





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							:			str        =							logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Tuple[int, int]:
    def constraint_to_multiple_of(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=0		,      _UpperCAmelCase=None					):
        lowerCamelCase__       : List[str]   					=						round(val / multiple					) * multiple

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

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

        return x

    lowerCamelCase__       : Dict   					=						(output_size, output_size) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) else output_size

    lowerCamelCase__					,				lowerCamelCase__       : str   					=						get_image_size(_UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						output_size

    # determine new height and width
    lowerCamelCase__       : List[str]   					=						output_height / input_height
    lowerCamelCase__       : str   					=						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__       : Optional[int]   					=						scale_width
        else:
            # fit height
            lowerCamelCase__       : Tuple   					=						scale_height

    lowerCamelCase__       : str   					=						constraint_to_multiple_of(scale_height * input_height		,      multiple=_UpperCAmelCase					)
    lowerCamelCase__       : Any   					=						constraint_to_multiple_of(scale_width * input_width		,      multiple=_UpperCAmelCase					)

    return (new_height, new_width)


class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BILINEAR     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : Optional[int]     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						size if size is not None else {'height': 384, 'width': 384}
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						do_resize
     lowerCamelCase__       : List[str]   					=						size
     lowerCamelCase__       : List[Any]   					=						keep_aspect_ratio
     lowerCamelCase__       : Dict   					=						ensure_multiple_of
     lowerCamelCase__       : str   					=						resample
     lowerCamelCase__       : int   					=						do_rescale
     lowerCamelCase__       : Any   					=						rescale_factor
     lowerCamelCase__       : List[str]   					=						do_normalize
     lowerCamelCase__       : Tuple   					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__       : str   					=						image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	A_      ( self    : int     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Optional[Any]     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase      )
     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__       : List[Any]   					=						get_resize_output_image_size(
         UpperCAmelCase     ,	output_size=(size['height'], size['width'])     ,	keep_aspect_ratio=UpperCAmelCase     ,	multiple=UpperCAmelCase     ,	)
     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      str:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Tuple     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Union[str, Any]     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : int   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : List[str]   					=						get_size_dict(UpperCAmelCase      )
     lowerCamelCase__       : int   					=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowerCamelCase__       : Dict   					=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowerCamelCase__       : List[str]   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Any   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : List[Any]   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : int   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Any   					=						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 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__       : Dict   					=						[to_numpy_array(UpperCAmelCase      ) for image in images]

     if do_resize:
         lowerCamelCase__       : Dict   					=						[self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase      ) for image in images]

     if do_rescale:
         lowerCamelCase__       : Tuple   					=						[self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase      ) for image in images]

     if do_normalize:
         lowerCamelCase__       : int   					=						[self.normalize(image=UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Optional[Any]   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Any   					=						{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )







 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : List[Tuple] = None      )     ->      Dict:
     lowerCamelCase__       : Tuple   					=						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      ):
             lowerCamelCase__       : List[str]   					=						target_sizes.numpy()

         lowerCamelCase__       : Tuple   					=						[]

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

     return semantic_segmentation












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							:			str        =							logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Tuple[int, int]:
    def constraint_to_multiple_of(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=0		,      _UpperCAmelCase=None					):
        lowerCamelCase__       : List[str]   					=						round(val / multiple					) * multiple

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

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

        return x

    lowerCamelCase__       : Dict   					=						(output_size, output_size) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) else output_size

    lowerCamelCase__					,				lowerCamelCase__       : str   					=						get_image_size(_UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						output_size

    # determine new height and width
    lowerCamelCase__       : List[str]   					=						output_height / input_height
    lowerCamelCase__       : str   					=						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__       : Optional[int]   					=						scale_width
        else:
            # fit height
            lowerCamelCase__       : Tuple   					=						scale_height

    lowerCamelCase__       : str   					=						constraint_to_multiple_of(scale_height * input_height		,      multiple=_UpperCAmelCase					)
    lowerCamelCase__       : Any   					=						constraint_to_multiple_of(scale_width * input_width		,      multiple=_UpperCAmelCase					)

    return (new_height, new_width)


class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BILINEAR     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : Optional[int]     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						size if size is not None else {'height': 384, 'width': 384}
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						do_resize
     lowerCamelCase__       : List[str]   					=						size
     lowerCamelCase__       : List[Any]   					=						keep_aspect_ratio
     lowerCamelCase__       : Dict   					=						ensure_multiple_of
     lowerCamelCase__       : str   					=						resample
     lowerCamelCase__       : int   					=						do_rescale
     lowerCamelCase__       : Any   					=						rescale_factor
     lowerCamelCase__       : List[str]   					=						do_normalize
     lowerCamelCase__       : Tuple   					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCamelCase__       : str   					=						image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	A_      ( self    : int     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : bool = False     ,	UpperCAmelCase    : int = 1     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Optional[Any]     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase      )
     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__       : List[Any]   					=						get_resize_output_image_size(
         UpperCAmelCase     ,	output_size=(size['height'], size['width'])     ,	keep_aspect_ratio=UpperCAmelCase     ,	multiple=UpperCAmelCase     ,	)
     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      str:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : List[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Tuple     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Union[str, Any]     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : int   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : List[str]   					=						get_size_dict(UpperCAmelCase      )
     lowerCamelCase__       : int   					=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
     lowerCamelCase__       : Dict   					=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
     lowerCamelCase__       : List[str]   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Any   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : List[Any]   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : int   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Any   					=						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 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__       : Dict   					=						[to_numpy_array(UpperCAmelCase      ) for image in images]

     if do_resize:
         lowerCamelCase__       : Dict   					=						[self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase      ) for image in images]

     if do_rescale:
         lowerCamelCase__       : Tuple   					=						[self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase      ) for image in images]

     if do_normalize:
         lowerCamelCase__       : int   					=						[self.normalize(image=UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Optional[Any]   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Any   					=						{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )







 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : List[Tuple] = None      )     ->      Dict:
     lowerCamelCase__       : Tuple   					=						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      ):
             lowerCamelCase__       : List[str]   					=						target_sizes.numpy()

         lowerCamelCase__       : Tuple   					=						[]

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

     return semantic_segmentation





