


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


A      = logging.get_logger(__name__)





def __UpperCAmelCase       (		__A ,						__A ,						__A ,						__A			)						->					Tuple[int, int]:



       '''simple docstring'''





       def constraint_to_multiple_of(__A ,						__A ,						__A=0 ,						__A=None			):
              UpperCAmelCase__										=     round(val / multiple			) * multiple

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

              if x < min_val:
                     UpperCAmelCase__										=     math.ceil(val / multiple			) * multiple

              return x

       UpperCAmelCase__										=     (output_size, output_size) if isinstance(__A ,						__A			) else output_size

       UpperCAmelCase__						,			UpperCAmelCase__										=     get_image_size(__A			)
       UpperCAmelCase__						,			UpperCAmelCase__										=     output_size

       # determine new height and width
       UpperCAmelCase__										=     output_height / input_height
       UpperCAmelCase__										=     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__										=     scale_width
              else:
                     # fit height
                     UpperCAmelCase__										=     scale_height

       UpperCAmelCase__										=     constraint_to_multiple_of(scale_height * input_height ,						multiple=__A			)
       UpperCAmelCase__										=     constraint_to_multiple_of(scale_width * input_width ,						multiple=__A			)

       return (new_height, new_width)




class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  ['pixel_values']






  def __init__(					self      :					List[str]		,						_lowercase      :					bool = True		,						_lowercase      :					Dict[str, int] = None		,						_lowercase      :					PILImageResampling = PILImageResampling.BILINEAR		,						_lowercase      :					bool = False		,						_lowercase      :					int = 1		,						_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      :					int		,						):




         """simple docstring"""







         super().__init__(**_lowercase )
         UpperCAmelCase__										=     size if size is not None else {"height": 3_84, "width": 3_84}
         UpperCAmelCase__										=     get_size_dict(_lowercase )
         UpperCAmelCase__										=     do_resize
         UpperCAmelCase__										=     size
         UpperCAmelCase__										=     keep_aspect_ratio
         UpperCAmelCase__										=     ensure_multiple_of
         UpperCAmelCase__										=     resample
         UpperCAmelCase__										=     do_rescale
         UpperCAmelCase__										=     rescale_factor
         UpperCAmelCase__										=     do_normalize
         UpperCAmelCase__										=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase__										=     image_std if image_std is not None else IMAGENET_STANDARD_STD






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					np.ndarray		,						_lowercase      :					Dict[str, int]		,						_lowercase      :					bool = False		,						_lowercase      :					int = 1		,						_lowercase      :					PILImageResampling = PILImageResampling.BICUBIC		,						_lowercase      :					Optional[Union[str, ChannelDimension]] = None		,						**_lowercase      :					Tuple		,						):




         """simple docstring"""







         UpperCAmelCase__										=     get_size_dict(_lowercase )
         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__										=     get_resize_output_image_size(
             _lowercase		,						output_size=(size["height"], size["width"])		,						keep_aspect_ratio=_lowercase		,						multiple=_lowercase		,						)
         return resize(_lowercase		,						size=_lowercase		,						resample=_lowercase		,						data_format=_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[int]		,						_lowercase      :					np.ndarray		,						_lowercase      :					Union[int, float]		,						_lowercase      :					Optional[Union[str, ChannelDimension]] = None		,						**_lowercase      :					Union[str, Any]		,						):




         """simple docstring"""







         return rescale(_lowercase		,						scale=_lowercase		,						data_format=_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					np.ndarray		,						_lowercase      :					Union[float, List[float]]		,						_lowercase      :					Union[float, List[float]]		,						_lowercase      :					Optional[Union[str, ChannelDimension]] = None		,						**_lowercase      :					Dict		,						):




         """simple docstring"""







         return normalize(_lowercase		,						mean=_lowercase		,						std=_lowercase		,						data_format=_lowercase		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					ImageInput		,						_lowercase      :					bool = None		,						_lowercase      :					int = None		,						_lowercase      :					bool = None		,						_lowercase      :					int = None		,						_lowercase      :					PILImageResampling = None		,						_lowercase      :					bool = None		,						_lowercase      :					float = None		,						_lowercase      :					bool = None		,						_lowercase      :					Optional[Union[float, List[float]]] = None		,						_lowercase      :					Optional[Union[float, List[float]]] = None		,						_lowercase      :					Optional[Union[str, TensorType]] = None		,						_lowercase      :					ChannelDimension = ChannelDimension.FIRST		,						**_lowercase      :					str		,						):




         """simple docstring"""







         UpperCAmelCase__										=     do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase__										=     size if size is not None else self.size
         UpperCAmelCase__										=     get_size_dict(_lowercase )
         UpperCAmelCase__										=     keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         UpperCAmelCase__										=     ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         UpperCAmelCase__										=     resample if resample is not None else self.resample
         UpperCAmelCase__										=     do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase__										=     rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase__										=     do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase__										=     image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase__										=     image_std if image_std is not None else self.image_std

         UpperCAmelCase__										=     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 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__										=     [to_numpy_array(_lowercase ) for image in images]

         if do_resize:
                UpperCAmelCase__										=     [self.resize(image=_lowercase		,						size=_lowercase		,						resample=_lowercase ) for image in images]

         if do_rescale:
                UpperCAmelCase__										=     [self.rescale(image=_lowercase		,						scale=_lowercase ) for image in images]

         if do_normalize:
                UpperCAmelCase__										=     [self.normalize(image=_lowercase		,						mean=_lowercase		,						std=_lowercase ) for image in images]

         UpperCAmelCase__										=     [to_channel_dimension_format(_lowercase		,						_lowercase ) for image in images]

         UpperCAmelCase__										=     {"pixel_values": images}
         return BatchFeature(data=_lowercase		,						tensor_type=_lowercase )






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					Dict		,						_lowercase      :					List[Tuple] = None ):




         """simple docstring"""







         UpperCAmelCase__										=     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 ):
                       UpperCAmelCase__										=     target_sizes.numpy()

                UpperCAmelCase__										=     []

                for idx in range(len(_lowercase ) ):
                       UpperCAmelCase__										=     torch.nn.functional.interpolate(
                           logits[idx].unsqueeze(dim=0 )		,						size=target_sizes[idx]		,						mode="bilinear"		,						align_corners=_lowercase )
                       UpperCAmelCase__										=     resized_logits[0].argmax(dim=0 )
                       semantic_segmentation.append(_lowercase )
         else:
                UpperCAmelCase__										=     logits.argmax(dim=1 )
                UpperCAmelCase__										=     [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


A      = logging.get_logger(__name__)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Union[str, Any]		,						*_lowercase      :					Any		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         warnings.warn(
             "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use DPTImageProcessor instead."		,						_lowercase		,						)
         super().__init__(*_lowercase		,						**_lowercase )





