

from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    logging.get_logger(__name__)






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      ["pixel_values"]






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

       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__					=					do_normalize
       SCREAMING_SNAKE_CASE__					=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       SCREAMING_SNAKE_CASE__					=					image_std if image_std is not None else IMAGENET_DEFAULT_STD






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BICUBIC			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	List[str]			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
            SCREAMING_SNAKE_CASE__					=					int((256 / 224) * size['''shortest_edge''']      )
            SCREAMING_SNAKE_CASE__					=					get_resize_output_image_size(__lowerCamelCase			,     size=__lowerCamelCase			,     default_to_square=__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					{'''height''': output_size[0], '''width''': output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
            raise ValueError(
                f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''      )
       return resize(
           __lowerCamelCase			,     size=(size_dict['''height'''], size_dict['''width'''])			,     resample=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






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






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[int, float]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	str			,     )	->							np.ndarray:
       return rescale(__lowerCamelCase			,     scale=__lowerCamelCase			,     data_format=__lowerCamelCase			,     **__lowerCamelCase      )






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







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

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

       SCREAMING_SNAKE_CASE__					=					make_list_of_images(__lowerCamelCase      )

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

       if do_resize and size is None:
            raise ValueError('''Size 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.'''      )

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE__					=					[to_numpy_array(__lowerCamelCase      ) for image in images]

       if do_resize:
            SCREAMING_SNAKE_CASE__					=					[self.resize(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       if do_center_crop:
            SCREAMING_SNAKE_CASE__					=					[self.center_crop(__lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__					=					[self.rescale(__lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       if do_normalize:
            SCREAMING_SNAKE_CASE__					=					[self.normalize(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       SCREAMING_SNAKE_CASE__					=					[to_channel_dimension_format(__lowerCamelCase			,     __lowerCamelCase      ) for image in images]

       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': images}
       return BatchFeature(data=__lowerCamelCase			,     tensor_type=__lowerCamelCase      )





import doctest
from collections import deque

import numpy as np






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]      )	->							None:
       SCREAMING_SNAKE_CASE__					=					[2, 1, 2, -1]
       SCREAMING_SNAKE_CASE__					=					[1, 2, 3, 4]







  def    lowercase_	(							self		:	List[str]      )	->							list[float]:
       SCREAMING_SNAKE_CASE__					=					len(self.first_signal      )
       SCREAMING_SNAKE_CASE__					=					len(self.second_signal      )

       SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     __lowerCamelCase      )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__					=					[[0] * max_length for i in range(__lowerCamelCase      )]

       # fills the smaller signal with zeros to make both signals of same length
       if length_first_signal < length_second_signal:
            self.first_signal += [0] * (max_length - length_first_signal)
       elif length_first_signal > length_second_signal:
            self.second_signal += [0] * (max_length - length_second_signal)
       for i in range(__lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					deque(self.second_signal      )
            rotated_signal.rotate(__lowerCamelCase      )
            for j, item in enumerate(__lowerCamelCase      ):
                 matrix[i][j] += item

        # multiply the matrix with the first signal
       SCREAMING_SNAKE_CASE__					=					np.matmul(np.transpose(__lowerCamelCase      )			,     np.transpose(self.first_signal      )      )

       # rounding-off to two decimal places
       return [round(__lowerCamelCase			,     2      ) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()


