

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 		UpperCamelCase      (lowercase_:				Dict      )  ->   List[str]:
	def wrapper(*lowercase_:				List[Any]					,   **lowercase_:				str      ):
		A__							:					List[Any]       =	timeit.default_timer()
		A__							:					List[str]       =	func(*lowercase_					,   **lowercase_      )
		A__							:					Any       =	timeit.default_timer() - starttime
		return delta

	A__							:					List[str]       =	func.__name__

	return wrapper

def 		UpperCamelCase      (lowercase_:				dict					,   lowercase_:				Any=100					,   lowercase_:				List[Any]=None      )  ->   str:
	A__							:					Dict       =	[]
	A__							:					List[Any]       =	seq_shapes or {}
	for i in range(lowercase_      ):
		A__							:					Any       =	{}
		for col_id, (k, v) in enumerate(features.items()      ):
			if isinstance(lowercase_					,   _ArrayXD      ):
				A__							:					Union[str, Any]       =	np.random.rand(*v.shape      ).astype(v.dtype      )
			elif isinstance(lowercase_					,   datasets.Value      ):
				if v.dtype == "string":
					A__							:					Optional[Any]       =	"""The small grey turtle was surprisingly fast when challenged."""
				else:
					A__							:					List[str]       =	np.random.randint(10					,   size=1      ).astype(v.dtype      ).item()
			elif isinstance(lowercase_					,   datasets.Sequence      ):
				while isinstance(lowercase_					,   datasets.Sequence      ):
					A__							:					Optional[Any]       =	v.feature
				A__							:					str       =	seq_shapes[k]
				A__							:					Dict       =	np.random.rand(*lowercase_      ).astype(v.dtype      )
			A__							:					List[Any]       =	data

		dummy_data.append((i, example)      )

	return dummy_data


def 		UpperCamelCase      (lowercase_:				Any					,   lowercase_:				Tuple					,   lowercase_:				Union[str, Any]=100					,   lowercase_:				Optional[int]=None      )  ->   str:
	A__							:					Union[str, Any]       =	generate_examples(lowercase_					,   num_examples=lowercase_					,   seq_shapes=lowercase_      )

	with ArrowWriter(features=lowercase_					,   path=lowercase_      ) as writer:
		for key, record in dummy_data:
			A__							:					List[Any]       =	features.encode_example(lowercase_      )
			writer.write(lowercase_      )

		A__     ,     A__							:					List[Any]       =	writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

	A__							:					Optional[Any]       =	datasets.Dataset.from_file(filename=lowercase_					,   info=datasets.DatasetInfo(features=lowercase_      )      )

	return dataset






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				int					,   lowercase_:				int      )  ->   np.ndarray:
	A__							:					Any       =	cva.getAffineTransform(lowercase_					,   lowercase_      )
	return cva.warpAffine(lowercase_					,   lowercase_					,   (rows, cols)      )


if __name__ == "__main__":
	# read original image
	A_ :				List[Any]						 =    cva.imread(
	    str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
	)
	# turn image in gray scale value
	A_ :				List[Any]						 =    cva.cvtColor(image, cva.COLOR_BGR2GRAY)
	# get image shape
	A_ ,    A_ :				Optional[Any]						 =    gray_img.shape

	# set different points to rotate image
	A_ :				str						 =    np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A_ :				Dict						 =    np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

	# add all rotated images in a list
	A_ :				Dict						 =    [
	    gray_img,
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
	]

	# plot different image rotations
	A_ :				Union[str, Any]						 =    plt.figure(1)
	A_ :				Union[str, Any]						 =    ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
	for i, image in enumerate(images):
		plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
		plt.title(titles[i])
		plt.axis('off')
		plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
	plt.show()



