

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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    np.ndarray	,     SCREAMING_SNAKE_CASE_		:    Union[int, Iterable[int]]	,     SCREAMING_SNAKE_CASE_		:    bool	,     SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     def constraint_to_multiple_of(SCREAMING_SNAKE_CASE_		:    Any	,     SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    str=0	,     SCREAMING_SNAKE_CASE_		:    List[Any]=None			):
          lowercase__    :   Union[str, Any]      =       round(val / multiple			) * multiple

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

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

          return x

     lowercase__    :   List[str]      =       (output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			) else output_size

     lowercase__      , lowercase__    :   List[Any]      =       get_image_size(SCREAMING_SNAKE_CASE_			)
     lowercase__      , lowercase__    :   Any      =       output_size

     # determine new height and width
     lowercase__    :   int      =       output_height / input_height
     lowercase__    :   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[Any]      =       scale_height

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

     return (new_height, new_width)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       List[Any]														=     ["""pixel_values"""]





    def __init__(      self						,   a = True						,   a = None						,   a = PILImageResampling.BILINEAR						,   a = False						,   a = 1						,   a = True						,   a = 1 / 255						,   a = True						,   a = None						,   a = None						,   **a						,   ):
         super().__init__(**a)
         lowercase__    :   Any      =       size if size is not None else {'height': 384, 'width': 384}
         lowercase__    :   List[str]      =       get_size_dict(a)
         lowercase__    :   int      =       do_resize
         lowercase__    :   Dict      =       size
         lowercase__    :   List[str]      =       keep_aspect_ratio
         lowercase__    :   Tuple      =       ensure_multiple_of
         lowercase__    :   Union[str, Any]      =       resample
         lowercase__    :   List[Any]      =       do_rescale
         lowercase__    :   Any      =       rescale_factor
         lowercase__    :   Union[str, Any]      =       do_normalize
         lowercase__    :   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       snake_case_				(      self						,   a						,   a						,   a = False						,   a = 1						,   a = PILImageResampling.BICUBIC						,   a = None						,   **a						,   ):
         lowercase__    :   Optional[int]      =       get_size_dict(a)
         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__    :   Optional[int]      =       get_resize_output_image_size(
             a						,   output_size=(size['height'], size['width'])						,   keep_aspect_ratio=a						,   multiple=a						,   )
         return resize(a						,   size=a						,   resample=a						,   data_format=a						,   **a)





    def       snake_case_				(      self						,   a						,   a						,   a = None						,   **a						,   ):
         return rescale(a						,   scale=a						,   data_format=a						,   **a)





    def       snake_case_				(      self						,   a						,   a						,   a						,   a = None						,   **a						,   ):
         return normalize(a						,   mean=a						,   std=a						,   data_format=a						,   **a)





    def       snake_case_				(      self						,   a						,   a = None						,   a = None						,   a = None						,   a = None						,   a = None						,   a = None						,   a = None						,   a = None						,   a = None						,   a = None						,   a = None						,   a = ChannelDimension.FIRST						,   **a						,   ):
         lowercase__    :   int      =       do_resize if do_resize is not None else self.do_resize
         lowercase__    :   Dict      =       size if size is not None else self.size
         lowercase__    :   Dict      =       get_size_dict(a)
         lowercase__    :   Optional[int]      =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         lowercase__    :   List[Any]      =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         lowercase__    :   Tuple      =       resample if resample is not None else self.resample
         lowercase__    :   Any      =       do_rescale if do_rescale is not None else self.do_rescale
         lowercase__    :   str      =       rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__    :   Optional[Any]      =       do_normalize if do_normalize is not None else self.do_normalize
         lowercase__    :   Optional[Any]      =       image_mean if image_mean is not None else self.image_mean
         lowercase__    :   Tuple      =       image_std if image_std is not None else self.image_std

         lowercase__    :   str      =       make_list_of_images(a)

         if not valid_images(a):
              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__    :   List[Any]      =       [to_numpy_array(a) for image in images]

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

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

         if do_normalize:
              lowercase__    :   List[Any]      =       [self.normalize(image=a						,   mean=a						,   std=a) for image in images]

         lowercase__    :   Optional[int]      =       [to_channel_dimension_format(a						,   a) for image in images]

         lowercase__    :   List[Any]      =       {'pixel_values': images}
         return BatchFeature(data=a						,   tensor_type=a)





    def       snake_case_				(      self						,   a						,   a = None):
         lowercase__    :   List[str]      =       outputs.logits

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

              if is_torch_tensor(a):
                   lowercase__    :   str      =       target_sizes.numpy()

              lowercase__    :   Optional[int]      =       []

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

         return semantic_segmentation




import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DPTImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

