'''simple docstring'''




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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


__UpperCAmelCase												=logging.get_logger(__name__)






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Tuple[int, int]:
    def constraint_to_multiple_of(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=0    ,  UpperCamelCase__=None    ):
        __lowerCamelCase          =  round(val / multiple    ) * multiple

        if max_val is not None and x > max_val:
            __lowerCamelCase          =  math.floor(val / multiple    ) * multiple

        if x < min_val:
            __lowerCamelCase          =  math.ceil(val / multiple    ) * multiple

        return x

    __lowerCamelCase          =  (output_size, output_size) if isinstance(UpperCamelCase__    ,  UpperCamelCase__    ) else output_size

    __lowerCamelCase     ,  __lowerCamelCase          =  get_image_size(UpperCamelCase__    )
    __lowerCamelCase     ,  __lowerCamelCase          =  output_size

    # determine new height and width
    __lowerCamelCase          =  output_height / input_height
    __lowerCamelCase          =  output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width    ) < abs(1 - scale_height    ):
            # fit width
            __lowerCamelCase          =  scale_width
        else:
            # fit height
            __lowerCamelCase          =  scale_height

    __lowerCamelCase          =  constraint_to_multiple_of(scale_height * input_height    ,  multiple=UpperCamelCase__    )
    __lowerCamelCase          =  constraint_to_multiple_of(scale_width * input_width    ,  multiple=UpperCamelCase__    )

    return (new_height, new_width)




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Union[str, Any]							=["pixel_values"]






     def __init__(			self		:		List[Any]  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		PILImageResampling = PILImageResampling.BILINEAR  ,    a		:		bool = False  ,    a		:		int = 1  ,    a		:		bool = True  ,    a		:		Union[int, float] = 1 / 2_55  ,    a		:		bool = True  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    **a		:		Optional[Any]  ,    ):





         """simple docstring"""



         super().__init__(**a   )
         __lowerCamelCase          =  size if size is not None else {'''height''': 3_84, '''width''': 3_84}
         __lowerCamelCase          =  get_size_dict(a   )
         __lowerCamelCase          =  do_resize
         __lowerCamelCase          =  size
         __lowerCamelCase          =  keep_aspect_ratio
         __lowerCamelCase          =  ensure_multiple_of
         __lowerCamelCase          =  resample
         __lowerCamelCase          =  do_rescale
         __lowerCamelCase          =  rescale_factor
         __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        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		np.ndarray  ,    a		:		Dict[str, int]  ,    a		:		bool = False  ,    a		:		int = 1  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		List[Any]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  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()}"""   )
         __lowerCamelCase          =  get_resize_output_image_size(
             a  ,    output_size=(size['''height'''], size['''width'''])  ,    keep_aspect_ratio=a  ,    multiple=a  ,    )
         return resize(a  ,    size=a  ,    resample=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		np.ndarray  ,    a		:		Union[int, float]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Union[str, Any]  ,    ):





         """simple docstring"""



         return rescale(a  ,    scale=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		np.ndarray  ,    a		:		Union[float, List[float]]  ,    a		:		Union[float, List[float]]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Optional[int]  ,    ):





         """simple docstring"""



         return normalize(a  ,    mean=a  ,    std=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		ImageInput  ,    a		:		bool = None  ,    a		:		int = None  ,    a		:		bool = None  ,    a		:		int = None  ,    a		:		PILImageResampling = None  ,    a		:		bool = None  ,    a		:		float = None  ,    a		:		bool = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[str, TensorType]] = None  ,    a		:		ChannelDimension = ChannelDimension.FIRST  ,    **a		:		Optional[Any]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase          =  size if size is not None else self.size
         __lowerCamelCase          =  get_size_dict(a   )
         __lowerCamelCase          =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         __lowerCamelCase          =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         __lowerCamelCase          =  resample if resample is not None else self.resample
         __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          =  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          =  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_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.
         __lowerCamelCase          =  [to_numpy_array(a   ) for image in images]

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

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

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

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

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		str  ,    a		:		List[Tuple] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  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   ):
                 __lowerCamelCase          =  target_sizes.numpy()

             __lowerCamelCase          =  []

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

         return semantic_segmentation

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   float:
    if principal <= 0:
        raise Exception('''Principal borrowed must be > 0'''    )
    if rate_per_annum < 0:
        raise Exception('''Rate of interest must be >= 0'''    )
    if years_to_repay <= 0 or not isinstance(UpperCamelCase__    ,  UpperCamelCase__    ):
        raise Exception('''Years to repay must be an integer > 0'''    )

    # Yearly rate is divided by 12 to get monthly rate
    __lowerCamelCase          =  rate_per_annum / 12

    # Years to repay is multiplied by 12 to get number of payments as payment is monthly
    __lowerCamelCase          =  years_to_repay * 12

    return (
        principal
        * rate_per_month
        * (1 + rate_per_month) ** number_of_payments
        / ((1 + rate_per_month) ** number_of_payments - 1)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
