


"""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   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]=10 )	->						Optional[int]:


    """simple docstring"""





    snake_case    :      Dict      					=    []
    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 )	->						List[str]:


    """simple docstring"""





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

                snake_case    :      List[str]      					=    torch.load(lowercase )
                scheduler.load_state_dict(lowercase )
    return lrs







@require_torch
class _lowerCAmelCase				(      unittest.TestCase	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		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						)	->		Dict:

       '''simple docstring'''




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

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    torch.tensor([0.1, -0.2, -0.1]						,	requires_grad=snake_case__						)
       snake_case    :      Optional[int]      					=    torch.tensor([0.4, 0.2, -0.5]						)
       snake_case    :      Optional[Any]      					=    nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       snake_case    :      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(1000						):
           snake_case    :      List[str]      					=    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 _lowerCAmelCase				(      unittest.TestCase	):
   __UpperCAmelCase :       str							  =  nn.Linear(50    ,							50	) if is_torch_available() else None
   __UpperCAmelCase :       Union[str, Any]							  =  AdamW(m.parameters()    ,							lr=10.0	) if is_torch_available() else None
   __UpperCAmelCase :       List[Any]							  =  10







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__=None						)	->		str:

       '''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						)	->		Any:

       '''simple docstring'''




       snake_case    :      Any      					=    {"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       snake_case    :      Optional[int]      					=    {
           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():
           snake_case    :      Dict      					=    data

           snake_case    :      List[Any]      					=    scheduler_func(self.optimizer						,	**snake_case__						)
           self.assertEqual(len([scheduler.get_lr()[0]]						)						,	1						)
           snake_case    :      str      					=    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'						,	)

           snake_case    :      Union[str, Any]      					=    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
           snake_case    :      List[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 _lowerCAmelCase				:







   def __init__(	self						,	UpperCamelCase__						)	->		Optional[Any]:

       '''simple docstring'''




       snake_case    :      Dict      					=    fn







   def __call__(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




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







   @classmethod
   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    list(map(self						,	scheduler.lr_lambdas						)						)









"""simple docstring"""







from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCAmelCase__      				=   100

lowerCAmelCase__      				=   set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__      				=   42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
     if prime not in primes:
          continue
     primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  if number_to_partition < 0:
    return set()
  elif number_to_partition == 0:
    return {1}

  lowerCAmelCase     :     set[int]								=							set()
  lowerCAmelCase     :     int
  lowerCAmelCase     :     int

  for prime in primes:
    if prime > number_to_partition:
      continue
    for sub in partition(number_to_partition - prime				):
      ret.add(sub * prime				)

  return ret
def 					a__			(				SCREAMING_SNAKE_CASE    :    int = 5_0_0_0				):


  '''simple docstring'''







  for number_to_partition in range(1				,      SCREAMING_SNAKE_CASE				):
    if len(partition(SCREAMING_SNAKE_CASE				)				) > number_unique_partitions:
      return number_to_partition
  return None


if __name__ == "__main__":
     print(F"{solution() = }")





