





'''simple docstring'''



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

import cva
__magic_name__   :						List[str]    =			""""""
__magic_name__   :						Any    =			""""""
__magic_name__   :						List[str]    =			""""""
__magic_name__   :						Union[str, Any]    =			1  # (0 is vertical, 1 is horizontal)




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				get_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  print("Processing..."       )
  _snake_case		,		_snake_case		,		_snake_case     		=				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'
    _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'''					,	SCREAMING_SNAKE_CASE__					,	[cva.IMWRITE_JPEG_QUALITY, 85]       )
    print(f'''Success {index+1}/{len(SCREAMING_SNAKE_CASE__       )} 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(SCREAMING_SNAKE_CASE__       )
    with open(f'''/{file_root}.txt'''					,	"w"       ) as outfile:
      outfile.write("\n".join(line for line in annos_list       )       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  _snake_case     		=				[]
  for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE__					,	"*.txt"       )       ):
    _snake_case     		=				label_file.split(os.sep       )[-1].rsplit("."					,	1       )[0]
    with open(SCREAMING_SNAKE_CASE__       ) as in_file:
      _snake_case     		=				in_file.readlines()
    _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	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(SCREAMING_SNAKE_CASE__       )
    labels.append(SCREAMING_SNAKE_CASE__       )
  return img_paths, labels




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 1       ):







  '''simple docstring'''



  _snake_case     		=				[]
  _snake_case     		=				[]
  _snake_case     		=				[]
  for idx in range(len(SCREAMING_SNAKE_CASE__       )       ):
    _snake_case     		=				[]
    _snake_case     		=				img_list[idx]
    path_list.append(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				anno_list[idx]
    _snake_case     		=				cva.imread(SCREAMING_SNAKE_CASE__       )
    if flip_type == 1:
      _snake_case     		=				cva.flip(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
      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(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
      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(SCREAMING_SNAKE_CASE__       )
    new_imgs_list.append(SCREAMING_SNAKE_CASE__       )
  return new_imgs_list, new_annos_lists, path_list




def 				snake_case_      (		SCREAMING_SNAKE_CASE__ = 32       ):







  '''simple docstring'''



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


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









'''simple docstring'''



import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
 __magic_name__   :						Union[str, Any]    =			pd.read_csv("""sample_data.csv""", header=None)
 __magic_name__   :						Optional[Any]    =			df.shape[:1][0]
 # If you're using some other dataset input the target column
 __magic_name__   :						str    =			df.iloc[:, 1:2]
 __magic_name__   :						Dict    =			actual_data.values.reshape(len_data, 1)
 __magic_name__   :						Tuple    =			MinMaxScaler().fit_transform(actual_data)
 __magic_name__   :						Union[str, Any]    =			10
 __magic_name__   :						Optional[int]    =			5
 __magic_name__   :						Any    =			20
 __magic_name__   :						int    =			len_data - periods * look_back
 __magic_name__   :						Union[str, Any]    =			actual_data[:division]
 __magic_name__   :						int    =			actual_data[division - look_back :]
 __magic_name__   ,    __magic_name__   :						List[str]    =			[], []
 __magic_name__   ,    __magic_name__   :						Tuple    =			[], []

 for i in range(0, len(train_data) - forward_days - look_back + 1):
  train_x.append(train_data[i : i + look_back])
  train_y.append(train_data[i + look_back : i + look_back + forward_days])
 for i in range(0, len(test_data) - forward_days - look_back + 1):
  test_x.append(test_data[i : i + look_back])
  test_y.append(test_data[i + look_back : i + look_back + forward_days])
 __magic_name__   :						List[str]    =			np.array(train_x)
 __magic_name__   :						List[str]    =			np.array(test_x)
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in train_y])
 __magic_name__   :						int    =			np.array([list(i.ravel()) for i in test_y])

 __magic_name__   :						Tuple    =			Sequential()
 model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
 model.add(LSTM(64, input_shape=(128, 1)))
 model.add(Dense(forward_days))
 model.compile(loss="""mean_squared_error""", optimizer="""adam""")
 __magic_name__   :						int    =			model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __magic_name__   :						int    =			model.predict(x_test)


