

"""simple docstring"""




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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
       import PIL

if is_torch_available():
       import torch


_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      logging.get_logger(__name__)






class 	UpperCAmelCase__		(	lowerCAmelCase__	):






  """simple docstring"""


  a									= ["pixel_values"]






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

       SCREAMING_SNAKE_CASE__   =   do_resize
       SCREAMING_SNAKE_CASE__   =   size
       SCREAMING_SNAKE_CASE__   =   resample
       SCREAMING_SNAKE_CASE__   =   do_rescale
       SCREAMING_SNAKE_CASE__   =   rescale_factor
       SCREAMING_SNAKE_CASE__   =   do_center_crop
       SCREAMING_SNAKE_CASE__   =   crop_size
       SCREAMING_SNAKE_CASE__   =   do_flip_channel_order






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Union[str, Any] = PIL.Image.BILINEAR     ,       __lowerCamelCase						:		Dict = None     ,       **__lowerCamelCase						:		int     ,       )		->							str:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(a__     ,       default_to_square=a__ )
       if "shortest_edge" not in size:
            raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''' )
       SCREAMING_SNAKE_CASE__   =   get_resize_output_image_size(a__     ,       size=size['''shortest_edge''']     ,       default_to_square=a__ )
       return resize(a__     ,       size=a__     ,       resample=a__     ,       data_format=a__     ,       **a__ )






  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Optional[Any] = None     ,       **__lowerCamelCase						:		Optional[int]     ,       )		->							int:
       SCREAMING_SNAKE_CASE__   =   get_size_dict(a__ )
       if "height" not in size or "width" not in size:
            raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
       return center_crop(a__     ,       size=(size['''height'''], size['''width'''])     ,       data_format=a__     ,       **a__ )






  def 				lowercase_		(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		List[Any] = None     ,       **__lowerCamelCase						:		List[Any]     ,       )		->							Union[str, Any]:
       return rescale(a__     ,       scale=a__     ,       data_format=a__     ,       **a__ )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		Tuple     ,       __lowerCamelCase						:		int = None )		->							Optional[Any]:
       return flip_channel_order(a__     ,       data_format=a__ )






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Union[str, Any] = None     ,       __lowerCamelCase						:		List[Any] = None     ,       __lowerCamelCase						:		Union[str, Any] = None     ,       __lowerCamelCase						:		Union[str, Any] = None     ,       __lowerCamelCase						:		Union[str, Any] = None     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		int = None     ,       __lowerCamelCase						:		Optional[int] = None     ,       __lowerCamelCase						:		List[Any] = None     ,       __lowerCamelCase						:		List[str] = ChannelDimension.FIRST     ,       **__lowerCamelCase						:		int     ,       )		->							Union[str, Any]:
       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_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_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__   =   (
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

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

       SCREAMING_SNAKE_CASE__   =   make_list_of_images(a__ )

       if not valid_images(a__ ):
            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_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

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

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

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

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

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

       # the pretrained checkpoints assume images are BGR, not RGB
       if do_flip_channel_order:
            SCREAMING_SNAKE_CASE__   =   [self.flip_channel_order(image=a__ ) for image in images]

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

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







  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Union[str, Any] = None )		->							Any:
       SCREAMING_SNAKE_CASE__   =   outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
            if len(a__ ) != len(a__ ):
                 raise ValueError(
                     '''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )

            if is_torch_tensor(a__ ):
                 SCREAMING_SNAKE_CASE__   =   target_sizes.numpy()

            SCREAMING_SNAKE_CASE__   =   []

            for idx in range(len(a__ ) ):
                 SCREAMING_SNAKE_CASE__   =   torch.nn.functional.interpolate(
                     logits[idx].unsqueeze(dim=0 )     ,       size=target_sizes[idx]     ,       mode='''bilinear'''     ,       align_corners=a__ )
                 SCREAMING_SNAKE_CASE__   =   resized_logits[0].argmax(dim=0 )
                 semantic_segmentation.append(a__ )
       else:
            SCREAMING_SNAKE_CASE__   =   logits.argmax(dim=1 )
            SCREAMING_SNAKE_CASE__   =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

       return semantic_segmentation





import doctest
from collections import deque

import numpy as np






class 	UpperCAmelCase__		:






  """simple docstring"""


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







  def 				lowercase_		(							self						:		Optional[int] )		->							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()


