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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       Optional[int]=10	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      []
  for _ in range(__UpperCamelCase	):
    lrs.append(scheduler.get_lr()[0]	)
    scheduler.step()
  return lrs



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       List[str]=10	)							->					Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      []
  for step in range(__UpperCamelCase	):
    lrs.append(scheduler.get_lr()[0]	)
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        SCREAMING_SNAKE_CASE__										=      os.path.join(__UpperCamelCase ,     """schedule.bin"""	)
        torch.save(scheduler.state_dict() ,     __UpperCamelCase	)

        SCREAMING_SNAKE_CASE__										=      torch.load(__UpperCamelCase	)
        scheduler.load_state_dict(__UpperCamelCase	)
  return lrs







@require_torch
class __snake_case				(						unittest.TestCase		):




   def 	__a						(    self			:				Dict       ,	_lowercase			:				str       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Dict      ):



     """simple docstring"""






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




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      torch.tensor([0.1, -0.2, -0.1]       ,	requires_grad=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.tensor([0.4, 0.2, -0.5]      )
     SCREAMING_SNAKE_CASE__										=      nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     SCREAMING_SNAKE_CASE__										=      AdamW(params=[w]       ,	lr=2E-1       ,	weight_decay=0.0      )
     for _ in range(1_00      ):
       SCREAMING_SNAKE_CASE__										=      criterion(_lowercase       ,	_lowercase      )
       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			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      torch.tensor([0.1, -0.2, -0.1]       ,	requires_grad=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.tensor([0.4, 0.2, -0.5]      )
     SCREAMING_SNAKE_CASE__										=      nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     SCREAMING_SNAKE_CASE__										=      Adafactor(
         params=[w]       ,	lr=1E-2       ,	eps=(1E-30, 1E-3)       ,	clip_threshold=1.0       ,	decay_rate=-0.8       ,	betaa=_lowercase       ,	weight_decay=0.0       ,	relative_step=_lowercase       ,	scale_parameter=_lowercase       ,	warmup_init=_lowercase       ,	)
     for _ in range(10_00      ):
       SCREAMING_SNAKE_CASE__										=      criterion(_lowercase       ,	_lowercase      )
       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		):
   lowerCAmelCase_														=							nn.Linear(50   ,		50		) if is_torch_available() else None
   lowerCAmelCase_														=							AdamW(m.parameters()   ,		lr=10.0		) if is_torch_available() else None
   lowerCAmelCase_														=							10




   def 	__a						(    self			:				str       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				int       ,	_lowercase			:				Optional[int]=None      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {"""num_warmup_steps""": 2, """num_training_steps""": 10}
     # schedulers doct format
     # function: (sched_args_dict, expected_learning_rates)
     SCREAMING_SNAKE_CASE__										=      {
         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, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
         ),
         get_inverse_sqrt_schedule: (
             {"""num_warmup_steps""": 2},
             [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
         ),
     }

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

       SCREAMING_SNAKE_CASE__										=      scheduler_func(self.optimizer       ,	**_lowercase      )
       self.assertEqual(len([scheduler.get_lr()[0]]      )       ,	1      )
       SCREAMING_SNAKE_CASE__										=      unwrap_schedule(_lowercase       ,	self.num_steps      )
       self.assertListAlmostEqual(
           _lowercase       ,	_lowercase       ,	tol=1E-2       ,	msg=f"""failed for {scheduler_func} in normal scheduler"""       ,	)

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







class __snake_case				:




   def __init__(    self			:				Optional[Any]       ,	_lowercase			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      fn




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



     """simple docstring"""






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




   @classmethod
   def 	__a						(    self			:				Tuple       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      list(map(self       ,	scheduler.lr_lambdas      )      )

import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
