






"""simple docstring"""



import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__A		:	Union[str, Any]      =		(30, 487)
__A		:	Union[str, Any]      =		(36, 1_024)
__A		:	int      =		(100, 100)
__A		:	Union[str, Any]      =		100

__A		:	Union[str, Any]      =		datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

__A    ,      __A		:	Optional[int]      =		os.path.split(__file__)
__A		:	Tuple      =		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))



@get_duration
def 						lowercase   (    __snake_case    :			Optional[int]		, __snake_case    :			Union[str, Any]		, __snake_case    :			Union[str, Any]     ):
		with ArrowWriter(features=__snake_case		, path=os.path.join(__snake_case		, '''beta.arrow'''     )     ) as writer:
				for key, record in dummy_data:
						lowercase_			:			List[str]								=       my_features.encode_example(__snake_case     )
						writer.write(__snake_case     )
				lowercase_	, lowercase_			:			List[Any]								=       writer.finalize()



@get_duration
def 						lowercase   (    __snake_case    :			str		, __snake_case    :			Union[str, Any]     ):
		lowercase_			:			Union[str, Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		for _ in dataset:
				pass



@get_duration
def 						lowercase   (    __snake_case    :			Optional[Any]		, __snake_case    :			Tuple     ):
		lowercase_			:			List[Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		dataset.set_format('''numpy'''     )
		for _ in dataset:
				pass



@get_duration
def 						lowercase   (    __snake_case    :			Any		, __snake_case    :			str     ):
		lowercase_			:			Any								=       1_0
		lowercase_			:			str								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		for i in range(0		, len(__snake_case     )		, __snake_case     ):
				lowercase_			:			Union[str, Any]								=       dataset[i : i + batch_size]



@get_duration
def 						lowercase   (    __snake_case    :			List[str]		, __snake_case    :			List[str]     ):
		lowercase_			:			Dict								=       1_0
		lowercase_			:			Optional[Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		dataset.set_format('''numpy'''     )
		for i in range(0		, len(__snake_case     )		, __snake_case     ):
				lowercase_			:			Union[str, Any]								=       dataset[i : i + batch_size]



@get_duration
def 						lowercase   (    __snake_case    :			Tuple		, __snake_case    :			Tuple     ):
		lowercase_			:			List[Any]								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		for col in feats:
				lowercase_			:			Tuple								=       dataset[col]



@get_duration
def 						lowercase   (    __snake_case    :			Optional[Any]		, __snake_case    :			int     ):
		lowercase_			:			int								=       datasets.Dataset.from_file(
		    filename=os.path.join(__snake_case		, '''beta.arrow'''     )		, info=datasets.DatasetInfo(features=__snake_case     )     )
		dataset.set_format('''numpy'''     )
		for col in feats:
				lowercase_			:			int								=       dataset[col]



def 						lowercase   (    ):
		lowercase_			:			List[Any]								=       {}
		lowercase_			:			Union[str, Any]								=       (
		    read_unformated,
		    read_formatted_as_numpy,
		    read_batch_unformated,
		    read_batch_formatted_as_numpy,
		    read_col_unformated,
		    read_col_formatted_as_numpy,
		)
		with tempfile.TemporaryDirectory() as tmp_dir:
				lowercase_			:			Union[str, Any]								=       datasets.Features({'''image''': ArrayaD(__snake_case		, dtype='''float32'''     )}     )
				lowercase_			:			Any								=       generate_examples(features=__snake_case		, num_examples=__snake_case     )
				lowercase_			:			Any								=       write(__snake_case		, __snake_case		, __snake_case     )
				for read_func in read_functions:
						lowercase_			:			List[Any]								=       read_func(__snake_case		, __snake_case     )

		with tempfile.TemporaryDirectory() as tmp_dir:
				# don't use fixed length for fair comparison
				# feats = datasets.Features(
				#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
				# )
				lowercase_			:			List[str]								=       datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''     )     )     )}     )
				lowercase_			:			Dict								=       generate_examples(
				    features=__snake_case		, num_examples=__snake_case		, seq_shapes={'''image''': SPEED_TEST_SHAPE}     )
				lowercase_			:			str								=       write(__snake_case		, __snake_case		, __snake_case     )
				for read_func in read_functions:
						lowercase_			:			Dict								=       read_func(__snake_case		, __snake_case     )

		with tempfile.TemporaryDirectory() as tmp_dir:
				# don't use fixed length for fair comparison
				# feats = datasets.Features(
				#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
				# )
				lowercase_			:			Any								=       datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''     )     )}     )
				lowercase_			:			List[Any]								=       generate_examples(
				    features=__snake_case		, num_examples=__snake_case		, seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		, )
				lowercase_			:			int								=       write(__snake_case		, __snake_case		, __snake_case     )
				for read_func in read_functions:
						lowercase_			:			Any								=       read_func(__snake_case		, __snake_case     )

		with open(__snake_case		, '''wb'''     ) as f:
				f.write(json.dumps(__snake_case     ).encode('''utf-8'''     )     )


if __name__ == "__main__":  # useful to run the profiler
						benchmark_array_xd()











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__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(__snake_case		, __snake_case     )} dB'''     )

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


if __name__ == "__main__":
						main()



