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


__snake_case						=			logging.get_logger(__name__)





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     Tuple[int, int]:






      '''simple docstring'''





      def constraint_to_multiple_of(__lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase=0						,    __lowerCAmelCase=None  ):
            UpperCAmelCase			:       List[Any]			     =round(val / multiple  ) * multiple

            if max_val is not None and x > max_val:
                  UpperCAmelCase			:       Tuple			     =math.floor(val / multiple  ) * multiple

            if x < min_val:
                  UpperCAmelCase			:       Union[str, Any]			     =math.ceil(val / multiple  ) * multiple

            return x

      UpperCAmelCase			:       Optional[int]			     =(output_size, output_size) if isinstance(_UpperCamelCase						,    _UpperCamelCase  ) else output_size

      UpperCAmelCase			:       List[str]			     =get_image_size(_UpperCamelCase  )
      UpperCAmelCase			:       Dict			     =output_size

      # determine new height and width
      UpperCAmelCase			:       Any			     =output_height / input_height
      UpperCAmelCase			:       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
                  UpperCAmelCase			:       str			     =scale_width
            else:
                  # fit height
                  UpperCAmelCase			:       int			     =scale_height

      UpperCAmelCase			:       Any			     =constraint_to_multiple_of(scale_height * input_height						,    multiple=_UpperCamelCase  )
      UpperCAmelCase			:       str			     =constraint_to_multiple_of(scale_width * input_width						,    multiple=_UpperCamelCase  )

      return (new_height, new_width)







class 	__snake_case      (   _a      ):
 __lowerCamelCase			:      Any           =					['''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__						,      )		->							None:
       '''simple docstring'''





       super().__init__(**snake_case__  )
       UpperCAmelCase			:       List[Any]			     =size if size is not None else {'''height''': 384, '''width''': 384}
       UpperCAmelCase			:       List[str]			     =get_size_dict(snake_case__  )
       UpperCAmelCase			:       Dict			     =do_resize
       UpperCAmelCase			:       Optional[int]			     =size
       UpperCAmelCase			:       int			     =keep_aspect_ratio
       UpperCAmelCase			:       Optional[Any]			     =ensure_multiple_of
       UpperCAmelCase			:       str			     =resample
       UpperCAmelCase			:       Union[str, Any]			     =do_rescale
       UpperCAmelCase			:       List[Any]			     =rescale_factor
       UpperCAmelCase			:       List[str]			     =do_normalize
       UpperCAmelCase			:       Optional[int]			     =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase			:       Optional[int]			     =image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__						,      snake_case__ = False						,      snake_case__ = 1						,      snake_case__ = PILImageResampling.BICUBIC						,      snake_case__ = None						,      **snake_case__						,      )		->							np.ndarray:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =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()}'''  )
       UpperCAmelCase			:       int			     =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 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__						,      snake_case__ = None						,      **snake_case__						,      )		->							Optional[Any]:
       '''simple docstring'''





       return rescale(snake_case__						,      scale=snake_case__						,      data_format=snake_case__						,      **snake_case__  )
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__						,      snake_case__						,      snake_case__ = None						,      **snake_case__						,      )		->							np.ndarray:
       '''simple docstring'''





       return normalize(snake_case__						,      mean=snake_case__						,      std=snake_case__						,      data_format=snake_case__						,      **snake_case__  )
 def 		UpperCAmelCase__				(     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__						,      )		->							PIL.Image.Image:
       '''simple docstring'''





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

       UpperCAmelCase			:       int			     =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.
       UpperCAmelCase			:       Optional[int]			     =[to_numpy_array(snake_case__  ) for image in images]

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

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

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

       UpperCAmelCase			:       List[str]			     =[to_channel_dimension_format(snake_case__						,      snake_case__  ) for image in images]

       UpperCAmelCase			:       Dict			     ={'''pixel_values''': images}
       return BatchFeature(data=snake_case__						,      tensor_type=snake_case__  )






 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__ = None  )		->							Any:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =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__  ):
                   UpperCAmelCase			:       List[Any]			     =target_sizes.numpy()

             UpperCAmelCase			:       Union[str, Any]			     =[]

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

       return semantic_segmentation







lowerCAmelCase_    =				{
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355_818,
}






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						float:






				"""simple docstring"""


				if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
								snake_case_						:    str				=  (
								    f'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
								    f'''Valid values are: {", ".join(_UpperCamelCase )}'''
								)
								raise ValueError(_UpperCamelCase )
				return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
