

"""simple docstring"""




# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json






class _UpperCamelCase							(							UpperCamelCase_				):
			'''simple docstring'''







			@classmethod
			def 							__UpperCamelCase	(     cls							:	Any		)							->    Union[str, Any]:

				"""simple docstring"""

				return cls






			@slow
			@require_torch_gpu
			def 							__UpperCamelCase	(     self							:	List[Any]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.get_auto_remove_tmp_dir()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    F"\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        ".split()

				# we want this test to run even if there is only one GPU, but if there are more we use them all
				SCREAMING_SNAKE_CASE				: Dict			       =    get_gpu_count()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    [sys.executable] + distributed_args + args
				execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=self.get_env()		)

				SCREAMING_SNAKE_CASE				: Dict			       =    os.path.join(__SCREAMING_SNAKE_CASE , "test_bleu.json"		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    load_json(__SCREAMING_SNAKE_CASE		)
				# print(metrics)
				self.assertGreaterEqual(metrics["bleu"] , 25		)


import math
import os

import cva
import numpy as np

a_         =      255.0

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

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

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: List[Any]			       =    os.path.dirname(os.path.realpath(_a))
	# Loading images (original image and compressed image)
	SCREAMING_SNAKE_CASE				: Any			       =    cva.imread(os.path.join(_a							,     "image_data/original_image.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(os.path.join(_a							,     "image_data/compressed_image.png")							,     1)

	SCREAMING_SNAKE_CASE				: List[Any]			       =    cva.imread(os.path.join(_a							,     "image_data/PSNR-example-base.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(
	    os.path.join(_a							,     "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(_a							,     _a)} dB")

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


if __name__ == "__main__":
			main()