





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







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


	lowerCamelCase_			:							Dict      =       CanineConfig()
	lowerCamelCase_			:							Any      =       CanineModel(lowerCAmelCase_)
	model.eval()

	print(F"""Building PyTorch model from configuration: {config}""")

	# Load weights from tf checkpoint
	load_tf_weights_in_canine(lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_)

	# Save pytorch-model (weights and configuration)
	print(F"""Save PyTorch model to {pytorch_dump_path}""")
	model.save_pretrained(lowerCAmelCase_)

	# Save tokenizer files
	lowerCamelCase_			:							Tuple      =       CanineTokenizer()
	print(F"""Save tokenizer files to {pytorch_dump_path}""")
	tokenizer.save_pretrained(lowerCAmelCase_)


if __name__ == "__main__":
							__magic_name__       					=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--tf_checkpoint_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''',
							    default=None,
							    type=str,
							    required=True,
							    help='''Path to a folder where the PyTorch model will be placed.''',
							)
							__magic_name__       					=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)





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()

