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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
     import PIL

if is_torch_available():
     import torch


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['pixel_values']



     def __init__(					self				,				A_ = True				,				A_ = None				,				A_ = PILImageResampling.BILINEAR				,				A_ = True				,				A_ = 1 / 2_55				,				A_ = True				,				A_ = None				,				A_ = True				,				**A_				,				) ->				None:






           """simple docstring"""
           super().__init__(**A_						)
           _lowerCamelCase			    =				size if size is not None else {'''shortest_edge''': 2_24}
           _lowerCamelCase			    =				get_size_dict(A_				,				default_to_square=A_						)
           _lowerCamelCase			    =				crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
           _lowerCamelCase			    =				get_size_dict(A_				,				param_name='''crop_size'''						)

           _lowerCamelCase			    =				do_resize
           _lowerCamelCase			    =				size
           _lowerCamelCase			    =				resample
           _lowerCamelCase			    =				do_rescale
           _lowerCamelCase			    =				rescale_factor
           _lowerCamelCase			    =				do_center_crop
           _lowerCamelCase			    =				crop_size
           _lowerCamelCase			    =				do_flip_channel_order



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = PIL.Image.BILINEAR				,				A_ = None				,				**A_				,				) ->				np.ndarray:






           """simple docstring"""
           _lowerCamelCase			    =				get_size_dict(A_				,				default_to_square=A_						)
           if "shortest_edge" not in size:
                 raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'						)
           _lowerCamelCase			    =				get_resize_output_image_size(A_				,				size=size['''shortest_edge''']				,				default_to_square=A_						)
           return resize(A_				,				size=A_				,				resample=A_				,				data_format=A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = None				,				**A_				,				) ->				np.ndarray:






           """simple docstring"""
           _lowerCamelCase			    =				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()}'						)
           return center_crop(A_				,				size=(size['''height'''], size['''width'''])				,				data_format=A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_ = None				,				**A_				,				) ->				Union[str, Any]:






           """simple docstring"""
           return rescale(A_				,				scale=A_				,				data_format=A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				np.ndarray:






           """simple docstring"""
           return flip_channel_order(A_				,				data_format=A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = ChannelDimension.FIRST				,				**A_				,				) ->				PIL.Image.Image:






           """simple docstring"""
           _lowerCamelCase			    =				do_resize if do_resize is not None else self.do_resize
           _lowerCamelCase			    =				resample if resample is not None else self.resample
           _lowerCamelCase			    =				do_rescale if do_rescale is not None else self.do_rescale
           _lowerCamelCase			    =				rescale_factor if rescale_factor is not None else self.rescale_factor
           _lowerCamelCase			    =				do_center_crop if do_center_crop is not None else self.do_center_crop
           _lowerCamelCase			    =				(
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

           _lowerCamelCase			    =				size if size is not None else self.size
           _lowerCamelCase			    =				get_size_dict(A_				,				default_to_square=A_						)
           _lowerCamelCase			    =				crop_size if crop_size is not None else self.crop_size
           _lowerCamelCase			    =				get_size_dict(A_				,				param_name='''crop_size'''						)

           _lowerCamelCase			    =				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:
                 raise ValueError('''Size 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_center_crop and crop_size is None:
                 raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

           # All transformations expect numpy arrays.
           _lowerCamelCase			    =				[to_numpy_array(A_						) for image in images]

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

           if do_center_crop:
                 _lowerCamelCase			    =				[self.center_crop(image=A_				,				size=A_						) for image in images]

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

           # the pretrained checkpoints assume images are BGR, not RGB
           if do_flip_channel_order:
                 _lowerCamelCase			    =				[self.flip_channel_order(image=A_						) for image in images]

           _lowerCamelCase			    =				[to_channel_dimension_format(A_				,				A_						) for image in images]

           _lowerCamelCase			    =				{'''pixel_values''': images}
           return BatchFeature(data=A_				,				tensor_type=A_						)





     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				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_						):
                       _lowerCamelCase			    =				target_sizes.numpy()

                 _lowerCamelCase			    =				[]

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

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           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_						)