

'''simple docstring'''




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

import cva
_a					:			Any							=    ''''''
_a					:			Dict							=    ''''''
_a					:			Optional[int]							=    ''''''
_a					:			Optional[int]							=    1  # (0 is vertical, 1 is horizontal)


def    lowerCamelCase__		(    ):
  UpperCAmelCase				,   UpperCAmelCase    				=						get_dataset(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_    )
  print('Processing...'    )
  UpperCAmelCase				,   UpperCAmelCase				,   UpperCAmelCase    				=						update_image_and_anno(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_    )

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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : str    ):
  UpperCAmelCase    				=						[]
  UpperCAmelCase    				=						[]
  for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE_							, '*.txt'    )    ):
    UpperCAmelCase    				=						label_file.split(os.sep    )[-1].rsplit('.'							, 1    )[0]
    with open(SCREAMING_SNAKE_CASE_    ) as in_file:
      UpperCAmelCase    				=						in_file.readlines()
    UpperCAmelCase    				=						os.path.join(SCREAMING_SNAKE_CASE_							, f'''{label_name}.jpg'''    )

    UpperCAmelCase    				=						[]
    for obj_list in obj_lists:
      UpperCAmelCase    				=						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(SCREAMING_SNAKE_CASE_    )
    labels.append(SCREAMING_SNAKE_CASE_    )
  return img_paths, labels


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : list							, SCREAMING_SNAKE_CASE   : list							, SCREAMING_SNAKE_CASE   : int = 1    ):
  UpperCAmelCase    				=						[]
  UpperCAmelCase    				=						[]
  UpperCAmelCase    				=						[]
  for idx in range(len(SCREAMING_SNAKE_CASE_    )    ):
    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						img_list[idx]
    path_list.append(SCREAMING_SNAKE_CASE_    )
    UpperCAmelCase    				=						anno_list[idx]
    UpperCAmelCase    				=						cva.imread(SCREAMING_SNAKE_CASE_    )
    if flip_type == 1:
      UpperCAmelCase    				=						cva.flip(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_    )
      for bbox in img_annos:
        UpperCAmelCase    				=						1 - bbox[1]
        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]    )
    elif flip_type == 0:
      UpperCAmelCase    				=						cva.flip(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_    )
      for bbox in img_annos:
        UpperCAmelCase    				=						1 - bbox[2]
        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]    )
    new_annos_lists.append(SCREAMING_SNAKE_CASE_    )
    new_imgs_list.append(SCREAMING_SNAKE_CASE_    )
  return new_imgs_list, new_annos_lists, path_list


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int = 32    ):
  assert number_char > 1, "The number of character should greater than 1"
  UpperCAmelCase    				=						ascii_lowercase + digits
  return "".join(random.choice(SCREAMING_SNAKE_CASE_    ) for _ in range(SCREAMING_SNAKE_CASE_    )    )


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




from sklearn.metrics import mean_squared_error

import datasets


A		: List[Any]								=     '''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

A		: str								=     '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


A		: Any								=     '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''



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






			def    UpperCamelCase_				(      self  ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(self._get_feature_types()  )				,			reference_urls=[
									        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
									    ]				,			)






			def    UpperCamelCase_				(      self  ):
									if self.config_name == "multilist":
															return {
															    "predictions": datasets.Sequence(datasets.Value("""float"""  )  ),
															    "references": datasets.Sequence(datasets.Value("""float"""  )  ),
															}
									else:
															return {
															    "predictions": datasets.Value("""float"""  ),
															    "references": datasets.Value("""float"""  ),
															}






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase=None				,			__UpperCamelCase="uniform_average"				,			__UpperCamelCase=True  ):
									_lowercase					       =					mean_squared_error(
									    __UpperCamelCase				,			__UpperCamelCase				,			sample_weight=__UpperCamelCase				,			multioutput=__UpperCamelCase				,			squared=__UpperCamelCase  )

									return {"mse": mse}