



"""simple docstring"""






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 			_lowercase				(  __snake_case					,__snake_case=10	)						->   Tuple:
			__lowerCAmelCase      :  List[Any]												=       []
			for _ in range(__UpperCamelCase	):
						lrs.append(scheduler.get_lr()[0]	)
						scheduler.step()
			return lrs


def 			_lowercase				(  __snake_case					,__snake_case=10	)						->   Optional[Any]:
			__lowerCAmelCase      :  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:
												__lowerCAmelCase      :  str												=       os.path.join(__UpperCamelCase					,"schedule.bin"	)
												torch.save(scheduler.state_dict()					,__UpperCamelCase	)

												__lowerCAmelCase      :  Optional[Any]												=       torch.load(__UpperCamelCase	)
												scheduler.load_state_dict(__UpperCamelCase	)
			return lrs



@require_torch
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		Dict)				->       List[str]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       torch.tensor([0.1, -0.2, -0.1]       ,			requires_grad=_lowercase)
				__lowerCAmelCase      :  str												=       torch.tensor([0.4, 0.2, -0.5])
				__lowerCAmelCase      :  Dict												=       nn.MSELoss()
				# No warmup, constant schedule, no gradient clipping
				__lowerCAmelCase      :  Dict												=       AdamW(params=[w]       ,			lr=2e-1       ,			weight_decay=0.0)
				for _ in range(100):
							__lowerCAmelCase      :  int												=       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 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       torch.tensor([0.1, -0.2, -0.1]       ,			requires_grad=_lowercase)
				__lowerCAmelCase      :  Optional[int]												=       torch.tensor([0.4, 0.2, -0.5])
				__lowerCAmelCase      :  Union[str, Any]												=       nn.MSELoss()
				# No warmup, constant schedule, no gradient clipping
				__lowerCAmelCase      :  List[Any]												=       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(1000):
							__lowerCAmelCase      :  Union[str, Any]												=       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 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	nn.Linear(5_0  ,		5_0							) if is_torch_available() else None
	SCREAMING_SNAKE_CASE          =	AdamW(m.parameters()  ,		lr=10.0							) if is_torch_available() else None
	SCREAMING_SNAKE_CASE          =	1_0

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=None)				->       Optional[int]:

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       {"num_warmup_steps": 2, "num_training_steps": 10}
				# schedulers doct format
				# function: (sched_args_dict, expected_learning_rates)
				__lowerCAmelCase      :  Union[str, 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():
							__lowerCAmelCase						,	__lowerCAmelCase      :  str												=       data

							__lowerCAmelCase      :  Any												=       scheduler_func(self.optimizer       ,			**_lowercase)
							self.assertEqual(len([scheduler.get_lr()[0]])       ,			1)
							__lowerCAmelCase      :  int												=       unwrap_schedule(_lowercase       ,			self.num_steps)
							self.assertListAlmostEqual(
							    _lowercase       ,			_lowercase       ,			tol=1e-2       ,			msg=F"""failed for {scheduler_func} in normal scheduler"""       ,			)

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






class 			A__   :




	'''simple docstring'''





	def __init__(     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       fn

	def __call__(     self:		List[str]       ,			*_SCREAMING_SNAKE_CASE:		Optional[int]       ,			**_SCREAMING_SNAKE_CASE:		List[Any])				->       Union[str, Any]:

				"""simple docstring"""

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







	@classmethod
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		List[Any])				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       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()
