


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__magic_name__       					=      logging.get_logger(__name__)





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		int  					=	'''mt5'''
						__UpperCAmelCase   :		List[Any]  					=	['''past_key_values''']




						def __init__(   self				,							a_=25_0112				,							a_=512				,							a_=64				,							a_=1024				,							a_=8				,							a_=None				,							a_=6				,							a_=32				,							a_=128				,							a_=0.1				,							a_=1E-6				,							a_=1.0				,							a_="gated-gelu"				,							a_=True				,							a_=True				,							a_="T5Tokenizer"				,							a_=False				,							a_=0				,							a_=1				,							a_=0				,							**a_				,							):
							super().__init__(
							    is_encoder_decoder=a_				,							tokenizer_class=a_				,							tie_word_embeddings=a_				,							pad_token_id=a_				,							eos_token_id=a_				,							decoder_start_token_id=a_				,							**a_				,							)
							lowerCamelCase_			:							Any      =       vocab_size
							lowerCamelCase_			:							Union[str, Any]      =       d_model
							lowerCamelCase_			:							str      =       d_kv
							lowerCamelCase_			:							List[Any]      =       d_ff
							lowerCamelCase_			:							Union[str, Any]      =       num_layers
							lowerCamelCase_			:							List[Any]      =       (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							lowerCamelCase_			:							Dict      =       num_heads
							lowerCamelCase_			:							Any      =       relative_attention_num_buckets
							lowerCamelCase_			:							Union[str, Any]      =       relative_attention_max_distance
							lowerCamelCase_			:							Union[str, Any]      =       dropout_rate
							lowerCamelCase_			:							Any      =       layer_norm_epsilon
							lowerCamelCase_			:							int      =       initializer_factor
							lowerCamelCase_			:							List[Any]      =       feed_forward_proj
							lowerCamelCase_			:							str      =       use_cache

							lowerCamelCase_			:							List[str]      =       self.feed_forward_proj.split("-"     )
							lowerCamelCase_			:							int      =       act_info[-1]
							lowerCamelCase_			:							List[Any]      =       act_info[0] == "gated"

							if len(a_     ) > 1 and act_info[0] != "gated" or len(a_     ) > 2:
								raise ValueError(
								    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
								    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
								    "'gated-gelu' or 'relu'"     )

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
								lowerCamelCase_			:							List[str]      =       "gelu_new"




						@property
						def 					_UpperCamelCase			(   self     ):
							return self.d_model




						@property
						def 					_UpperCamelCase			(   self     ):
							return self.num_heads




						@property
						def 					_UpperCamelCase			(   self     ):
							return self.num_layers





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							str      =       {
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
								lowerCamelCase_			:							List[str]      =       "past_encoder_sequence + sequence"
								lowerCamelCase_			:							List[str]      =       {0: "batch"}
								lowerCamelCase_			:							List[str]      =       {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
								lowerCamelCase_			:							Optional[int]      =       {0: "batch", 1: "decoder_sequence"}
								lowerCamelCase_			:							int      =       {0: "batch", 1: "decoder_sequence"}

							if self.use_past:
								self.fill_with_past_key_values_(a_				,							direction="inputs"     )

							return common_inputs




						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def 					_UpperCamelCase			(   self     ):
							return 13




						@property
						def 					_UpperCamelCase			(   self     ):
							return 5E-4





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

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







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"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(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

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


if __name__ == "__main__":
							main()

