


'''simple docstring'''

import math
import os

import cva
import numpy as np

__lowercase:		Tuple    						=  2_5_5.0



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					float     ,		_UpperCamelCase   :					float )		->       float:





   '''simple docstring'''







   UpperCamelCase__			    =    np.mean((original - contrast) ** 2 )
   if mse == 0:
      return 1_00

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



def 							SCREAMING_SNAKE_CASE__(		)		->       None:





   '''simple docstring'''







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

   UpperCamelCase__			    =    cva.imread(os.path.join(_UpperCamelCase     ,		"image_data/PSNR-example-base.png" ) )
   UpperCamelCase__			    =    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()

"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }

      return inputs
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()    ,     reason="XFormers attention is only available with CUDA and `xformers` installed"    ,     )
    def        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1E-1  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


    def        lowerCamelCase__						(     self  ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2    ,     )

