code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import math
  import os
  
  import cva
  import numpy as np
  
  UpperCAmelCase__      =							255.0
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   float,_SCREAMING_SNAKE_CASE				:   float   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  np.mean((original - contrast) ** 2   )
  			if mse == 0:
  						return 100
  
  			return 20 * math.logaa(PIXEL_MAX / math.sqrt(_SCREAMING_SNAKE_CASE   )   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  os.path.dirname(os.path.realpath(_SCREAMING_SNAKE_CASE   )   )
  			# Loading images (original image and compressed image)
  			__A=  cva.imread(os.path.join(_SCREAMING_SNAKE_CASE,'image_data/original_image.png'   )   )
  			__A=  cva.imread(os.path.join(_SCREAMING_SNAKE_CASE,'image_data/compressed_image.png'   ),1   )
  
  			__A=  cva.imread(os.path.join(_SCREAMING_SNAKE_CASE,'image_data/PSNR-example-base.png'   )   )
  			__A=  cva.imread(
  			    os.path.join(_SCREAMING_SNAKE_CASE,'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(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )} dB"""   )
  
  			# # Value expected: 31.53dB (Wikipedia Example)
  			print('\n-- Second Test --'   )
  			print(f"""PSNR value is {peak_signal_to_noise_ratio(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )} dB"""   )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  			A	:							Optional[int]							= '''mobilebert'''
  			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  hidden_act
  						__A=  intermediate_size
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  max_position_embeddings
  						__A=  type_vocab_size
  						__A=  initializer_range
  						__A=  layer_norm_eps
  						__A=  embedding_size
  						__A=  trigram_input
  						__A=  use_bottleneck
  						__A=  intra_bottleneck_size
  						__A=  use_bottleneck_attention
  						__A=  key_query_shared_bottleneck
  						__A=  num_feedforward_networks
  						__A=  normalization_type
  						__A=  classifier_activation
  
  						if self.use_bottleneck:
  									__A=  intra_bottleneck_size
  						else:
  									__A=  hidden_size
  
  						__A=  classifier_dropout
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
  						if self.task == "multiple-choice":
  									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
  						else:
  									__A=  {0: 'batch', 1: 'sequence'}
  						return OrderedDict(
  						    [
  						        ('input_ids', dynamic_axis),
  						        ('attention_mask', dynamic_axis),
  						        ('token_type_ids', dynamic_axis),
  						    ]  )
  
  
  
  