




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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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							     =					logging.get_logger(__name__)

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = ['''pixel_values''']





    def __init__( self  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = 1 / 255  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = True  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						None:
         super().__init__(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Tuple  =     size if size is not None else {'''height''': 256, '''width''': 256}
         UpperCAmelCase_     :				Tuple  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase_     :				Dict  =     get_size_dict(_SCREAMING_SNAKE_CASE  ,param_name='''crop_size'''    )

         UpperCAmelCase_     :				Optional[int]  =     do_resize
         UpperCAmelCase_     :				Dict  =     size
         UpperCAmelCase_     :				Tuple  =     resample
         UpperCAmelCase_     :				Dict  =     do_center_crop
         UpperCAmelCase_     :				Optional[int]  =     crop_size
         UpperCAmelCase_     :				Union[str, Any]  =     do_rescale
         UpperCAmelCase_     :				Any  =     rescale_factor
         UpperCAmelCase_     :				List[Any]  =     do_normalize
         UpperCAmelCase_     :				List[str]  =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_     :				Optional[int]  =     image_std if image_std is not None else IMAGENET_STANDARD_STD





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						np.ndarray:
         UpperCAmelCase_     :				Optional[int]  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
         return resize(
             _SCREAMING_SNAKE_CASE  ,size=(size['''height'''], size['''width'''])  ,resample=_SCREAMING_SNAKE_CASE  ,data_format=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						np.ndarray:
         UpperCAmelCase_     :				int  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. 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  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						List[str]:
         return rescale(_SCREAMING_SNAKE_CASE  ,scale=_SCREAMING_SNAKE_CASE  ,data_format=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,**_SCREAMING_SNAKE_CASE  ,)					->						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  ,_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 = None  ,_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  ,)					->						PIL.Image.Image:
         UpperCAmelCase_     :				Any  =     do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_     :				List[Any]  =     resample if resample is not None else self.resample
         UpperCAmelCase_     :				str  =     do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_     :				Any  =     do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_     :				str  =     rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_     :				Tuple  =     do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_     :				List[Any]  =     image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_     :				List[str]  =     image_std if image_std is not None else self.image_std

         UpperCAmelCase_     :				List[str]  =     size if size is not None else self.size
         UpperCAmelCase_     :				int  =     get_size_dict(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_     :				List[str]  =     get_size_dict(_SCREAMING_SNAKE_CASE  ,param_name='''crop_size'''    )

         UpperCAmelCase_     :				Tuple  =     make_list_of_images(_SCREAMING_SNAKE_CASE    )

         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.'''    )

         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.'''    )

         # All transformations expect numpy arrays.
         UpperCAmelCase_     :				Optional[int]  =     [to_numpy_array(_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_resize:
              UpperCAmelCase_     :				int  =     [self.resize(image=_SCREAMING_SNAKE_CASE  ,size=_SCREAMING_SNAKE_CASE  ,resample=_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_center_crop:
              UpperCAmelCase_     :				int  =     [self.center_crop(image=_SCREAMING_SNAKE_CASE  ,size=_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_rescale:
              UpperCAmelCase_     :				Optional[int]  =     [self.rescale(image=_SCREAMING_SNAKE_CASE  ,scale=_SCREAMING_SNAKE_CASE    ) for image in images]

         if do_normalize:
              UpperCAmelCase_     :				Dict  =     [self.normalize(image=_SCREAMING_SNAKE_CASE  ,mean=_SCREAMING_SNAKE_CASE  ,std=_SCREAMING_SNAKE_CASE    ) for image in images]

         UpperCAmelCase_     :				List[Any]  =     [to_channel_dimension_format(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) for image in images]

         UpperCAmelCase_     :				str  =     {'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE  ,tensor_type=_SCREAMING_SNAKE_CASE    )





from __future__ import annotations

import math

__a							     =					'2020.9.26'
__a							     =					'xcodz-dot, cclaus, dhruvmanila'







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     if not all(isinstance(_lowercase					,		(float, int) ) for val in locals().values() ):
          UpperCAmelCase_     :				Optional[int]  =     f'''Input values must either be float or int: {list(locals().values() )}'''
          raise TypeError(_lowercase )
     UpperCAmelCase_     :				Tuple  =     ((x * distance) / (z + distance)) * scale
     UpperCAmelCase_     :				str  =     ((y * distance) / (z + distance)) * scale
     return projected_x, projected_y







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          raise TypeError('''Axis must be a str''' )
     UpperCAmelCase_     :				Optional[Any]  =     locals()
     del input_variables["axis"]
     if not all(isinstance(_lowercase					,		(float, int) ) for val in input_variables.values() ):
          UpperCAmelCase_     :				List[Any]  =     (
              '''Input values except axis must either be float or int: '''
              f'''{list(input_variables.values() )}'''
          )
          raise TypeError(_lowercase )
     UpperCAmelCase_     :				Dict  =     (angle % 360) / 450 * 180 / math.pi
     if axis == "z":
          UpperCAmelCase_     :				Optional[int]  =     x * math.cos(_lowercase ) - y * math.sin(_lowercase )
          UpperCAmelCase_     :				List[Any]  =     y * math.cos(_lowercase ) + x * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     z
     elif axis == "x":
          UpperCAmelCase_     :				Any  =     y * math.cos(_lowercase ) - z * math.sin(_lowercase )
          UpperCAmelCase_     :				int  =     z * math.cos(_lowercase ) + y * math.sin(_lowercase )
          UpperCAmelCase_     :				Dict  =     x
     elif axis == "y":
          UpperCAmelCase_     :				Union[str, Any]  =     x * math.cos(_lowercase ) - z * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     z * math.cos(_lowercase ) + x * math.sin(_lowercase )
          UpperCAmelCase_     :				Optional[int]  =     y
     else:
          raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )

     return new_x, new_y, new_z


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
     print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")