ,						len(__lowerCamelCase  )  )
							for a, b in zip(__lowerCamelCase     ,						__lowerCamelCase  ):
									self.assertAlmostEqual(__lowerCamelCase     ,						__lowerCamelCase     ,						delta=__lowerCamelCase     ,						msg=__lowerCamelCase  )



					def  A__		(      self  ):
							UpperCAmelCase_						       =					{"num_warmup_steps": 2, "num_training_steps": 10}
							# schedulers doct format
							# function: (sched_args_dict, expected_learning_rates)
							UpperCAmelCase_						       =					{
							    get_constant_schedule: ({}, [10.0] * self.num_steps),
							    get_constant_schedule_with_warmup: (
							        {"num_warmup_steps": 4},
							        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
							    ),
							    get_linear_schedule_with_warmup: (
							        {**common_kwargs},
							        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
							    ),
							    get_cosine_schedule_with_warmup: (
							        {**common_kwargs},
							        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
							    ),
							    get_cosine_with_hard_restarts_schedule_with_warmup: (
							        {**common_kwargs, "num_cycles": 2},
							        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
							    ),
							    get_polynomial_decay_schedule_with_warmup: (
							        {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
							        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
							    ),
							    get_inverse_sqrt_schedule: (
							        {"num_warmup_steps": 2},
							        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
							    ),
							}

							for scheduler_func, data in scheds.items():
									UpperCAmelCase_       ,    UpperCAmelCase_						       =					data

									UpperCAmelCase_						       =					scheduler_func(self.optimizer     ,						**__lowerCamelCase  )
									self.assertEqual(len([scheduler.get_lr()[0]]  )     ,						1  )
									UpperCAmelCase_						       =					unwrap_schedule(__lowerCamelCase     ,						self.num_steps  )
									self.assertListAlmostEqual(
									    __lowerCamelCase     ,						__lowerCamelCase     ,						tol=1e-2     ,						msg=f'''failed for {scheduler_func} in normal scheduler'''     ,						)

									UpperCAmelCase_						       =					scheduler_func(self.optimizer     ,						**__lowerCamelCase  )
									if scheduler_func.__name__ != "get_constant_schedule":
											LambdaScheduleWrapper.wrap_scheduler(__lowerCamelCase  )  # wrap to test picklability of the schedule
									UpperCAmelCase_						       =					unwrap_and_save_reload_schedule(__lowerCamelCase     ,						self.num_steps  )
									self.assertListEqual(__lowerCamelCase     ,						__lowerCamelCase     ,						msg=f'''failed for {scheduler_func} in save and reload'''  )





class 						lowerCamelCase   :







					'''simple docstring'''







					def __init__(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					fn



					def __call__(      self     ,						*lowerCAmelCase     ,						**lowerCAmelCase  ):
							return self.fn(*__lowerCamelCase     ,						**__lowerCamelCase  )



					@classmethod
					def  A__		(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					list(map(self     ,						scheduler.lr_lambdas  )  )




from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__A			:					List[Any]						=			{'UserAgent': UserAgent().random}





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   script.contents[0]
    SCREAMING_SNAKE_CASE										=   json.loads(data[data.find("{\"config\""       ) : -1]       )
    return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			List[str]     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   f"https://www.instagram.com/{username}/"
       SCREAMING_SNAKE_CASE										=   self.get_json()





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   requests.get(self.url     , headers=__lowerCamelCase			).text
       SCREAMING_SNAKE_CASE										=   BeautifulSoup(__lowerCamelCase     , "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					:			Union[str, Any]			):
       return f"{self.__class__.__name__}('{self.username}')"





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





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





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





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





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





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





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





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





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





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





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





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





def 	__a     (      A__			:  str = "github"       ):
    import os

    if os.environ.get("CI"       ):
        return  # test failing on GitHub Actions
    SCREAMING_SNAKE_CASE										=   InstagramUser(A__       )
    assert instagram_user.user_data
    assert isinstance(instagram_user.user_data			,  A__       )
    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 > 150
    assert instagram_user.number_of_followers > 120000
    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()
    __A			:					Dict						=			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 = }')