




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
     import PIL

UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     if isinstance(lowerCAmelCase_       ,    (list, tuple)      ) and isinstance(videos[0]       ,    (list, tuple)      ) and is_valid_image(videos[0][0]      ):
          return videos

     elif isinstance(lowerCAmelCase_       ,    (list, tuple)      ) and is_valid_image(videos[0]      ):
          return [videos]

     elif is_valid_image(lowerCAmelCase_      ):
          return [[videos]]

     raise ValueError(F'Could not make batched video from {videos}'      )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				["pixel_values"]


    def __init__(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = PILImageResampling.BILINEAR       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Union[int, float] = 1 / 255       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     **SCREAMING_SNAKE_CASE__					:					Dict       ,     )				->	None:
         super().__init__(**SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			size if size is not None else {"shortest_edge": 256}
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       ,     default_to_square=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			crop_size if crop_size is not None else {"height": 224, "width": 224}
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       ,     param_name="crop_size"       )

         lowerCAmelCase__          =			do_resize
         lowerCAmelCase__          =			size
         lowerCAmelCase__          =			do_center_crop
         lowerCAmelCase__          =			crop_size
         lowerCAmelCase__          =			resample
         lowerCAmelCase__          =			do_rescale
         lowerCAmelCase__          =			rescale_factor
         lowerCAmelCase__          =			offset
         lowerCAmelCase__          =			do_normalize
         lowerCAmelCase__          =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__          =			image_std if image_std is not None else IMAGENET_STANDARD_STD


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int]       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = PILImageResampling.BILINEAR       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     **SCREAMING_SNAKE_CASE__					:					Tuple       ,     )				->	np.ndarray:
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       ,     default_to_square=SCREAMING_SNAKE_CASE__       )
         if "shortest_edge" in size:
              lowerCAmelCase__          =			get_resize_output_image_size(SCREAMING_SNAKE_CASE__       ,     size["shortest_edge"]       ,     default_to_square=SCREAMING_SNAKE_CASE__       )
         elif "height" in size and "width" in size:
              lowerCAmelCase__          =			(size["height"], size["width"])
         else:
              raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
         return resize(SCREAMING_SNAKE_CASE__       ,     size=SCREAMING_SNAKE_CASE__       ,     resample=SCREAMING_SNAKE_CASE__       ,     data_format=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     **SCREAMING_SNAKE_CASE__					:					str       ,     )				->	np.ndarray:
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       )
         if "height" not in size or "width" not in size:
              raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'       )
         return center_crop(SCREAMING_SNAKE_CASE__       ,     size=(size["height"], size["width"])       ,     data_format=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Union[int, float]       ,     SCREAMING_SNAKE_CASE__					:					bool = True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     **SCREAMING_SNAKE_CASE__					:					List[Any]       ,     )				->	Optional[int]:
         lowerCAmelCase__          =			image.astype(np.floataa       )
         if offset:
              lowerCAmelCase__          =			image - (scale / 2)
         return rescale(SCREAMING_SNAKE_CASE__       ,     scale=SCREAMING_SNAKE_CASE__       ,     data_format=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					Union[float, List[float]]       ,     SCREAMING_SNAKE_CASE__					:					Union[float, List[float]]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, ChannelDimension]] = None       ,     **SCREAMING_SNAKE_CASE__					:					Any       ,     )				->	np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__       ,     mean=SCREAMING_SNAKE_CASE__       ,     std=SCREAMING_SNAKE_CASE__       ,     data_format=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					ImageInput       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					float = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[ChannelDimension] = ChannelDimension.FIRST       ,     )				->	np.ndarray:
         if do_resize and size is None or resample is None:
              raise ValueError("Size and resample must be specified if do_resize is True."       )

         if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."       )

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."       )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."       )

         if offset and not do_rescale:
              raise ValueError("For offset, do_rescale must also be set to True."       )

         # All transformations expect numpy arrays.
         lowerCAmelCase__          =			to_numpy_array(SCREAMING_SNAKE_CASE__       )

         if do_resize:
              lowerCAmelCase__          =			self.resize(image=SCREAMING_SNAKE_CASE__       ,     size=SCREAMING_SNAKE_CASE__       ,     resample=SCREAMING_SNAKE_CASE__       )

         if do_center_crop:
              lowerCAmelCase__          =			self.center_crop(SCREAMING_SNAKE_CASE__       ,     size=SCREAMING_SNAKE_CASE__       )

         if do_rescale:
              lowerCAmelCase__          =			self.rescale(image=SCREAMING_SNAKE_CASE__       ,     scale=SCREAMING_SNAKE_CASE__       ,     offset=SCREAMING_SNAKE_CASE__       )

         if do_normalize:
              lowerCAmelCase__          =			self.normalize(image=SCREAMING_SNAKE_CASE__       ,     mean=SCREAMING_SNAKE_CASE__       ,     std=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         return image


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					ImageInput       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					PILImageResampling = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, int] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					float = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					bool = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[float, List[float]]] = None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Union[str, TensorType]] = None       ,     SCREAMING_SNAKE_CASE__					:					ChannelDimension = ChannelDimension.FIRST       ,     **SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     )				->	PIL.Image.Image:
         lowerCAmelCase__          =			do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__          =			resample if resample is not None else self.resample
         lowerCAmelCase__          =			do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__          =			do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__          =			rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__          =			offset if offset is not None else self.offset
         lowerCAmelCase__          =			do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__          =			image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__          =			image_std if image_std is not None else self.image_std

         lowerCAmelCase__          =			size if size is not None else self.size
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       ,     default_to_square=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__          =			get_size_dict(SCREAMING_SNAKE_CASE__       ,     param_name="crop_size"       )

         if not valid_images(SCREAMING_SNAKE_CASE__       ):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."       )

         lowerCAmelCase__          =			make_batched(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			[
             [
                 self._preprocess_image(
                     image=SCREAMING_SNAKE_CASE__       ,     do_resize=SCREAMING_SNAKE_CASE__       ,     size=SCREAMING_SNAKE_CASE__       ,     resample=SCREAMING_SNAKE_CASE__       ,     do_center_crop=SCREAMING_SNAKE_CASE__       ,     crop_size=SCREAMING_SNAKE_CASE__       ,     do_rescale=SCREAMING_SNAKE_CASE__       ,     rescale_factor=SCREAMING_SNAKE_CASE__       ,     offset=SCREAMING_SNAKE_CASE__       ,     do_normalize=SCREAMING_SNAKE_CASE__       ,     image_mean=SCREAMING_SNAKE_CASE__       ,     image_std=SCREAMING_SNAKE_CASE__       ,     data_format=SCREAMING_SNAKE_CASE__       ,     )
                 for img in video
             ]
             for video in videos
         ]

         lowerCAmelCase__          =			{"pixel_values": videos}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__       ,     tensor_type=SCREAMING_SNAKE_CASE__       )






import argparse
from collections import defaultdict



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'{file}_{class_name}_{test_name}'
     done_test[_id] += 1

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     lowerCAmelCase__          =			F'class {class_name}('
     lowerCAmelCase__          =			F'{4 * " "}def {test_name}('
     lowerCAmelCase__          =			F'{8 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			F'{16 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			0
     lowerCAmelCase__          =			0

     lowerCAmelCase__          =			[]
     for line in lines:
          if line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and in_func and (line.startswith(lowerCAmelCase_      ) or line.startswith(lowerCAmelCase_      )):
               lowerCAmelCase__          =			len(line.split(correct_line.split()[0]      )[0]      )
               count += 1

               if count == done_test[_id]:
                    lowerCAmelCase__          =			True

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

          if in_class and in_func and in_line and insert_line:
               new_lines.append(F'{spaces * " "}{correct_line}'      )
               lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			False
          else:
               new_lines.append(lowerCAmelCase_      )

     with open(lowerCAmelCase_       ,    "w"      ) as f:
          for line in new_lines:
               f.write(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     if fail is not None:
          with open(lowerCAmelCase_       ,    "r"      ) as f:
               lowerCAmelCase__          =			{l.strip() for l in f.readlines()}
     else:
          lowerCAmelCase__          =			None

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

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


if __name__ == "__main__":
     UpperCamelCase								=					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)
     UpperCamelCase								=					parser.parse_args()

     main(args.correct_filename, args.fail_filename)
