






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

import cva
import numpy as np

# Parrameters
__lowerCamelCase	:		Any										=   (7_20, 12_80)  # Height, Width
__lowerCamelCase	:		List[str]										=   (0.4, 0.6)  # if height or width lower than this scale, drop it.
__lowerCamelCase	:		str										=   1 / 1_00
__lowerCamelCase	:		Optional[Any]										=   """"""
__lowerCamelCase	:		Any										=   """"""
__lowerCamelCase	:		Tuple										=   """"""
__lowerCamelCase	:		Any										=   2_50







def 			A__							(      ):





 '''simple docstring'''







 snake_case__       ,   snake_case__  :    Union[str, Any]							       =get_dataset(_a		,  _a	)
 for index in range(_a	):
  snake_case__  :    Optional[int]							       =random.sample(range(len(_a	)	)		,  4	)
  snake_case__       ,   snake_case__       ,   snake_case__  :    int							       =update_image_and_anno(
      _a		,  _a		,  _a		,  _a		,  _a		,  filter_scale=_a		,  )

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







def 			A__							(      _a  :					str		,  _a  :					str	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =[]
 snake_case__  :    Optional[int]							       =[]
 for label_file in glob.glob(os.path.join(_a		,  """*.txt"""	)	):
  snake_case__  :    Any							       =label_file.split(os.sep	)[-1].rsplit("""."""		,  1	)[0]
  with open(_a	) as in_file:
   snake_case__  :    Optional[int]							       =in_file.readlines()
  snake_case__  :    Dict							       =os.path.join(_a		,  f"{label_name}.jpg"	)

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

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







def 			A__							(      _a  :					list		,  _a  :					list		,  _a  :					list[int]		,  _a  :					tuple[int, int]		,  _a  :					tuple[float, float]		,  _a  :					float = 0.0		,  ):





 '''simple docstring'''







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

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





 '''simple docstring'''







 assert number_char > 1, "The number of character should greater than 1"
 snake_case__  :    List[str]							       =ascii_lowercase + digits
 return "".join(random.choice(_a	) for _ in range(_a	)	)


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













from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__lowerCamelCase	:		Any										=   """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

__lowerCamelCase	:		str										=   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__lowerCamelCase	:		str										=   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 			A__							(      _a  :					int		,  _a  :					str	):





 '''simple docstring'''







 return float((preds == labels).mean()	)







def 			A__							(      _a  :					Optional[Any]		,  _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    str							       =simple_accuracy(_a		,  _a	)
 snake_case__  :    str							       =float(fa_score(y_true=_a		,  y_pred=_a	)	)
 return {
     "accuracy": acc,
     "f1": fa,
 }







def 			A__							(      _a  :					int		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    str							       =float(pearsonr(_a		,  _a	)[0]	)
 snake_case__  :    Optional[int]							       =float(spearmanr(_a		,  _a	)[0]	)
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )
       return datasets.MetricInfo(
           description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
               {
                   """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
                   """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
               }   )				,			codebase_urls=[]				,			reference_urls=[]				,			format="""numpy"""				,			)



      def 						lowercase__					(     self				,			a				,			a   ):
       if self.config_name == "cola":
        return {"matthews_correlation": matthews_corrcoef(a				,			a   )}
       elif self.config_name == "stsb":
        return pearson_and_spearman(a				,			a   )
       elif self.config_name in ["mrpc", "qqp"]:
        return acc_and_fa(a				,			a   )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
        return {"accuracy": simple_accuracy(a				,			a   )}
       else:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )





