





'''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


__lowerCAmelCase							:	Tuple			=logging.get_logger(__name__)





def 						UpperCamelCase				(							_lowerCamelCase					:					np.ndarray						,       _lowerCamelCase					:					Union[int, Iterable[int]]						,       _lowerCamelCase					:					bool						,       _lowerCamelCase					:					int       ):
    def constraint_to_multiple_of(_lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Optional[Any]						,       _lowerCamelCase					:					Optional[int]=0						,       _lowerCamelCase					:					int=None       ):
        A__				=			round(val / multiple       ) * multiple

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

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

        return x

    A__				=			(output_size, output_size) if isinstance(_lowerCamelCase						,       _lowerCamelCase       ) else output_size

    A__,	A__				=			get_image_size(_lowerCamelCase       )
    A__,	A__				=			output_size

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

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

    A__				=			constraint_to_multiple_of(scale_height * input_height						,       multiple=_lowerCamelCase       )
    A__				=			constraint_to_multiple_of(scale_width * input_width						,       multiple=_lowerCamelCase       )

    return (new_height, new_width)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				["""pixel_values"""]
 def __init__(				self      :List[str]						,       lowercase_      :bool = True						,       lowercase_      :Dict[str, int] = None						,       lowercase_      :PILImageResampling = PILImageResampling.BILINEAR						,       lowercase_      :bool = False						,       lowercase_      :int = 1						,       lowercase_      :bool = True						,       lowercase_      :Union[int, float] = 1 / 2_55						,       lowercase_      :bool = True						,       lowercase_      :Optional[Union[float, List[float]]] = None						,       lowercase_      :Optional[Union[float, List[float]]] = None						,       **lowercase_      :Optional[Any]						,       )->							None:
     super().__init__(**lowercase_							)
     A__				=			size if size is not None else {"height": 3_84, "width": 3_84}
     A__				=			get_size_dict(lowercase_							)
     A__				=			do_resize
     A__				=			size
     A__				=			keep_aspect_ratio
     A__				=			ensure_multiple_of
     A__				=			resample
     A__				=			do_rescale
     A__				=			rescale_factor
     A__				=			do_normalize
     A__				=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     A__				=			image_std if image_std is not None else IMAGENET_STANDARD_STD
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :np.ndarray						,       lowercase_      :Dict[str, int]						,       lowercase_      :bool = False						,       lowercase_      :int = 1						,       lowercase_      :PILImageResampling = PILImageResampling.BICUBIC						,       lowercase_      :Optional[Union[str, ChannelDimension]] = None						,       **lowercase_      :Tuple						,       )->							np.ndarray:
     A__				=			get_size_dict(lowercase_							)
     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()}"							)
     A__				=			get_resize_output_image_size(
         lowercase_						,       output_size=(size["height"], size["width"])						,       keep_aspect_ratio=lowercase_						,       multiple=lowercase_						,       )
     return resize(lowercase_						,       size=lowercase_						,       resample=lowercase_						,       data_format=lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :np.ndarray						,       lowercase_      :Union[int, float]						,       lowercase_      :Optional[Union[str, ChannelDimension]] = None						,       **lowercase_      :int						,       )->							int:
     return rescale(lowercase_						,       scale=lowercase_						,       data_format=lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :np.ndarray						,       lowercase_      :Union[float, List[float]]						,       lowercase_      :Union[float, List[float]]						,       lowercase_      :Optional[Union[str, ChannelDimension]] = None						,       **lowercase_      :Optional[Any]						,       )->							np.ndarray:
     return normalize(lowercase_						,       mean=lowercase_						,       std=lowercase_						,       data_format=lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :ImageInput						,       lowercase_      :bool = None						,       lowercase_      :int = None						,       lowercase_      :bool = None						,       lowercase_      :int = None						,       lowercase_      :PILImageResampling = None						,       lowercase_      :bool = None						,       lowercase_      :float = None						,       lowercase_      :bool = None						,       lowercase_      :Optional[Union[float, List[float]]] = None						,       lowercase_      :Optional[Union[float, List[float]]] = None						,       lowercase_      :Optional[Union[str, TensorType]] = None						,       lowercase_      :ChannelDimension = ChannelDimension.FIRST						,       **lowercase_      :Optional[Any]						,       )->							PIL.Image.Image:
     A__				=			do_resize if do_resize is not None else self.do_resize
     A__				=			size if size is not None else self.size
     A__				=			get_size_dict(lowercase_							)
     A__				=			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     A__				=			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     A__				=			resample if resample is not None else self.resample
     A__				=			do_rescale if do_rescale is not None else self.do_rescale
     A__				=			rescale_factor if rescale_factor is not None else self.rescale_factor
     A__				=			do_normalize if do_normalize is not None else self.do_normalize
     A__				=			image_mean if image_mean is not None else self.image_mean
     A__				=			image_std if image_std is not None else self.image_std

     A__				=			make_list_of_images(lowercase_							)

     if not valid_images(lowercase_							):
         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.
     A__				=			[to_numpy_array(lowercase_							) for image in images]

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

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

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

     A__				=			[to_channel_dimension_format(lowercase_						,       lowercase_							) for image in images]

     A__				=			{"pixel_values": images}
     return BatchFeature(data=lowercase_						,       tensor_type=lowercase_							)



 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Dict						,       lowercase_      :List[Tuple] = None							)->							List[str]:
     A__				=			outputs.logits

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

         if is_torch_tensor(lowercase_							):
             A__				=			target_sizes.numpy()

         A__				=			[]

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

     return semantic_segmentation










'''simple docstring'''

from __future__ import annotations

from decimal import Decimal

from numpy import array





def 						UpperCamelCase				(							_lowerCamelCase					:					list[list[float]]       ):
    A__				=			Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(_lowerCamelCase       ) == 2 and len(matrix[0]       ) == 2 and len(matrix[1]       ) == 2:
        # Calculate the determinant of the matrix
        A__				=			float(
            d(matrix[0][0]       ) * d(matrix[1][1]       ) - d(matrix[1][0]       ) * d(matrix[0][1]       )       )
        if determinant == 0:
            raise ValueError("This matrix has no inverse."       )

        # Creates a copy of the matrix with swapped positions of the elements
        A__				=			[[0.0, 0.0], [0.0, 0.0]]
        A__,	A__				=			matrix[1][1], matrix[0][0]
        A__,	A__				=			-matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(_lowerCamelCase       )       ) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(_lowerCamelCase       ) == 3
        and len(matrix[0]       ) == 3
        and len(matrix[1]       ) == 3
        and len(matrix[2]       ) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        A__				=			float(
            (
                (d(matrix[0][0]       ) * d(matrix[1][1]       ) * d(matrix[2][2]       ))
                + (d(matrix[0][1]       ) * d(matrix[1][2]       ) * d(matrix[2][0]       ))
                + (d(matrix[0][2]       ) * d(matrix[1][0]       ) * d(matrix[2][1]       ))
            )
            - (
                (d(matrix[0][2]       ) * d(matrix[1][1]       ) * d(matrix[2][0]       ))
                + (d(matrix[0][1]       ) * d(matrix[1][0]       ) * d(matrix[2][2]       ))
                + (d(matrix[0][0]       ) * d(matrix[1][2]       ) * d(matrix[2][1]       ))
            )       )
        if determinant == 0:
            raise ValueError("This matrix has no inverse."       )

        # Creating cofactor matrix
        A__				=			[
            [d(0.0       ), d(0.0       ), d(0.0       )],
            [d(0.0       ), d(0.0       ), d(0.0       )],
            [d(0.0       ), d(0.0       ), d(0.0       )],
        ]
        A__				=			(d(matrix[1][1]       ) * d(matrix[2][2]       )) - (
            d(matrix[1][2]       ) * d(matrix[2][1]       )
        )
        A__				=			-(
            (d(matrix[1][0]       ) * d(matrix[2][2]       )) - (d(matrix[1][2]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[1][0]       ) * d(matrix[2][1]       )) - (
            d(matrix[1][1]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][1]       ) * d(matrix[2][2]       )) - (d(matrix[0][2]       ) * d(matrix[2][1]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[2][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[2][0]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[2][1]       )) - (d(matrix[0][1]       ) * d(matrix[2][0]       ))
        )
        A__				=			(d(matrix[0][1]       ) * d(matrix[1][2]       )) - (
            d(matrix[0][2]       ) * d(matrix[1][1]       )
        )
        A__				=			-(
            (d(matrix[0][0]       ) * d(matrix[1][2]       )) - (d(matrix[0][2]       ) * d(matrix[1][0]       ))
        )
        A__				=			(d(matrix[0][0]       ) * d(matrix[1][1]       )) - (
            d(matrix[0][1]       ) * d(matrix[1][0]       )
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        A__				=			array(_lowerCamelCase       )
        for i in range(3       ):
            for j in range(3       ):
                A__				=			cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        A__				=			array(_lowerCamelCase       )
        for i in range(3       ):
            for j in range(3       ):
                inverse_matrix[i][j] /= d(_lowerCamelCase       )

        # Calculate the inverse of the matrix
        return [[float(d(_lowerCamelCase       )       ) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3."       )



