
import argparse
from collections import defaultdict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Union[str, Any]											=					f'''{file}_{class_name}_{test_name}'''
      done_test[_id] += 1

      with open(UpperCamelCase__			,		'''r'''		) as f:
            UpperCamelCase__     : Tuple											=					f.readlines()

      UpperCamelCase__     : int											=					f'''class {class_name}('''
      UpperCamelCase__     : str											=					f'''{4 * " "}def {test_name}('''
      UpperCamelCase__     : List[Any]											=					f'''{8 * " "}{correct_line.split()[0]}'''
      UpperCamelCase__     : int											=					f'''{1_6 * " "}{correct_line.split()[0]}'''
      UpperCamelCase__     : Union[str, Any]											=					False
      UpperCamelCase__     : Optional[int]											=					False
      UpperCamelCase__     : Union[str, Any]											=					False
      UpperCamelCase__     : str											=					False
      UpperCamelCase__     : List[str]											=					0
      UpperCamelCase__     : List[str]											=					0

      UpperCamelCase__     : Tuple											=					[]
      for line in lines:
            if line.startswith(UpperCamelCase__		):
                  UpperCamelCase__     : Tuple											=					True
            elif in_class and line.startswith(UpperCamelCase__		):
                  UpperCamelCase__     : Dict											=					True
            elif in_class and in_func and (line.startswith(UpperCamelCase__		) or line.startswith(UpperCamelCase__		)):
                  UpperCamelCase__     : List[str]											=					len(line.split(correct_line.split()[0]		)[0]		)
                  count += 1

                  if count == done_test[_id]:
                        UpperCamelCase__     : str											=					True

            if in_class and in_func and in_line:
                  if ")" not in line:
                        continue
                  else:
                        UpperCamelCase__     : str											=					True

            if in_class and in_func and in_line and insert_line:
                  new_lines.append(f'''{spaces * " "}{correct_line}'''		)
                  UpperCamelCase__     : Optional[int]											=					False
            else:
                  new_lines.append(UpperCamelCase__		)

      with open(UpperCamelCase__			,		'''w'''		) as f:
            for line in new_lines:
                  f.write(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__=None		):
      if fail is not None:
            with open(UpperCamelCase__			,		'''r'''		) as f:
                  UpperCamelCase__     : int											=					{l.strip() for l in f.readlines()}
      else:
            UpperCamelCase__     : Tuple											=					None

      with open(UpperCamelCase__			,		'''r'''		) as f:
            UpperCamelCase__     : Optional[int]											=					f.readlines()

      UpperCamelCase__     : str											=					defaultdict(UpperCamelCase__		)
      for line in correct_lines:
            UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__					,UpperCamelCase__     : Dict											=					line.split(''';'''		)
            if test_failures is None or "::".join([file, class_name, test_name]		) in test_failures:
                  overwrite_file(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    lowerCamelCase  =argparse.ArgumentParser()
    parser.add_argument("--correct_filename", help="filename of tests with expected result")
    parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
    lowerCamelCase  =parser.parse_args()

    main(args.correct_filename, args.fail_filename)




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


