





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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=10	)       ->     int:
  lowercase__			    =						[]
  for _ in range(_snake_case	):
    lrs.append(scheduler.get_lr()[0]	)
    scheduler.step()
  return lrs




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=10	)       ->     str:
  lowercase__			    =						[]
  for step in range(_snake_case	):
    lrs.append(scheduler.get_lr()[0]	)
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        lowercase__			    =						os.path.join(_snake_case	,		'schedule.bin'	)
        torch.save(scheduler.state_dict()	,		_snake_case	)

        lowercase__			    =						torch.load(_snake_case	)
        scheduler.load_state_dict(_snake_case	)
  return lrs
@require_torch
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       List[str]    ,  a    :       Tuple    ,  a    :       Tuple )->     Union[str, Any]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     str:





     """simple docstring"""







     lowercase__			    =						torch.tensor([0.1, -0.2, -0.1]    ,  requires_grad=UpperCAmelCase_ )
     lowercase__			    =						torch.tensor([0.4, 0.2, -0.5] )
     lowercase__			    =						nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     lowercase__			    =						AdamW(params=[w]    ,  lr=2E-1    ,  weight_decay=0.0 )
     for _ in range(100 ):
       lowercase__			    =						criterion(UpperCAmelCase_    ,  UpperCAmelCase_ )
       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[str] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						torch.tensor([0.1, -0.2, -0.1]    ,  requires_grad=UpperCAmelCase_ )
     lowercase__			    =						torch.tensor([0.4, 0.2, -0.5] )
     lowercase__			    =						nn.MSELoss()
     # No warmup, constant schedule, no gradient clipping
     lowercase__			    =						Adafactor(
         params=[w]    ,  lr=1E-2    ,  eps=(1E-3_0, 1E-3)    ,  clip_threshold=1.0    ,  decay_rate=-0.8    ,  betaa=UpperCAmelCase_    ,  weight_decay=0.0    ,  relative_step=UpperCAmelCase_    ,  scale_parameter=UpperCAmelCase_    ,  warmup_init=UpperCAmelCase_    ,  )
     for _ in range(1_000 ):
       lowercase__			    =						criterion(UpperCAmelCase_    ,  UpperCAmelCase_ )
       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        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = 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					:	Any				      = 10


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Tuple    ,  a    :       Optional[int]    ,  a    :       Optional[Any]    ,  a    :       Tuple=None )->     List[str]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Any:





     """simple docstring"""







     lowercase__			    =						{'num_warmup_steps': 2, 'num_training_steps': 10}
     # schedulers doct format
     # function: (sched_args_dict, expected_learning_rates)
     lowercase__			    =						{
         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():
       lowercase__    ,					lowercase__			    =						data

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

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






class        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       Any    ,  a    :       Any )->     str:





     """simple docstring"""







     lowercase__			    =						fn


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





     """simple docstring"""







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


   @classmethod
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       Optional[int] )->     List[Any]:





     """simple docstring"""







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









from PIL import Image




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Image:

  def brightness(_SCREAMING_SNAKE_CASE	) -> float:
    return 128 + level + (c - 128)

  if not -2_5_5.0 <= level <= 2_5_5.0:
    raise ValueError('level must be between -255.0 (black) and 255.0 (white)'	)
  return img.point(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            lowercase_					     =	change_brightness(img, 100)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


