
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  : Any						   =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   int:
       def constraint_to_multiple_of(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=0 ,     SCREAMING_SNAKE_CASE__=None    ):
              lowercase							:					str	     =				round(val / multiple    ) * multiple

              if max_val is not None and x > max_val:
                     lowercase							:					Union[str, Any]	     =				math.floor(val / multiple    ) * multiple

              if x < min_val:
                     lowercase							:					Optional[int]	     =				math.ceil(val / multiple    ) * multiple

              return x

       lowercase							:					List[Any]	     =				(output_size, output_size) if isinstance(_A ,     _A    ) else output_size

       lowercase							:					Dict	     =				get_image_size(_A    )
       lowercase							:					Optional[int]	     =				output_size

       # determine new height and width
       lowercase							:					Dict	     =				output_height / input_height
       lowercase							:					List[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
                     lowercase							:					Dict	     =				scale_width
              else:
                     # fit height
                     lowercase							:					Optional[int]	     =				scale_height

       lowercase							:					str	     =				constraint_to_multiple_of(scale_height * input_height ,     multiple=_A    )
       lowercase							:					str	     =				constraint_to_multiple_of(scale_width * input_width ,     multiple=_A    )

       return (new_height, new_width)




class __snake_case		(				lowerCamelCase__					):
    _a		:						Tuple=    ["pixel_values"]
    def __init__(  self		,snake_case = True		,snake_case = None		,snake_case = PILImageResampling.BILINEAR		,snake_case = False		,snake_case = 1		,snake_case = True		,snake_case = 1 / 255		,snake_case = True		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**__snake_case				)
           lowercase							:					List[Any]	     =				size if size is not None else {'height': 384, 'width': 384}
           lowercase							:					List[str]	     =				get_size_dict(__snake_case				)
           lowercase							:					Optional[int]	     =				do_resize
           lowercase							:					Tuple	     =				size
           lowercase							:					str	     =				keep_aspect_ratio
           lowercase							:					Optional[Any]	     =				ensure_multiple_of
           lowercase							:					int	     =				resample
           lowercase							:					Optional[Any]	     =				do_rescale
           lowercase							:					Tuple	     =				rescale_factor
           lowercase							:					Any	     =				do_normalize
           lowercase							:					Optional[Any]	     =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowercase							:					Optional[Any]	     =				image_std if image_std is not None else IMAGENET_STANDARD_STD
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case = False		,snake_case = 1		,snake_case = PILImageResampling.BICUBIC		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				get_size_dict(__snake_case				)
           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()}"				)
           lowercase							:					Any	     =				get_resize_output_image_size(
               __snake_case		,output_size=(size["""height"""], size["""width"""])		,keep_aspect_ratio=__snake_case		,multiple=__snake_case		,)
           return resize(__snake_case		,size=__snake_case		,resample=__snake_case		,data_format=__snake_case		,**__snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return rescale(__snake_case		,scale=__snake_case		,data_format=__snake_case		,**__snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return normalize(__snake_case		,mean=__snake_case		,std=__snake_case		,data_format=__snake_case		,**__snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					Any	     =				do_resize if do_resize is not None else self.do_resize
           lowercase							:					Optional[Any]	     =				size if size is not None else self.size
           lowercase							:					Optional[int]	     =				get_size_dict(__snake_case				)
           lowercase							:					int	     =				keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
           lowercase							:					Any	     =				ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
           lowercase							:					List[str]	     =				resample if resample is not None else self.resample
           lowercase							:					Optional[int]	     =				do_rescale if do_rescale is not None else self.do_rescale
           lowercase							:					Union[str, Any]	     =				rescale_factor if rescale_factor is not None else self.rescale_factor
           lowercase							:					Any	     =				do_normalize if do_normalize is not None else self.do_normalize
           lowercase							:					Dict	     =				image_mean if image_mean is not None else self.image_mean
           lowercase							:					str	     =				image_std if image_std is not None else self.image_std

           lowercase							:					Any	     =				make_list_of_images(__snake_case				)

           if not valid_images(__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_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							:					Dict	     =				[to_numpy_array(__snake_case				) for image in images]

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

           if do_rescale:
                  lowercase							:					int	     =				[self.rescale(image=__snake_case		,scale=__snake_case				) for image in images]

           if do_normalize:
                  lowercase							:					Tuple	     =				[self.normalize(image=__snake_case		,mean=__snake_case		,std=__snake_case				) for image in images]

           lowercase							:					List[str]	     =				[to_channel_dimension_format(__snake_case		,__snake_case				) for image in images]

           lowercase							:					str	     =				{'pixel_values': images}
           return BatchFeature(data=__snake_case		,tensor_type=__snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					Any	     =				outputs.logits

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

                  if is_torch_tensor(__snake_case				):
                         lowercase							:					List[str]	     =				target_sizes.numpy()

                  lowercase							:					str	     =				[]

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

           return semantic_segmentation


class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=None		,snake_case=None				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				data
           lowercase							:					List[Any]	     =				previous
           lowercase							:					List[str]	     =				next_node
    def __str__(  self				):



           '''simple docstring'''







           return f"{self.data}"
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.data
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.next
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.previous






class __snake_case		:
    def __init__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				head
    def __iter__(  self				):



           '''simple docstring'''







           return self
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if not self.current:
                  raise StopIteration
           else:
                  lowercase							:					Union[str, Any]	     =				self.current.get_data()
                  lowercase							:					Optional[Any]	     =				self.current.get_next()
                  return value






class __snake_case		:
    def __init__(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				None  # First node in list
           lowercase							:					str	     =				None  # Last node in list
    def __str__(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				self.head
           lowercase							:					str	     =				[]
           while current is not None:
                  nodes.append(current.get_data()				)
                  lowercase							:					Dict	     =				current.get_next()
           return " ".join(str(snake_case				) for node in nodes				)
    def __contains__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.head
           while current:
                  if current.get_data() == value:
                         return True
                  lowercase							:					Any	     =				current.get_next()
           return False
    def __iter__(  self				):



           '''simple docstring'''







           return LinkedListIterator(self.head				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.head:
                  return self.head.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.tail:
                  return self.tail.get_data()
           return None
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  lowercase							:					Any	     =				node
                  lowercase							:					Dict	     =				node
           else:
                  self.insert_before_node(self.head		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				Node(snake_case				)
           if self.head is None:
                  self.set_head(snake_case				)
           else:
                  self.set_tail(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				node
           lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous() is None:
                  lowercase							:					Optional[int]	     =				node_to_insert
           else:
                  lowercase							:					Optional[int]	     =				node_to_insert

           lowercase							:					List[Any]	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				node
           lowercase							:					List[str]	     =				node.next

           if node.get_next() is None:
                  lowercase							:					Union[str, Any]	     =				node_to_insert
           else:
                  lowercase							:					List[str]	     =				node_to_insert

           lowercase							:					Dict	     =				node_to_insert
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				1
           lowercase							:					List[str]	     =				Node(snake_case				)
           lowercase							:					Any	     =				self.head
           while node:
                  if current_position == position:
                         self.insert_before_node(snake_case		,snake_case				)
                         return
                  current_position += 1
                  lowercase							:					List[Any]	     =				node.next
           self.insert_after_node(self.tail		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.head
           while node:
                  if node.get_data() == item:
                         return node
                  lowercase							:					Any	     =				node.get_next()
           raise Exception("""Node not found"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if (node := self.get_node(snake_case				)) is not None:
                  if node == self.head:
                         lowercase							:					Optional[Any]	     =				self.head.get_next()

                  if node == self.tail:
                         lowercase							:					List[str]	     =				self.tail.get_previous()

                  self.remove_node_pointers(snake_case				)
    @staticmethod
    def 				_SCREAMING_SNAKE_CASE	(  snake_case				):



           '''simple docstring'''







           if node.get_next():
                  lowercase							:					Optional[int]	     =				node.previous

           if node.get_previous():
                  lowercase							:					Union[str, Any]	     =				node.next

           lowercase							:					Union[str, Any]	     =				None
           lowercase							:					Optional[Any]	     =				None
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.head is None







def 						_snake_case(  )			->   None:
       pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()
