"""simple docstring"""





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

import cva
import numpy as np

# Parrameters
__A  =				(720, 1280)  # Height, Width
__A  =				(0.4, 0.6)  # if height or width lower than this scale, drop it.
__A  =				1 / 100
__A  =				''''''
__A  =				''''''
__A  =				''''''
__A  =				250


def    lowercase_   (   ) ->      None:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     get_dataset(_lowerCamelCase						,   _lowerCamelCase      )
							for index in range(_lowerCamelCase      ):
														__lowerCamelCase					:		Optional[Any]		   =     random.sample(range(len(_lowerCamelCase      )      )						,   4      )
														__lowerCamelCase					:		Optional[Any]		   =     update_image_and_anno(
														    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   filter_scale=_lowerCamelCase						,   )

														# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
														__lowerCamelCase					:		Tuple		   =     random_chars(32      )
														__lowerCamelCase					:		Dict		   =     path.split(os.sep      )[-1].rsplit("."						,   1      )[0]
														__lowerCamelCase					:		List[str]		   =     F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
														cva.imwrite(F"""{file_root}.jpg"""						,   _lowerCamelCase						,   [cva.IMWRITE_JPEG_QUALITY, 85]      )
														print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""      )
														__lowerCamelCase					:		List[Any]		   =     []
														for anno in new_annos:
																					__lowerCamelCase					:		Any		   =     anno[3] - anno[1]
																					__lowerCamelCase					:		Optional[int]		   =     anno[4] - anno[2]
																					__lowerCamelCase					:		Optional[int]		   =     anno[1] + width / 2
																					__lowerCamelCase					:		Union[str, Any]		   =     anno[2] + height / 2
																					__lowerCamelCase					:		int		   =     F"""{anno[0]} {x_center} {y_center} {width} {height}"""
																					annos_list.append(_lowerCamelCase      )
														with open(F"""{file_root}.txt"""						,   "w"      ) as outfile:
																					outfile.write("\n".join(line for line in annos_list      )      )


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	str      ) ->      tuple[list, list]:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[int]		   =     []
							__lowerCamelCase					:		Any		   =     []
							for label_file in glob.glob(os.path.join(_lowerCamelCase						,   "*.txt"      )      ):
														__lowerCamelCase					:		List[Any]		   =     label_file.split(os.sep      )[-1].rsplit("."						,   1      )[0]
														with open(_lowerCamelCase      ) as in_file:
																					__lowerCamelCase					:		Tuple		   =     in_file.readlines()
														__lowerCamelCase					:		List[str]		   =     os.path.join(_lowerCamelCase						,   F"""{label_name}.jpg"""      )

														__lowerCamelCase					:		Union[str, Any]		   =     []
														for obj_list in obj_lists:
																					__lowerCamelCase					:		str		   =     obj_list.rstrip("\n"      ).split(" "      )
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) - float(obj[3]      ) / 2
																					__lowerCamelCase					:		Tuple		   =     float(obj[2]      ) - float(obj[4]      ) / 2
																					__lowerCamelCase					:		Union[str, Any]		   =     float(obj[1]      ) + float(obj[3]      ) / 2
																					__lowerCamelCase					:		Any		   =     float(obj[2]      ) + float(obj[4]      ) / 2

																					boxes.append([int(obj[0]      ), xmin, ymin, xmax, ymax]      )
														if not boxes:
																					continue
														img_paths.append(_lowerCamelCase      )
														labels.append(_lowerCamelCase      )
							return img_paths, labels


def    lowercase_   (   _lowerCamelCase:	list						,   _lowerCamelCase:	list						,   _lowerCamelCase:	list[int]						,   _lowerCamelCase:	tuple[int, int]						,   _lowerCamelCase:	tuple[float, float]						,   _lowerCamelCase:	float = 0.0						,   ) ->      tuple[list, list, str]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     np.zeros([output_size[0], output_size[1], 3]						,   dtype=np.uinta      )
							__lowerCamelCase					:		Optional[Any]		   =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
							__lowerCamelCase					:		List[str]		   =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
							__lowerCamelCase					:		int		   =     int(scale_x * output_size[1]      )
							__lowerCamelCase					:		Optional[Any]		   =     int(scale_y * output_size[0]      )

							__lowerCamelCase					:		List[Any]		   =     []
							__lowerCamelCase					:		Optional[Any]		   =     []
							for i, index in enumerate(_lowerCamelCase      ):
														__lowerCamelCase					:		List[str]		   =     all_img_list[index]
														path_list.append(_lowerCamelCase      )
														__lowerCamelCase					:		Optional[Any]		   =     all_annos[index]
														__lowerCamelCase					:		List[str]		   =     cva.imread(_lowerCamelCase      )
														if i == 0:  # top-left
																					__lowerCamelCase					:		List[str]		   =     cva.resize(_lowerCamelCase						,   (divid_point_x, divid_point_y)      )
																					__lowerCamelCase					:		Any		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		str		   =     bbox[1] * scale_x
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[2] * scale_y
																												__lowerCamelCase					:		Optional[int]		   =     bbox[3] * scale_x
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[4] * scale_y
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														elif i == 1:  # top-right
																					__lowerCamelCase					:		str		   =     cva.resize(_lowerCamelCase						,   (output_size[1] - divid_point_x, divid_point_y)      )
																					__lowerCamelCase					:		Any		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		List[Any]		   =     scale_x + bbox[1] * (1 - scale_x)
																												__lowerCamelCase					:		List[Any]		   =     bbox[2] * scale_y
																												__lowerCamelCase					:		Tuple		   =     scale_x + bbox[3] * (1 - scale_x)
																												__lowerCamelCase					:		Union[str, Any]		   =     bbox[4] * scale_y
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														elif i == 2:  # bottom-left
																					__lowerCamelCase					:		Any		   =     cva.resize(_lowerCamelCase						,   (divid_point_x, output_size[0] - divid_point_y)      )
																					__lowerCamelCase					:		List[str]		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		Any		   =     bbox[1] * scale_x
																												__lowerCamelCase					:		Optional[int]		   =     scale_y + bbox[2] * (1 - scale_y)
																												__lowerCamelCase					:		Dict		   =     bbox[3] * scale_x
																												__lowerCamelCase					:		Tuple		   =     scale_y + bbox[4] * (1 - scale_y)
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
														else:  # bottom-right
																					__lowerCamelCase					:		int		   =     cva.resize(
																					    _lowerCamelCase						,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)      )
																					__lowerCamelCase					:		Optional[Any]		   =     img
																					for bbox in img_annos:
																												__lowerCamelCase					:		Union[str, Any]		   =     scale_x + bbox[1] * (1 - scale_x)
																												__lowerCamelCase					:		Union[str, Any]		   =     scale_y + bbox[2] * (1 - scale_y)
																												__lowerCamelCase					:		int		   =     scale_x + bbox[3] * (1 - scale_x)
																												__lowerCamelCase					:		int		   =     scale_y + bbox[4] * (1 - scale_y)
																												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )

    # Remove bounding box small than scale of filter
							if filter_scale > 0:
														__lowerCamelCase					:		str		   =     [
														    anno
														    for anno in new_anno
														    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
														]

							return output_img, new_anno, path_list[0]


def    lowercase_   (   _lowerCamelCase:	int      ) ->      str:



							'''simple docstring'''

							assert number_char > 1, "The number of character should greater than 1"
							__lowerCamelCase					:		Tuple		   =     ascii_lowercase + digits
							return "".join(random.choice(_lowerCamelCase      ) for _ in range(_lowerCamelCase      )      )


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





from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


__A  =				''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
				class  _snake_case     (					tr.AbstractTransform		):






										def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			str = " "    ):
																	__lowerCamelCase					:		List[str]		   =     sentence_delimiter






										def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			str    ):
																	return list(UpperCAmelCase    )







										def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[str]    ):
																	__lowerCamelCase					:		Any		   =     []
																	for sent_idx, sentence in enumerate(UpperCAmelCase    ):
																								chars.extend(self.process_string(UpperCAmelCase    )    )
																								if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase    ) - 1:
																															chars.append(self.sentence_delimiter    )
																	return chars

				__A  =				tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
else:
				__A  =				tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)


__A  =				'''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

__A  =				'''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

__A  =				'''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				, _KWARGS_DESCRIPTION		)
class  _snake_case     (					datasets.Metric		):






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													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/jitsi/jiwer/"]							,					reference_urls=[
													        "https://en.wikipedia.org/wiki/Word_error_rate",
													        "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
													    ]							,					)







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[str]=False    ):
													if concatenate_texts:
																				return jiwer.compute_measures(
																				    UpperCAmelCase							,					UpperCAmelCase							,					truth_transform=UpperCAmelCase							,					hypothesis_transform=UpperCAmelCase							,					)["wer"]

													__lowerCamelCase					:		Optional[int]		   =     0
													__lowerCamelCase					:		Optional[int]		   =     0
													for prediction, reference in zip(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		Dict		   =     jiwer.compute_measures(
																				    UpperCAmelCase							,					UpperCAmelCase							,					truth_transform=UpperCAmelCase							,					hypothesis_transform=UpperCAmelCase							,					)
																				incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																				total += measures["substitutions"] + measures["deletions"] + measures["hits"]

													return incorrect / total