



'''simple docstring'''

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def    __lowercase	(_SCREAMING_SNAKE_CASE   :int = 8							):
				SCREAMING_SNAKE_CASE	:    Optional[int]          =			ascii_letters + digits + punctuation
				return "".join(secrets.choice(UpperCAmelCase_							) for _ in range(UpperCAmelCase_							)							)






def    __lowercase	(_SCREAMING_SNAKE_CASE   :str     ,						_SCREAMING_SNAKE_CASE   :int							):
				i -= len(UpperCAmelCase_							)
				SCREAMING_SNAKE_CASE	:    Tuple          =			i // 3
				SCREAMING_SNAKE_CASE	:    int          =			i % 3
				# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
				#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			(
				    chars_incl
				    + random(UpperCAmelCase_     ,						quotient + remainder							)
				    + random(UpperCAmelCase_     ,						UpperCAmelCase_							)
				    + random(UpperCAmelCase_     ,						UpperCAmelCase_							)
				)
				SCREAMING_SNAKE_CASE	:    Dict          =			list(UpperCAmelCase_							)
				shuffle(UpperCAmelCase_							)
				return "".join(UpperCAmelCase_							)

				# random is a generalised function for letters, characters and numbers






def    __lowercase	(_SCREAMING_SNAKE_CASE   :str     ,						_SCREAMING_SNAKE_CASE   :int							):
				return "".join(secrets.choice(UpperCAmelCase_							) for _ in range(UpperCAmelCase_							)							)






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Union[str, Any]     ,						_SCREAMING_SNAKE_CASE   :Tuple							):
				pass  # Put your code here...






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Union[str, Any]     ,						_SCREAMING_SNAKE_CASE   :str							):
				pass  # Put your code here...






def    __lowercase	(_SCREAMING_SNAKE_CASE   :str     ,						_SCREAMING_SNAKE_CASE   :Optional[int]							):
				pass  # Put your code here...






def    __lowercase	(_SCREAMING_SNAKE_CASE   :str     ,						_SCREAMING_SNAKE_CASE   :int = 8							):





				if len(UpperCAmelCase_							) < min_length:
								# Your Password must be at least 8 characters long
								return False

				SCREAMING_SNAKE_CASE	:    Dict          =			any(char in ascii_uppercase for char in password							)
				SCREAMING_SNAKE_CASE	:    Optional[int]          =			any(char in ascii_lowercase for char in password							)
				SCREAMING_SNAKE_CASE	:    Tuple          =			any(char in digits for char in password							)
				SCREAMING_SNAKE_CASE	:    Any          =			any(char in punctuation for char in password							)

				return upper and lower and num and spec_char
				# Passwords should contain UPPERCASE, lowerase
				# numbers, and special characters






def    __lowercase	():
				SCREAMING_SNAKE_CASE	:    int          =			int(input('''Please indicate the max length of your password: '''							).strip()							)
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			input(
				    '''Please indicate the characters that must be in your password: '''							).strip()
				print('''Password generated:'''     ,						password_generator(UpperCAmelCase_							)							)
				print(
				    '''Alternative Password generated:'''     ,						alternative_password_generator(UpperCAmelCase_     ,						UpperCAmelCase_							)     ,						)
				print('''[If you are thinking of using this passsword, You better save it.]'''							)


if __name__ == "__main__":
	main()








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 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





      a		:List[Any]									=			[]
      for step in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        a		:Union[str, Any]									=			os.path.join(UpperCAmelCase_					,    '''schedule.bin'''		)
                        torch.save(scheduler.state_dict()					,    UpperCAmelCase_		)

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			Adafactor(
           params=[w]   ,    lr=1e-2   ,    eps=(1e-30, 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       ):
             a		:str									=			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 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						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 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		:Any									=			{
           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():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

             a		:List[Any]									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase       )  # wrap to test picklability of the schedule
             a		:Dict									=			unwrap_and_save_reload_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    msg=F'''failed for {scheduler_func} in save and reload'''       )







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





