

"""simple docstring"""


import re
import string

import numpy as np

import datasets


__A						       =      "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

__A						       =      "\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

__A						       =      "\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,							_KWARGS_DESCRIPTION  )
class    snake_case     (    datasets.Metric  ):

				def 							lowercase_					(     self							:    List[Any])->       Optional[int]:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"					,  id="sequence"),
						            "references": datasets.Value("string"					,  id="sequence"),
						        })					,  reference_urls=[]					,  )




				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    Union[str, Any]=False					,  UpperCamelCase__							:    List[Any]=False					,  UpperCamelCase__							:    int=False					,  )->       Union[str, Any]:



						'''simple docstring'''


						if regexes_to_ignore is not None:
								for s in regexes_to_ignore:
										__lowerCAmelCase:	Optional[int]   				=      np.array([re.sub(UpperCamelCase__					,  ""					,  UpperCamelCase__) for x in predictions])
										__lowerCAmelCase:	Optional[int]   				=      np.array([re.sub(UpperCamelCase__					,  ""					,  UpperCamelCase__) for x in references])
						else:
								__lowerCAmelCase:	int   				=      np.asarray(UpperCamelCase__)
								__lowerCAmelCase:	Optional[int]   				=      np.asarray(UpperCamelCase__)

						if ignore_case:
								__lowerCAmelCase:	int   				=      np.char.lower(UpperCamelCase__)
								__lowerCAmelCase:	str   				=      np.char.lower(UpperCamelCase__)

						if ignore_punctuation:
								__lowerCAmelCase:	Optional[int]   				=      string.punctuation.maketrans(""					,  ""					,  string.punctuation)
								__lowerCAmelCase:	Any   				=      np.char.translate(UpperCamelCase__					,  table=UpperCamelCase__)
								__lowerCAmelCase:	Dict   				=      np.char.translate(UpperCamelCase__					,  table=UpperCamelCase__)

						if ignore_numbers:
								__lowerCAmelCase:	Union[str, Any]   				=      string.digits.maketrans(""					,  ""					,  string.digits)
								__lowerCAmelCase:	Optional[int]   				=      np.char.translate(UpperCamelCase__					,  table=UpperCamelCase__)
								__lowerCAmelCase:	Optional[int]   				=      np.char.translate(UpperCamelCase__					,  table=UpperCamelCase__)

						__lowerCAmelCase:	Dict   				=      predictions == references

						return {"exact_match": np.mean(UpperCamelCase__) * 1_0_0}





"""simple docstring"""


from pathlib import Path

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


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

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    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						       =      plt.figure(1)
			__A						       =      ["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()


