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


_UpperCAmelCase            =		logging.get_logger(__name__)





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''pixel_values''']
				def __init__(						self		,     lowercase = True		,     lowercase = None		,     lowercase = PILImageResampling.BILINEAR		,     lowercase = True		,     lowercase = None		,     lowercase = True		,     lowercase = 1 / 2_5_5		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(**lowercase )
						A_		:    Optional[int]         =				size if size is not None else {'shortest_edge': 2_5_6}
						A_		:    List[Any]         =				get_size_dict(lowercase		,     default_to_square=lowercase )
						A_		:    Dict         =				crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A_		:    List[Any]         =				get_size_dict(lowercase		,     param_name='crop_size' )
						A_		:    Optional[int]         =				do_resize
						A_		:    Any         =				size
						A_		:    Union[str, Any]         =				resample
						A_		:    Optional[Any]         =				do_center_crop
						A_		:    Tuple         =				crop_size
						A_		:    List[Any]         =				do_rescale
						A_		:    List[str]         =				rescale_factor
						A_		:    Optional[Any]         =				do_normalize
						A_		:    List[str]         =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A_		:    Optional[Any]         =				image_std if image_std is not None else IMAGENET_STANDARD_STD
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = PILImageResampling.BICUBIC		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				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()}''' )
						A_		:    List[str]         =				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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Dict         =				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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase ):



						"""simple docstring"""





						return rescale(lowercase		,     scale=lowercase		,     data_format=lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						return normalize(lowercase		,     mean=lowercase		,     std=lowercase		,     data_format=lowercase		,     **lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = ChannelDimension.FIRST		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[str]         =				do_resize if do_resize is not None else self.do_resize
						A_		:    Optional[int]         =				size if size is not None else self.size
						A_		:    int         =				get_size_dict(lowercase		,     default_to_square=lowercase )
						A_		:    Tuple         =				resample if resample is not None else self.resample
						A_		:    Union[str, Any]         =				do_center_crop if do_center_crop is not None else self.do_center_crop
						A_		:    int         =				crop_size if crop_size is not None else self.crop_size
						A_		:    Tuple         =				get_size_dict(lowercase		,     param_name='crop_size' )
						A_		:    int         =				do_rescale if do_rescale is not None else self.do_rescale
						A_		:    int         =				rescale_factor if rescale_factor is not None else self.rescale_factor
						A_		:    Union[str, Any]         =				do_normalize if do_normalize is not None else self.do_normalize
						A_		:    int         =				image_mean if image_mean is not None else self.image_mean
						A_		:    Optional[Any]         =				image_std if image_std is not None else self.image_std

						A_		:    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.
						A_		:    Tuple         =				[to_numpy_array(lowercase ) for image in images]

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

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

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

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

						A_		:    str         =				[to_channel_dimension_format(lowercase		,     lowercase ) for image in images]

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







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Tuple         =				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 ):
										A_		:    int         =				target_sizes.numpy()

								A_		:    str         =				[]

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

						return semantic_segmentation







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






