




"""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


lowercase__     						=						logging.get_logger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				def constraint_to_multiple_of(lowercase__							,    lowercase__							,    lowercase__=0							,    lowercase__=None		):
								_lowerCamelCase     :			List[Any]    =    round(val / multiple		) * multiple

								if max_val is not None and x > max_val:
												_lowerCamelCase     :			Optional[int]    =    math.floor(val / multiple		) * multiple

								if x < min_val:
												_lowerCamelCase     :			List[str]    =    math.ceil(val / multiple		) * multiple

								return x

				_lowerCamelCase     :			Optional[int]    =    (output_size, output_size) if isinstance(lowercase__							,    lowercase__		) else output_size

				_lowerCamelCase, _lowerCamelCase     :			List[str]    =    get_image_size(lowercase__		)
				_lowerCamelCase, _lowerCamelCase     :			Union[str, Any]    =    output_size

				# determine new height and width
				_lowerCamelCase     :			Dict    =    output_height / input_height
				_lowerCamelCase     :			Any    =    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     :			Tuple    =    scale_width
								else:
												# fit height
												_lowerCamelCase     :			Any    =    scale_height

				_lowerCamelCase     :			Optional[int]    =    constraint_to_multiple_of(scale_height * input_height							,    multiple=lowercase__		)
				_lowerCamelCase     :			Any    =    constraint_to_multiple_of(scale_width * input_width							,    multiple=lowercase__		)

				return (new_height, new_width)






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""pixel_values"""]
			def __init__(  self  ,      lowercase = True  ,      lowercase = None  ,      lowercase = PILImageResampling.BILINEAR  ,      lowercase = False  ,      lowercase = 1  ,      lowercase = True  ,      lowercase = 1 / 255  ,      lowercase = True  ,      lowercase = None  ,      lowercase = None  ,      **lowercase  ,      ):
							super().__init__(**lowercase  )
							_lowerCamelCase     :			Optional[int]    =    size if size is not None else {'height': 384, 'width': 384}
							_lowerCamelCase     :			Union[str, Any]    =    get_size_dict(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    do_resize
							_lowerCamelCase     :			List[Any]    =    size
							_lowerCamelCase     :			Tuple    =    keep_aspect_ratio
							_lowerCamelCase     :			List[Any]    =    ensure_multiple_of
							_lowerCamelCase     :			str    =    resample
							_lowerCamelCase     :			Optional[Any]    =    do_rescale
							_lowerCamelCase     :			Union[str, Any]    =    rescale_factor
							_lowerCamelCase     :			int    =    do_normalize
							_lowerCamelCase     :			int    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_lowerCamelCase     :			Optional[Any]    =    image_std if image_std is not None else IMAGENET_STANDARD_STD
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase = False  ,      lowercase = 1  ,      lowercase = PILImageResampling.BICUBIC  ,      lowercase = None  ,      **lowercase  ,      ):
							_lowerCamelCase     :			str    =    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()}'''  )
							_lowerCamelCase     :			Optional[Any]    =    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 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      **lowercase  ,      ):
							return rescale(lowercase  ,      scale=lowercase  ,      data_format=lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ,      lowercase = None  ,      **lowercase  ,      ):
							return normalize(lowercase  ,      mean=lowercase  ,      std=lowercase  ,      data_format=lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = None  ,      lowercase = ChannelDimension.FIRST  ,      **lowercase  ,      ):
							_lowerCamelCase     :			List[str]    =    do_resize if do_resize is not None else self.do_resize
							_lowerCamelCase     :			List[str]    =    size if size is not None else self.size
							_lowerCamelCase     :			Tuple    =    get_size_dict(lowercase  )
							_lowerCamelCase     :			Any    =    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
							_lowerCamelCase     :			int    =    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
							_lowerCamelCase     :			int    =    resample if resample is not None else self.resample
							_lowerCamelCase     :			int    =    do_rescale if do_rescale is not None else self.do_rescale
							_lowerCamelCase     :			int    =    rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowerCamelCase     :			Union[str, Any]    =    do_normalize if do_normalize is not None else self.do_normalize
							_lowerCamelCase     :			Dict    =    image_mean if image_mean is not None else self.image_mean
							_lowerCamelCase     :			Tuple    =    image_std if image_std is not None else self.image_std

							_lowerCamelCase     :			List[Any]    =    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.
							_lowerCamelCase     :			Tuple    =    [to_numpy_array(lowercase  ) for image in images]

							if do_resize:
											_lowerCamelCase     :			List[Any]    =    [self.resize(image=lowercase  ,      size=lowercase  ,      resample=lowercase  ) for image in images]

							if do_rescale:
											_lowerCamelCase     :			List[Any]    =    [self.rescale(image=lowercase  ,      scale=lowercase  ) for image in images]

							if do_normalize:
											_lowerCamelCase     :			List[str]    =    [self.normalize(image=lowercase  ,      mean=lowercase  ,      std=lowercase  ) for image in images]

							_lowerCamelCase     :			Optional[Any]    =    [to_channel_dimension_format(lowercase  ,      lowercase  ) for image in images]

							_lowerCamelCase     :			Dict    =    {'pixel_values': images}
							return BatchFeature(data=lowercase  ,      tensor_type=lowercase  )






			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ):
							_lowerCamelCase     :			int    =    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  ):
															_lowerCamelCase     :			Union[str, Any]    =    target_sizes.numpy()

											_lowerCamelCase     :			Union[str, Any]    =    []

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

							return semantic_segmentation

from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@staticmethod
	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase )				->       int:
					raise NotImplementedError()



	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					raise NotImplementedError()


