






'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser






class        __SCREAMING_SNAKE_CASE (     __UpperCamelCase			):




 @staticmethod
 @abstractmethod
 def        __magic_name__		( __lowercase		:							ArgumentParser )		->  Any:
       raise NotImplementedError()




 @abstractmethod
 def        __magic_name__		( self		:							str )		->  str:
       raise NotImplementedError()







'''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


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							["""pixel_values"""]




 def __init__( self		:							List[str]   , __lowercase		:							bool = True   , __lowercase		:							Optional[Dict[str, int]] = None   , __lowercase		:							PILImageResampling = PILImageResampling.BILINEAR   , __lowercase		:							bool = True   , __lowercase		:							Dict[str, int] = None   , __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		:							List[Any]   , )		->  None:
       super().__init__(**__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=size if size is not None else {'''shortest_edge''': 2_56}
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
       SCREAMING_SNAKE_CASE__       :			Tuple													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			int													=do_resize
       SCREAMING_SNAKE_CASE__       :			Dict													=size
       SCREAMING_SNAKE_CASE__       :			List[str]													=resample
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_center_crop
       SCREAMING_SNAKE_CASE__       :			str													=crop_size
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_rescale
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize
       SCREAMING_SNAKE_CASE__       :			Any													=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							PILImageResampling = PILImageResampling.BICUBIC   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Optional[Any]   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			str													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=get_resize_output_image_size(__lowercase   , size=size['''shortest_edge''']   , default_to_square=__lowercase )
       return resize(__lowercase   , size=__lowercase   , resample=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							int   , __lowercase		:							np.ndarray   , __lowercase		:							Dict[str, int]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Any   , )		->  np.ndarray:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=get_size_dict(__lowercase )
       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(__lowercase   , size=(size['''height'''], size['''width'''])   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							np.ndarray   , __lowercase		:							float   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							Tuple )		->  np.ndarray:
       return rescale(__lowercase   , scale=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Dict   , __lowercase		:							np.ndarray   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Union[float, List[float]]   , __lowercase		:							Optional[Union[str, ChannelDimension]] = None   , **__lowercase		:							str   , )		->  np.ndarray:
       return normalize(__lowercase   , mean=__lowercase   , std=__lowercase   , data_format=__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							List[Any]   , __lowercase		:							ImageInput   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							PILImageResampling = None   , __lowercase		:							bool = None   , __lowercase		:							Dict[str, int] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[float] = None   , __lowercase		:							Optional[bool] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[float, List[float]]] = None   , __lowercase		:							Optional[Union[str, TensorType]] = None   , __lowercase		:							Union[str, ChannelDimension] = ChannelDimension.FIRST   , **__lowercase		:							int   , )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__       :			int													=size if size is not None else self.size
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=get_size_dict(__lowercase   , default_to_square=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Any													=resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_center_crop if do_center_crop is not None else self.do_center_crop
       SCREAMING_SNAKE_CASE__       :			List[str]													=crop_size if crop_size is not None else self.crop_size
       SCREAMING_SNAKE_CASE__       :			int													=get_size_dict(__lowercase   , param_name='''crop_size''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__       :			int													=rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__       :			List[str]													=do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__       :			Any													=image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE__       :			str													=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:
             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.
       SCREAMING_SNAKE_CASE__       :			Dict													=[to_numpy_array(__lowercase ) for image in images]

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

       if do_center_crop:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=[self.center_crop(image=__lowercase   , size=__lowercase ) for image in images]

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

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

       SCREAMING_SNAKE_CASE__       :			str													=[to_channel_dimension_format(__lowercase   , __lowercase ) for image in images]

       SCREAMING_SNAKE_CASE__       :			Any													={'''pixel_values''': images}
       return BatchFeature(data=__lowercase   , tensor_type=__lowercase )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							Tuple   , __lowercase		:							List[Tuple] = None )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[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 ):
                   SCREAMING_SNAKE_CASE__       :			int													=target_sizes.numpy()

             SCREAMING_SNAKE_CASE__       :			List[Any]													=[]

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

       return semantic_segmentation