



'''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,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Optional[Any]     =       ["pixel_values"]



       def __init__(					self  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = PILImageResampling.BILINEAR  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = 1 / 255  ,UpperCAmelCase_ = True  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_  ,)	->				None:
           super().__init__(**UpperCAmelCase_   )
           lowercase__									=    size if size is not None else {"shortest_edge": 256}
           lowercase__									=    get_size_dict(UpperCAmelCase_  ,default_to_square=UpperCAmelCase_   )
           lowercase__									=    crop_size if crop_size is not None else {"height": 224, "width": 224}
           lowercase__									=    get_size_dict(UpperCAmelCase_  ,param_name="crop_size"   )
           lowercase__									=    do_resize
           lowercase__									=    size
           lowercase__									=    resample
           lowercase__									=    do_center_crop
           lowercase__									=    crop_size
           lowercase__									=    do_rescale
           lowercase__									=    rescale_factor
           lowercase__									=    do_normalize
           lowercase__									=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowercase__									=    image_std if image_std is not None else IMAGENET_STANDARD_STD



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = PILImageResampling.BICUBIC  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_  ,)	->				np.ndarray:
           lowercase__									=    get_size_dict(UpperCAmelCase_  ,default_to_square=UpperCAmelCase_   )
           if "shortest_edge" not in size:
               raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''   )
           lowercase__									=    get_resize_output_image_size(UpperCAmelCase_  ,size=size["shortest_edge"]  ,default_to_square=UpperCAmelCase_   )
           return resize(UpperCAmelCase_  ,size=UpperCAmelCase_  ,resample=UpperCAmelCase_  ,data_format=UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_  ,)	->				np.ndarray:
           lowercase__									=    get_size_dict(UpperCAmelCase_   )
           if "height" not in size or "width" not in size:
               raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''   )
           return center_crop(UpperCAmelCase_  ,size=(size["height"], size["width"])  ,data_format=UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_   )	->				np.ndarray:
           return rescale(UpperCAmelCase_  ,scale=UpperCAmelCase_  ,data_format=UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,**UpperCAmelCase_  ,)	->				np.ndarray:
           return normalize(UpperCAmelCase_  ,mean=UpperCAmelCase_  ,std=UpperCAmelCase_  ,data_format=UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = ChannelDimension.FIRST  ,**UpperCAmelCase_  ,)	->				Dict:
           lowercase__									=    do_resize if do_resize is not None else self.do_resize
           lowercase__									=    size if size is not None else self.size
           lowercase__									=    get_size_dict(UpperCAmelCase_  ,default_to_square=UpperCAmelCase_   )
           lowercase__									=    resample if resample is not None else self.resample
           lowercase__									=    do_center_crop if do_center_crop is not None else self.do_center_crop
           lowercase__									=    crop_size if crop_size is not None else self.crop_size
           lowercase__									=    get_size_dict(UpperCAmelCase_  ,param_name="crop_size"   )
           lowercase__									=    do_rescale if do_rescale is not None else self.do_rescale
           lowercase__									=    rescale_factor if rescale_factor is not None else self.rescale_factor
           lowercase__									=    do_normalize if do_normalize is not None else self.do_normalize
           lowercase__									=    image_mean if image_mean is not None else self.image_mean
           lowercase__									=    image_std if image_std is not None else self.image_std

           lowercase__									=    make_list_of_images(UpperCAmelCase_   )

           if not valid_images(UpperCAmelCase_   ):
               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_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.
           lowercase__									=    [to_numpy_array(UpperCAmelCase_   ) for image in images]

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

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

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

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

           lowercase__									=    [to_channel_dimension_format(UpperCAmelCase_  ,UpperCAmelCase_   ) for image in images]

           lowercase__									=    {"pixel_values": images}
           return BatchFeature(data=UpperCAmelCase_  ,tensor_type=UpperCAmelCase_   )







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None   )	->				Optional[int]:
           lowercase__									=    outputs.logits

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

               if is_torch_tensor(UpperCAmelCase_   ):
                   lowercase__									=    target_sizes.numpy()

               lowercase__									=    []

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

           return semantic_segmentation









'''simple docstring'''





def 							lowerCamelCase	(					):





    '''simple docstring'''


    return [list(range(1_000 - i  ,-1_000 - i  ,-1							)							) for i in range(1_000							)]


SCREAMING_SNAKE_CASE__          =  generate_large_matrix()
SCREAMING_SNAKE_CASE__          =  (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    assert all(row == sorted(_snake_case  ,reverse=_snake_case							) for row in grid							)
    assert all(list(_snake_case							) == sorted(_snake_case  ,reverse=_snake_case							) for col in zip(*_snake_case							)							)




def 							lowerCamelCase	(					_snake_case	:    list[int]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(_snake_case							) - 1

    # Edge cases such as no values or all numbers are negative.
    if not array or array[0] < 0:
        return 0

    while right + 1 > left:
        lowercase__									=    (left + right) // 2
        lowercase__									=    array[mid]

        # Num must be negative and the index must be greater than or equal to 0.
        if num < 0 and array[mid - 1] >= 0:
            return mid

        if num >= 0:
            lowercase__									=    mid + 1
        else:
            lowercase__									=    mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
    return len(_snake_case							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    lowercase__									=    len(grid[0]							)

    for i in range(len(_snake_case							)							):
        lowercase__									=    find_negative_index(grid[i][:bound]							)
        total += bound
    return (len(_snake_case							) * len(grid[0]							)) - total




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    return len([number for row in grid for number in row if number < 0]							)




def 							lowerCamelCase	(					_snake_case	:    list[list[int]]							):





    '''simple docstring'''


    lowercase__									=    0
    for row in grid:
        for i, number in enumerate(_snake_case							):
            if number < 0:
                total += len(_snake_case							) - i
                break
    return total




def 							lowerCamelCase	(					):





    '''simple docstring'''


    from timeit import timeit

    print("Running benchmarks"							)
    lowercase__									=    (
        "from __main__ import count_negatives_binary_search, "
        "count_negatives_brute_force, count_negatives_brute_force_with_break, grid"
    )
    for func in (
        "count_negatives_binary_search",  # took 0.7727 seconds
        "count_negatives_brute_force_with_break",  # took 4.6505 seconds
        "count_negatives_brute_force",  # took 12.8160 seconds
    ):
        lowercase__									=    timeit(f'''{func}(grid=grid)'''  ,setup=_snake_case  ,number=500							)
        print(f'''{func}() took {time:0.4f} seconds'''							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()




