import math
import os

import cva
import numpy as np

_SCREAMING_SNAKE_CASE   		=			2_55.0





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_					)		->  float:







 '''simple docstring'''
 UpperCamelCase      =			np.mean((original - contrast) ** 2					)
 if mse == 0:
  return 100

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





def  lowercase(  )		->  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()

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
     import transformer_engine.pytorch as te





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True       ,				UpperCamelCase_=True					)		->  int:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""					)
 for name, module in model.named_children():
  if isinstance(UpperCamelCase_       ,				nn.Linear					) and to_transformer_engine and _convert_linear:
   # Return early if the linear layer weights are not multiples of 16
   if any(p % 16 != 0 for p in module.weight.shape					):
    return
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			te.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				nn.LayerNorm					) and to_transformer_engine and _convert_ln:
   UpperCamelCase      =			te.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.Linear					) and not to_transformer_engine and _convert_linear:
   UpperCamelCase      =			module.bias is not None
   UpperCamelCase      =			nn.Linear(
       module.in_features       ,				module.out_features       ,				bias=UpperCamelCase_       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   if has_bias:
    UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  elif isinstance(UpperCamelCase_       ,				te.LayerNorm					) and not to_transformer_engine and _convert_ln:
   UpperCamelCase      =			nn.LayerNorm(module.normalized_shape[0]       ,				eps=module.eps       ,				params_dtype=module.weight.dtype					)
   UpperCamelCase      =			module.weight.data.clone()
   UpperCamelCase      =			module.bias.data.clone()

   setattr(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  else:
   convert_model(
       UpperCamelCase_       ,				to_transformer_engine=UpperCamelCase_       ,				_convert_linear=_convert_linear       ,				_convert_ln=_convert_ln       ,				)





def  lowercase(  UpperCamelCase_					)		->  Any:







 '''simple docstring'''
 if not is_fpa_available():
  raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""					)
 for m in model.modules():
  if isinstance(UpperCamelCase_       ,				(te.LayerNorm, te.Linear)					):
   return True
 return False
