





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

import cva
import numpy as np

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




def 		lowerCAmelCase_    (  )      ->	Optional[Any]:
    UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      get_dataset(UpperCAmelCase_		,   UpperCAmelCase_      )
    for index in range(UpperCAmelCase_      ):
        UpperCamelCase__							:				List[Any]          =      random.sample(range(len(UpperCAmelCase_      )      )		,   4      )
        UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Any          =      update_image_and_anno(
            UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_		,   filter_scale=UpperCAmelCase_		,   )

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			str      )      ->	List[str]:
    UpperCamelCase__							:				Optional[Any]          =      []
    UpperCamelCase__							:				List[str]          =      []
    for label_file in glob.glob(os.path.join(UpperCAmelCase_		,   '''*.txt'''      )      ):
        UpperCamelCase__							:				List[str]          =      label_file.split(os.sep      )[-1].rsplit('''.'''		,   1      )[0]
        with open(UpperCAmelCase_      ) as in_file:
            UpperCamelCase__							:				Optional[int]          =      in_file.readlines()
        UpperCamelCase__							:				Dict          =      os.path.join(UpperCAmelCase_		,   f"{label_name}.jpg"      )

        UpperCamelCase__							:				Optional[int]          =      []
        for obj_list in obj_lists:
            UpperCamelCase__							:				Dict          =      obj_list.rstrip('''\n'''      ).split(''' '''      )
            UpperCamelCase__							:				Union[str, Any]          =      float(obj[1]      ) - float(obj[3]      ) / 2
            UpperCamelCase__							:				Dict          =      float(obj[2]      ) - float(obj[4]      ) / 2
            UpperCamelCase__							:				List[Any]          =      float(obj[1]      ) + float(obj[3]      ) / 2
            UpperCamelCase__							:				Union[str, Any]          =      float(obj[2]      ) + float(obj[4]      ) / 2

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			list		,   __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			tuple[int, int]		,   __UpperCAmelCase:			tuple[float, float]		,   __UpperCAmelCase:			float = 0.0		,   )      ->	Tuple:
    UpperCamelCase__							:				Tuple          =      np.zeros([output_size[0], output_size[1], 3]		,   dtype=np.uinta      )
    UpperCamelCase__							:				List[Any]          =      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__							:				List[Any]          =      scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__							:				Optional[Any]          =      int(scale_x * output_size[1]      )
    UpperCamelCase__							:				List[str]          =      int(scale_y * output_size[0]      )

    UpperCamelCase__							:				Any          =      []
    UpperCamelCase__							:				Optional[int]          =      []
    for i, index in enumerate(UpperCAmelCase_      ):
        UpperCamelCase__							:				str          =      all_img_list[index]
        path_list.append(UpperCAmelCase_      )
        UpperCamelCase__							:				Any          =      all_annos[index]
        UpperCamelCase__							:				Optional[Any]          =      cva.imread(UpperCAmelCase_      )
        if i == 0:  # top-left
            UpperCamelCase__							:				str          =      cva.resize(UpperCAmelCase_		,   (divid_point_x, divid_point_y)      )
            UpperCamelCase__							:				Dict          =      img
            for bbox in img_annos:
                UpperCamelCase__							:				int          =      bbox[1] * scale_x
                UpperCamelCase__							:				List[Any]          =      bbox[2] * scale_y
                UpperCamelCase__							:				Optional[Any]          =      bbox[3] * scale_x
                UpperCamelCase__							:				Union[str, Any]          =      bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
        elif i == 1:  # top-right
            UpperCamelCase__							:				Dict          =      cva.resize(UpperCAmelCase_		,   (output_size[1] - divid_point_x, divid_point_y)      )
            UpperCamelCase__							:				Any          =      img
            for bbox in img_annos:
                UpperCamelCase__							:				Dict          =      scale_x + bbox[1] * (1 - scale_x)
                UpperCamelCase__							:				Union[str, Any]          =      bbox[2] * scale_y
                UpperCamelCase__							:				Union[str, Any]          =      scale_x + bbox[3] * (1 - scale_x)
                UpperCamelCase__							:				Union[str, Any]          =      bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
        elif i == 2:  # bottom-left
            UpperCamelCase__							:				List[Any]          =      cva.resize(UpperCAmelCase_		,   (divid_point_x, output_size[0] - divid_point_y)      )
            UpperCamelCase__							:				Any          =      img
            for bbox in img_annos:
                UpperCamelCase__							:				str          =      bbox[1] * scale_x
                UpperCamelCase__							:				Optional[int]          =      scale_y + bbox[2] * (1 - scale_y)
                UpperCamelCase__							:				int          =      bbox[3] * scale_x
                UpperCamelCase__							:				Union[str, Any]          =      scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax]      )
        else:  # bottom-right
            UpperCamelCase__							:				List[str]          =      cva.resize(
                UpperCAmelCase_		,   (output_size[1] - divid_point_x, output_size[0] - divid_point_y)      )
            UpperCamelCase__							:				List[str]          =      img
            for bbox in img_annos:
                UpperCamelCase__							:				List[Any]          =      scale_x + bbox[1] * (1 - scale_x)
                UpperCamelCase__							:				Tuple          =      scale_y + bbox[2] * (1 - scale_y)
                UpperCamelCase__							:				Optional[Any]          =      scale_x + bbox[3] * (1 - scale_x)
                UpperCamelCase__							:				List[str]          =      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:
        UpperCamelCase__							:				Dict          =      [
            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 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	Union[str, Any]:
    assert number_char > 1, "The number of character should greater than 1"
    UpperCamelCase__							:				Optional[Any]          =      ascii_lowercase + digits
    return "".join(random.choice(UpperCAmelCase_      ) for _ in range(UpperCAmelCase_      )      )


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





'''simple docstring'''

import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_  :				str						= """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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





  """simple docstring"""




  def   snake_case		(    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/google-research/google-research/tree/master/rouge"]			,  reference_urls=[
           "https://en.wikipedia.org/wiki/ROUGE_(metric)",
           "https://github.com/google-research/google-research/tree/master/rouge",
       ]			,  )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

   lowerCamelCase_      =  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase			,  use_stemmer=UpperCamelCase     )
   if use_aggregator:
    lowerCamelCase_      =  scoring.BootstrapAggregator()
   else:
    lowerCamelCase_      =  []

   for ref, pred in zip(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  scorer.score(UpperCamelCase			,  UpperCamelCase     )
    if use_aggregator:
     aggregator.add_scores(UpperCamelCase     )
    else:
     scores.append(UpperCamelCase     )

   if use_aggregator:
    lowerCamelCase_      =  aggregator.aggregate()
   else:
    lowerCamelCase_      =  {}
    for key in scores[0]:
     lowerCamelCase_      =  [score[key] for score in scores]

   return result



