
'''simple docstring'''







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase					:							str   =      version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
					from nltk import word_tokenize


lowercase					:							int   =      "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowercase					:							Any   =      "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowercase					:							Optional[Any]   =      "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"


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


							def 				lowerCamelCase       ( self						):





								"""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'						),
								        }						)					,     codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']					,     reference_urls=[
								        'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
								        'https://en.wikipedia.org/wiki/METEOR',
								    ]					,     )


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								import nltk

								nltk.download('wordnet'						)
								if NLTK_VERSION >= version.Version('3.6.5'						):
									nltk.download('punkt'						)
								if NLTK_VERSION >= version.Version('3.6.6'						):
									nltk.download('omw-1.4'						)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_=0.9					,     lowerCAmelCase_=3					,     lowerCAmelCase_=0.5						):





								"""simple docstring"""






								if NLTK_VERSION >= version.Version('3.6.5'						):
									_snake_case     		=	[
									    meteor_score.single_meteor_score(
									        word_tokenize(lowerCAmelCase_						)					,     word_tokenize(lowerCAmelCase_						)					,     alpha=lowerCAmelCase_					,     beta=lowerCAmelCase_					,     gamma=lowerCAmelCase_						)
									    for ref, pred in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
									]
								else:
									_snake_case     		=	[
									    meteor_score.single_meteor_score(lowerCAmelCase_					,     lowerCAmelCase_					,     alpha=lowerCAmelCase_					,     beta=lowerCAmelCase_					,     gamma=lowerCAmelCase_						)
									    for ref, pred in zip(lowerCAmelCase_					,     lowerCAmelCase_						)
									]

								return {"meteor": np.mean(lowerCAmelCase_						)}



'''simple docstring'''







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowercase					:							int   =      ""
lowercase					:							Union[str, Any]   =      ""
lowercase					:							Tuple   =      ""
lowercase					:							Optional[int]   =      1  # (0 is vertical, 1 is horizontal)







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case		,				_snake_case     		=	get_dataset(__A    ,       __A			)
	print('Processing...'			)
	_snake_case		,				_snake_case		,				_snake_case     		=	update_image_and_anno(__A    ,       __A    ,       __A			)

	for index, image in enumerate(__A			):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		_snake_case     		=	random_chars(32			)
		_snake_case     		=	paths[index].split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		_snake_case     		=	F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
		cva.imwrite(F'/{file_root}.jpg'    ,       __A    ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
		print(F'Success {index+1}/{len(__A			)} with {file_name}'			)
		_snake_case     		=	[]
		for anno in new_annos[index]:
			_snake_case     		=	F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
			annos_list.append(__A			)
		with open(F'/{file_root}.txt'    ,       'w'			) as outfile:
			outfile.write('\n'.join(line for line in annos_list			)			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			tuple[list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	for label_file in glob.glob(os.path.join(__A    ,       '*.txt'			)			):
		_snake_case     		=	label_file.split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		with open(__A			) as in_file:
			_snake_case     		=	in_file.readlines()
		_snake_case     		=	os.path.join(__A    ,       F'{label_name}.jpg'			)

		_snake_case     		=	[]
		for obj_list in obj_lists:
			_snake_case     		=	obj_list.rstrip('\n'			).split(' '			)
			boxes.append(
			    [
			        int(obj[0]			),
			        float(obj[1]			),
			        float(obj[2]			),
			        float(obj[3]			),
			        float(obj[4]			),
			    ]			)
		if not boxes:
			continue
		img_paths.append(__A			)
		labels.append(__A			)
	return img_paths, labels







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = 1			) ->			tuple[list, list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	_snake_case     		=	[]
	for idx in range(len(__A			)			):
		_snake_case     		=	[]
		_snake_case     		=	img_list[idx]
		path_list.append(__A			)
		_snake_case     		=	anno_list[idx]
		_snake_case     		=	cva.imread(__A			)
		if flip_type == 1:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
		elif flip_type == 0:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
		new_annos_lists.append(__A			)
		new_imgs_list.append(__A			)
	return new_imgs_list, new_annos_lists, path_list







def        SCREAMING_SNAKE_CASE__			(     __A = 32			) ->			str:
	assert number_char > 1, "The number of character should greater than 1"
	_snake_case     		=	ascii_lowercase + digits
	return "".join(random.choice(__A			) for _ in range(__A			)			)


if __name__ == "__main__":
					main()
					print("DONE ✅")

