


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


snake_case__  				=			(30, 487)
snake_case__  				=			(36, 1024)
snake_case__  				=			(100, 100)
snake_case__  				=			100

snake_case__  				=			datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

snake_case__				,  snake_case__  				=			os.path.split(__file__)
snake_case__  				=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[str]   ,    lowercase__	:List[Any]   ,    lowercase__	:int					)							->	Tuple:







						with ArrowWriter(features=lowercase__   ,    path=os.path.join(lowercase__   ,    'beta.arrow'					)					) as writer:
												for key, record in dummy_data:
																		__magic_name__					:				str				=   my_features.encode_example(lowercase__					)
																		writer.write(lowercase__					)
												__magic_name__					:				Optional[Any]				=   writer.finalize()
@get_duration
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[str]   ,    lowercase__	:Optional[int]					)							->	Union[str, Any]:
						__magic_name__					:				Any				=   datasets.Dataset.from_file(
						    filename=os.path.join(lowercase__   ,    'beta.arrow'					)   ,    info=datasets.DatasetInfo(features=lowercase__					)					)
						for _ in dataset:
												pass
@get_duration
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict   ,    lowercase__	:Optional[int]					)							->	List[str]:
						__magic_name__					:				Tuple				=   datasets.Dataset.from_file(
						    filename=os.path.join(lowercase__   ,    'beta.arrow'					)   ,    info=datasets.DatasetInfo(features=lowercase__					)					)
						dataset.set_format('numpy'					)
						for _ in dataset:
												pass
@get_duration
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Any   ,    lowercase__	:List[Any]					)							->	int:
						__magic_name__					:				int				=   1_0
						__magic_name__					:				Union[str, Any]				=   datasets.Dataset.from_file(
						    filename=os.path.join(lowercase__   ,    'beta.arrow'					)   ,    info=datasets.DatasetInfo(features=lowercase__					)					)
						for i in range(0   ,    len(lowercase__					)   ,    lowercase__					):
												__magic_name__					:				Optional[int]				=   dataset[i : i + batch_size]
@get_duration
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Union[str, Any]   ,    lowercase__	:Optional[int]					)							->	Union[str, Any]:
						__magic_name__					:				Tuple				=   1_0
						__magic_name__					:				Any				=   datasets.Dataset.from_file(
						    filename=os.path.join(lowercase__   ,    'beta.arrow'					)   ,    info=datasets.DatasetInfo(features=lowercase__					)					)
						dataset.set_format('numpy'					)
						for i in range(0   ,    len(lowercase__					)   ,    lowercase__					):
												__magic_name__					:				Tuple				=   dataset[i : i + batch_size]
@get_duration
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict   ,    lowercase__	:Dict					)							->	int:
						__magic_name__					:				List[str]				=   datasets.Dataset.from_file(
						    filename=os.path.join(lowercase__   ,    'beta.arrow'					)   ,    info=datasets.DatasetInfo(features=lowercase__					)					)
						for col in feats:
												__magic_name__					:				Union[str, Any]				=   dataset[col]
@get_duration
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[str]   ,    lowercase__	:int					)							->	List[str]:
						__magic_name__					:				Tuple				=   datasets.Dataset.from_file(
						    filename=os.path.join(lowercase__   ,    'beta.arrow'					)   ,    info=datasets.DatasetInfo(features=lowercase__					)					)
						dataset.set_format('numpy'					)
						for col in feats:
												__magic_name__					:				str				=   dataset[col]
def 			__SCREAMING_SNAKE_CASE							(					)							->	Optional[int]:
						__magic_name__					:				Dict				=   {}
						__magic_name__					:				Dict				=   (
						    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:
												__magic_name__					:				str				=   datasets.Features({'image': ArrayaD(lowercase__   ,    dtype='float32'					)}					)
												__magic_name__					:				Any				=   generate_examples(features=lowercase__   ,    num_examples=lowercase__					)
												__magic_name__					:				Union[str, Any]				=   write(lowercase__   ,    lowercase__   ,    lowercase__					)
												for read_func in read_functions:
																		__magic_name__					:				List[str]				=   read_func(lowercase__   ,    lowercase__					)

						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])}
												# )
												__magic_name__					:				str				=   datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'					)					)					)}					)
												__magic_name__					:				str				=   generate_examples(
												    features=lowercase__   ,    num_examples=lowercase__   ,    seq_shapes={'image': SPEED_TEST_SHAPE}					)
												__magic_name__					:				Tuple				=   write(lowercase__   ,    lowercase__   ,    lowercase__					)
												for read_func in read_functions:
																		__magic_name__					:				Dict				=   read_func(lowercase__   ,    lowercase__					)

						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])}
												# )
												__magic_name__					:				List[Any]				=   datasets.Features({'image': datasets.Sequence(datasets.Value('float32'					)					)}					)
												__magic_name__					:				Any				=   generate_examples(
												    features=lowercase__   ,    num_examples=lowercase__   ,    seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}   ,    )
												__magic_name__					:				Union[str, Any]				=   write(lowercase__   ,    lowercase__   ,    lowercase__					)
												for read_func in read_functions:
																		__magic_name__					:				Optional[Any]				=   read_func(lowercase__   ,    lowercase__					)

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


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





"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
