




import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin





def 							lowerCamelCase    (							UpperCAmelCase_	:      List[str]=2   ,       UpperCAmelCase_	:      List[Any]=True   ,       UpperCAmelCase_	:      List[Any]=False      )->			Any:

       """simple docstring"""




       a			   =Accelerator(step_scheduler_with_optimizer=UpperCAmelCase_   ,       split_batches=UpperCAmelCase_      )
       a			   =torch.nn.Linear(2   ,       4      )
       a			   =torch.optim.AdamW(model.parameters()   ,       lr=1.0      )
       a			   =torch.optim.lr_scheduler.OneCycleLR(UpperCAmelCase_   ,       max_lr=0.01   ,       steps_per_epoch=2   ,       epochs=1      )
       a   ,      a   ,      a			   =accelerator.prepare(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_      )

       # Optimizer has stepped
       scheduler.step()
       if step_scheduler_with_optimizer or (num_processes == 1):
              assert (
                  scheduler.scheduler.last_epoch == num_processes
              ), F'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
       else:
              assert (
                  scheduler.scheduler.last_epoch != num_processes
              ), F'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''





def 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[Any]=2   ,       UpperCAmelCase_	:      Optional[int]=True   ,       UpperCAmelCase_	:      Optional[Any]=False      )->			Dict:

       """simple docstring"""




       a			   =Accelerator(step_scheduler_with_optimizer=UpperCAmelCase_   ,       split_batches=UpperCAmelCase_      )
       a			   =torch.nn.Linear(2   ,       4      )
       a			   =torch.optim.AdamW(model.parameters()   ,       lr=1.0      )
       a			   =torch.optim.lr_scheduler.LambdaLR(UpperCAmelCase_   ,       lr_lambda=lambda UpperCAmelCase_      : 1 - n / 10      )
       a   ,      a   ,      a			   =accelerator.prepare(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_      )

       # Optimizer has stepped
       a			   =False
       scheduler.step()
       a			   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
       assert (
           scheduler.get_last_lr()[0] == expected_lr
       ), F'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

       # Optimizer has not stepped
       a			   =True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              a			   =1 - 2 / 10
       assert (
           scheduler.get_last_lr()[0] == expected_lr
       ), F'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''





def 							lowerCamelCase    (							UpperCAmelCase_	:      int = 2      )->			Optional[Any]:

       """simple docstring"""




       from transformers import get_linear_schedule_with_warmup

       a			   =[1, 2, 4]
       for num_steps in steps:
              a			   =GradientAccumulationPlugin(num_steps=UpperCAmelCase_   ,       adjust_scheduler=num_steps > 1      )
              a			   =Accelerator(gradient_accumulation_plugin=UpperCAmelCase_      )
              a			   =torch.nn.Linear(2   ,       4      )
              a			   =torch.optim.AdamW(model.parameters()   ,       lr=10.0      )
              a			   =get_linear_schedule_with_warmup(optimizer=UpperCAmelCase_   ,       num_warmup_steps=0   ,       num_training_steps=20      )

              a   ,      a   ,      a			   =accelerator.prepare(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_      )

              for i in range(10 * num_steps      ):
                     with accelerator.accumulate(UpperCAmelCase_      ):
                            optimizer.step()
                            scheduler.step()

                     if i == (10 * num_steps - 2):
                            assert (
                                scheduler.get_last_lr()[0] != 0
                            ), F'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
              assert (
                  scheduler.get_last_lr()[0] == 0
              ), F'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
              GradientState._reset_state()






@require_cpu
class        UpperCAmelCase__				(							unittest.TestCase				):



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1	) ,    num_processes=1	)
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1 ,    split_batches=_lowerCAmelCase	) ,    num_processes=1	)
     def        lowerCAmelCase__  (				self	):
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1	) ,    num_processes=1	)
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1 ,    split_batches=_lowerCAmelCase	) ,    num_processes=1	)
     def        lowerCAmelCase__  (				self	):
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1 ,    step_scheduler_with_optimizer=_lowerCAmelCase	) ,    num_processes=1	)
     def        lowerCAmelCase__  (				self	):
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1 ,    step_scheduler_with_optimizer=_lowerCAmelCase	) ,    num_processes=1	)
     def        lowerCAmelCase__  (				self	):
            AcceleratorState._reset_state(_lowerCAmelCase	)
            debug_launcher(_lowerCAmelCase	)
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1 ,    split_batches=_lowerCAmelCase	) ,    num_processes=1	)
     def        lowerCAmelCase__  (				self	):
            AcceleratorState._reset_state(_lowerCAmelCase	)
            debug_launcher(_lowerCAmelCase	)
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1 ,    split_batches=_lowerCAmelCase	) ,    num_processes=1	)
     def        lowerCAmelCase__  (				self	):
            AcceleratorState._reset_state(_lowerCAmelCase	)
            debug_launcher(partial(_lowerCAmelCase ,    step_scheduler_with_optimizer=_lowerCAmelCase	)	)
     def        lowerCAmelCase__  (				self	):
            AcceleratorState._reset_state(_lowerCAmelCase	)
            debug_launcher(partial(_lowerCAmelCase ,    step_scheduler_with_optimizer=_lowerCAmelCase	)	)






     @require_huggingface_suite
     def        lowerCAmelCase__  (				self	):
            AcceleratorState._reset_state(_lowerCAmelCase	)
            debug_launcher(partial(_lowerCAmelCase ,    num_processes=1	)	)
            debug_launcher(_lowerCAmelCase	)






import math
import os

import cva
import numpy as np

_lowerCamelCase							=      255.0





def 							lowerCamelCase    (							UpperCAmelCase_	:      float   ,       UpperCAmelCase_	:      float      )->			float:

       """simple docstring"""




       a			   =np.mean((original - contrast) ** 2      )
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       a			   =os.path.dirname(os.path.realpath(UpperCAmelCase_      )      )
       # Loading images (original image and compressed image)
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/original_image.png"""      )      )
       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/compressed_image.png"""      )   ,       1      )

       a			   =cva.imread(os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-base.png"""      )      )
       a			   =cva.imread(
           os.path.join(UpperCAmelCase_   ,       """image_data/PSNR-example-comp-10.jpg"""      )   ,       1      )

       # Value expected: 29.73dB
       print("""-- First Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )

       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""      )
       print(F'''PSNR value is {peak_signal_to_noise_ratio(UpperCAmelCase_   ,       UpperCAmelCase_      )} dB'''      )


if __name__ == "__main__":
    main()
