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_  (		lowercase:				Union[str, Any]	,       lowercase:				str=10		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[Any]    						=			[]
 for _ in range(lowercase		):
  lrs.append(scheduler.get_lr()[0]		)
  scheduler.step()
 return lrs




def    lowerCAmelCase_  (		lowercase:				Union[str, Any]	,       lowercase:				Union[str, Any]=10		)     ->					str:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			[]
 for step in range(lowercase		):
  lrs.append(scheduler.get_lr()[0]		)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    _UpperCamelCase:  Optional[Any]    						=			os.path.join(lowercase	,       '''schedule.bin'''		)
    torch.save(scheduler.state_dict()	,       lowercase		)

    _UpperCamelCase:  str    						=			torch.load(lowercase		)
    scheduler.load_state_dict(lowercase		)
 return lrs




@require_torch
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	Any		,	_lowercase       :	List[Any]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			torch.tensor([0.1, -0.2, -0.1]		,	requires_grad=snake_case__				)
    _UpperCamelCase:  Optional[int]    						=			torch.tensor([0.4, 0.2, -0.5]				)
    _UpperCamelCase:  Dict    						=			nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    _UpperCamelCase:  Union[str, Any]    						=			AdamW(params=[w]		,	lr=2E-1		,	weight_decay=0.0				)
    for _ in range(100				):
     _UpperCamelCase:  Dict    						=			criterion(snake_case__		,	snake_case__				)
     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 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			torch.tensor([0.1, -0.2, -0.1]		,	requires_grad=snake_case__				)
    _UpperCamelCase:  Union[str, Any]    						=			torch.tensor([0.4, 0.2, -0.5]				)
    _UpperCamelCase:  Optional[int]    						=			nn.MSELoss()
    # No warmup, constant schedule, no gradient clipping
    _UpperCamelCase:  List[str]    						=			Adafactor(
        params=[w]		,	lr=1E-2		,	eps=(1E-30, 1E-3)		,	clip_threshold=1.0		,	decay_rate=-0.8		,	betaa=snake_case__		,	weight_decay=0.0		,	relative_step=snake_case__		,	scale_parameter=snake_case__		,	warmup_init=snake_case__		,	)
    for _ in range(1_000				):
     _UpperCamelCase:  int    						=			criterion(snake_case__		,	snake_case__				)
     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 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = nn.Linear(5_0   ,						5_0     ) if is_torch_available() else None
   lowerCAmelCase   :     Optional[int]	      = AdamW(m.parameters()   ,						lr=1_0.0     ) if is_torch_available() else None
   lowerCAmelCase   :     Tuple	      = 1_0



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	Optional[int]		,	_lowercase       :	Tuple		,	_lowercase       :	Dict		,	_lowercase       :	int=None				):



    """simple docstring"""



    self.assertEqual(len(snake_case__				)		,	len(snake_case__				)				)
    for a, b in zip(snake_case__		,	snake_case__				):
     self.assertAlmostEqual(snake_case__		,	snake_case__		,	delta=snake_case__		,	msg=snake_case__				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			{"""num_warmup_steps""": 2, """num_training_steps""": 10}
    # schedulers doct format
    # function: (sched_args_dict, expected_learning_rates)
    _UpperCamelCase:  Dict    						=			{
        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:  int    						=			data

     _UpperCamelCase:  Union[str, Any]    						=			scheduler_func(self.optimizer		,	**snake_case__				)
     self.assertEqual(len([scheduler.get_lr()[0]]				)		,	1				)
     _UpperCamelCase:  Optional[Any]    						=			unwrap_schedule(snake_case__		,	self.num_steps				)
     self.assertListAlmostEqual(
         snake_case__		,	snake_case__		,	tol=1E-2		,	msg=f"""failed for {scheduler_func} in normal scheduler"""		,	)

     _UpperCamelCase:  Optional[int]    						=			scheduler_func(self.optimizer		,	**snake_case__				)
     if scheduler_func.__name__ != "get_constant_schedule":
      LambdaScheduleWrapper.wrap_scheduler(snake_case__				)  # wrap to test picklability of the schedule
     _UpperCamelCase:  Optional[Any]    						=			unwrap_and_save_reload_schedule(snake_case__		,	self.num_steps				)
     self.assertListEqual(snake_case__		,	snake_case__		,	msg=f"""failed for {scheduler_func} in save and reload"""				)




class 			__magic_name__	:





   """simple docstring"""




   def __init__(						self       :	int		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			fn



   def __call__(						self       :	int		,	*_lowercase       :	Optional[Any]		,	**_lowercase       :	int				):



    """simple docstring"""



    return self.fn(*snake_case__		,	**snake_case__				)



   @classmethod
   def 	lowerCAmelCase    (						self       :	int		,	_lowercase       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			list(map(self		,	scheduler.lr_lambdas				)				)






from string import ascii_uppercase

UpperCAmelCase_							=				{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_							=				dict(enumerate(ascii_uppercase))





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Dict  				=     len(lowercase				)
							lowercase_	:   Tuple  				=     0
							while True:
														if x == i:
																					lowercase_	:   Dict  				=     0
														if len(lowercase				) == len(lowercase				):
																					break
														key += key[i]
														i += 1
							return key





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Any  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					lowercase_	:   Any  				=     (dicta[letter] - dicta[key_new[i]]) % 26
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					lowercase_	:   Optional[int]  				=     (dicta[letter] + dicta[key_new[i]] + 26) % 26
																					i += 1
																					or_txt += dicta[x]
							return or_txt





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     """THE GERMAN ATTACK"""
							lowercase_	:   str  				=     """SECRET"""
							lowercase_	:   int  				=     generate_key(lowercase				,       lowercase				)
							lowercase_	:   List[str]  				=     cipher_text(lowercase				,       lowercase				)
							print(f"""Encrypted Text = {s}"""				)
							print(f"""Original Text = {original_text(lowercase				,       lowercase				)}"""				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()