



'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import 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_vision_available, logging


if is_vision_available():
   import PIL


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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = b.T
       _UpperCAmelCase  :						str			     = np.sum(np.square(lowerCAmelCase_		)       ,   axis=1		)
       _UpperCAmelCase  :						Union[str, Any]			     = np.sum(np.square(lowerCAmelCase_		)       ,   axis=0		)
       _UpperCAmelCase  :						Tuple			     = np.matmul(lowerCAmelCase_       ,   lowerCAmelCase_		)
       _UpperCAmelCase  :						Dict			     = aa[:, None] - 2 * ab + ba[None, :]
       return d




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = x.reshape(-1       ,   3		)
       _UpperCAmelCase  :						str			     = squared_euclidean_distance(lowerCAmelCase_       ,   lowerCAmelCase_		)
       return np.argmin(lowerCAmelCase_       ,   axis=1		)



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  ["""pixel_values"""]







 def __init__(							self  ,a_ = None  ,a_ = True  ,a_ = None  ,a_ = PILImageResampling.BILINEAR  ,a_ = True  ,a_ = True  ,**a_  ,)		->   None:
        super().__init__(**a_							)
        _UpperCAmelCase  :						str			     = size if size is not None else {"""height""": 256, """width""": 256}
        _UpperCAmelCase  :						str			     = get_size_dict(a_							)
        _UpperCAmelCase  :						Optional[Any]			     = np.array(a_							) if clusters is not None else None
        _UpperCAmelCase  :						Dict			     = do_resize
        _UpperCAmelCase  :						List[str]			     = size
        _UpperCAmelCase  :						Union[str, Any]			     = resample
        _UpperCAmelCase  :						Union[str, Any]			     = do_normalize
        _UpperCAmelCase  :						Tuple			     = do_color_quantize







 def _snake_case (							self  ,a_  ,a_  ,a_ = PILImageResampling.BILINEAR  ,a_ = None  ,**a_  ,)		->   np.ndarray:
        _UpperCAmelCase  :						str			     = get_size_dict(a_							)
        if "height" not in size or "width" not in size:
               raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''							)
        return resize(
            a_  ,size=(size["""height"""], size["""width"""])  ,resample=a_  ,data_format=a_  ,**a_							)







 def _snake_case (							self  ,a_  ,a_ = None  ,)		->   np.ndarray:
        _UpperCAmelCase  :						List[str]			     = rescale(image=a_  ,scale=1 / 127.5  ,data_format=a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = image - 1
        return image







 def _snake_case (							self  ,a_  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = None  ,a_ = ChannelDimension.FIRST  ,**a_  ,)		->   PIL.Image.Image:
        _UpperCAmelCase  :						Union[str, Any]			     = do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase  :						List[str]			     = size if size is not None else self.size
        _UpperCAmelCase  :						Dict			     = get_size_dict(a_							)
        _UpperCAmelCase  :						List[str]			     = resample if resample is not None else self.resample
        _UpperCAmelCase  :						str			     = do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase  :						str			     = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        _UpperCAmelCase  :						Dict			     = clusters if clusters is not None else self.clusters
        _UpperCAmelCase  :						Union[str, Any]			     = np.array(a_							)

        _UpperCAmelCase  :						List[Any]			     = 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 or resample is None:
               raise ValueError("""Size and resample must be specified if do_resize is True."""							)

        if do_color_quantize and clusters is None:
               raise ValueError("""Clusters must be specified if do_color_quantize is True."""							)

        # All transformations expect numpy arrays.
        _UpperCAmelCase  :						Dict			     = [to_numpy_array(a_							) for image in images]

        if do_resize:
               _UpperCAmelCase  :						Optional[int]			     = [self.resize(image=a_  ,size=a_  ,resample=a_							) for image in images]

        if do_normalize:
               _UpperCAmelCase  :						Optional[Any]			     = [self.normalize(image=a_							) for image in images]

        if do_color_quantize:
               _UpperCAmelCase  :						List[Any]			     = [to_channel_dimension_format(a_  ,ChannelDimension.LAST							) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               _UpperCAmelCase  :						Optional[Any]			     = np.array(a_							)
               _UpperCAmelCase  :						int			     = color_quantize(a_  ,a_							).reshape(images.shape[:-1]							)

               # flatten to (batch_size, height*width)
               _UpperCAmelCase  :						Any			     = images.shape[0]
               _UpperCAmelCase  :						List[Any]			     = images.reshape(a_  ,-1							)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               _UpperCAmelCase  :						Dict			     = list(a_							)
        else:
               _UpperCAmelCase  :						Any			     = [to_channel_dimension_format(a_  ,a_							) for image in images]

        _UpperCAmelCase  :						Tuple			     = {"""input_ids""": images}
        return BatchFeature(data=a_  ,tensor_type=a_							)








'''simple docstring'''






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = False		)->		list[float]:





       '''simple docstring'''


       if radian_mode:
              return [magnitude * cos(lowerCAmelCase_		), magnitude * sin(lowerCAmelCase_		)]
       return [magnitude * cos(radians(lowerCAmelCase_		)		), magnitude * sin(radians(lowerCAmelCase_		)		)]




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = 10**-1		)->		bool:





       '''simple docstring'''


       _UpperCAmelCase  :						NDArray[floataa]			     = cross(lowerCAmelCase_       ,   lowerCAmelCase_		)
       _UpperCAmelCase  :						float			     = sum(lowerCAmelCase_		)
       return abs(lowerCAmelCase_		) < eps


if __name__ == "__main__":
   # Test to check if it works
   A_							: str						 =	array(
       [
           polar_force(718.4, 1_8_0 - 3_0),
           polar_force(879.54, 4_5),
           polar_force(1_0_0, -9_0),
       ]
   )

   A_							: NDArray[floataa]						 =	array([[0, 0], [0, 0], [0, 0]])

   assert in_static_equilibrium(forces, location)

   # Problem 1 in image_data/2D_problems.jpg
   A_							: List[str]						 =	array(
       [
           polar_force(3_0 * 9.81, 1_5),
           polar_force(2_1_5, 1_8_0 - 4_5),
           polar_force(2_6_4, 9_0 - 3_0),
       ]
   )

   A_							: Tuple						 =	array([[0, 0], [0, 0], [0, 0]])

   assert in_static_equilibrium(forces, location)

   # Problem in image_data/2D_problems_1.jpg
   A_							: Dict						 =	array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])

   A_							: Union[str, Any]						 =	array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])

   assert in_static_equilibrium(forces, location)

   import doctest

   doctest.testmod()



