


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


_lowerCamelCase :    List[str]  							= logging.get_logger(__name__)


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ["pixel_values"]






							def __init__( self     :					int,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					PILImageResampling = PILImageResampling.BILINEAR,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Union[int, float] = 1 / 2_5_5,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					bool = True,			**_UpperCAmelCase     :					Optional[Any],			)				->			None:





														"""simple docstring"""
														super().__init__(**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						size if size is not None else {"shortest_edge": 2_2_4}
														SCREAMING_SNAKE_CASE__       :      int     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
														SCREAMING_SNAKE_CASE__       :      str     =						get_size_dict(_UpperCAmelCase,			param_name="crop_size"						)

														SCREAMING_SNAKE_CASE__       :      int     =						do_resize
														SCREAMING_SNAKE_CASE__       :      str     =						size
														SCREAMING_SNAKE_CASE__       :      Dict     =						resample
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						do_rescale
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						rescale_factor
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						do_center_crop
														SCREAMING_SNAKE_CASE__       :      Any     =						crop_size
														SCREAMING_SNAKE_CASE__       :      Dict     =						do_flip_channel_order






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Dict[str, int],			_UpperCAmelCase     :					PILImageResampling = PIL.Image.BILINEAR,			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					Any,			)				->			np.ndarray:





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






							def  A_     ( self     :					int,			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Dict[str, int],			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					List[str],			)				->			np.ndarray:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						get_size_dict(_UpperCAmelCase						)
														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(_UpperCAmelCase,			size=(size["height"], size["width"]),			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






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





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






							def  A_     ( self     :					int,			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None						)				->			np.ndarray:





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






							def  A_     ( self     :					int,			_UpperCAmelCase     :					ImageInput,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					PILImageResampling = None,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					float = None,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					Optional[Union[str, TensorType]] = None,			_UpperCAmelCase     :					ChannelDimension = ChannelDimension.FIRST,			**_UpperCAmelCase     :					int,			)				->			PIL.Image.Image:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						do_resize if do_resize is not None else self.do_resize
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						resample if resample is not None else self.resample
														SCREAMING_SNAKE_CASE__       :      int     =						do_rescale if do_rescale is not None else self.do_rescale
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						rescale_factor if rescale_factor is not None else self.rescale_factor
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						do_center_crop if do_center_crop is not None else self.do_center_crop
														SCREAMING_SNAKE_CASE__       :      List[str]     =						(
														    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
														)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						size if size is not None else self.size
														SCREAMING_SNAKE_CASE__       :      Any     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						crop_size if crop_size is not None else self.crop_size
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						get_size_dict(_UpperCAmelCase,			param_name="crop_size"						)

														SCREAMING_SNAKE_CASE__       :      Tuple     =						make_list_of_images(_UpperCAmelCase						)

														if not valid_images(_UpperCAmelCase						):
																					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.
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[to_numpy_array(_UpperCAmelCase						) for image in images]

														if do_resize:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						[self.resize(image=_UpperCAmelCase,			size=_UpperCAmelCase,			resample=_UpperCAmelCase						) for image in images]

														if do_center_crop:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						[self.center_crop(image=_UpperCAmelCase,			size=_UpperCAmelCase						) for image in images]

														if do_rescale:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						[self.rescale(image=_UpperCAmelCase,			scale=_UpperCAmelCase						) for image in images]

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

														SCREAMING_SNAKE_CASE__       :      int     =						[to_channel_dimension_format(_UpperCAmelCase,			_UpperCAmelCase						) for image in images]

														SCREAMING_SNAKE_CASE__       :      str     =						{"pixel_values": images}
														return BatchFeature(data=_UpperCAmelCase,			tensor_type=_UpperCAmelCase						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Tuple] = None						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						outputs.logits

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

																					if is_torch_tensor(_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      Any     =						target_sizes.numpy()

																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[]

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

														return semantic_segmentation













import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[Any]=sys.maxsize						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bilinear"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						short_edge_length






							def __call__( self     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[]
														for img in imgs:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						img.shape[:2]
																					# later: provide list and randomly choose index for resize
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						np.random.randint(self.short_edge_length[0],			self.short_edge_length[1] + 1						)
																					if size == 0:
																												return img
																					SCREAMING_SNAKE_CASE__       :      int     =						size * 1.0 / min(_UpperCAmelCase,			_UpperCAmelCase						)
																					if h < w:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						size, scale * w
																					else:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						scale * h, size
																					if max(_UpperCAmelCase,			_UpperCAmelCase						) > self.max_size:
																												SCREAMING_SNAKE_CASE__       :      str     =						self.max_size * 1.0 / max(_UpperCAmelCase,			_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Any     =						newh * scale
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						neww * scale
																					SCREAMING_SNAKE_CASE__       :      Any     =						int(neww + 0.5						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						int(newh + 0.5						)

																					if img.dtype == np.uinta:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						Image.fromarray(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pil_image.resize((neww, newh),			PILImageResampling.BILINEAR						)
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						np.asarray(_UpperCAmelCase						)
																					else:
																												SCREAMING_SNAKE_CASE__       :      str     =						img.permute(2,			0,			1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						nn.functional.interpolate(
																												    _UpperCAmelCase,			(newh, neww),			mode=self.interp_method,			align_corners=_UpperCAmelCase						).squeeze(0						)
																					img_augs.append(_UpperCAmelCase						)

														return img_augs


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],			cfg.INPUT.MAX_SIZE_TEST						)
														SCREAMING_SNAKE_CASE__       :      Any     =						cfg.INPUT.FORMAT
														SCREAMING_SNAKE_CASE__       :      List[str]     =						cfg.SIZE_DIVISIBILITY
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						cfg.PAD_VALUE
														SCREAMING_SNAKE_CASE__       :      Dict     =						cfg.INPUT.MAX_SIZE_TEST
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cfg.MODEL.DEVICE
														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						lambda _UpperCAmelCase						: (x - self.pixel_mean) / self.pixel_std






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tuple(max(_UpperCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[im.shape[-2:] for im in images]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    nn.functional.pad(
														        _UpperCAmelCase,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],			value=self.pad_value,			)
														    for size, im in zip(_UpperCAmelCase,			_UpperCAmelCase						)
														]

														return torch.stack(_UpperCAmelCase						), torch.tensor(_UpperCAmelCase						)






							def __call__( self     :					Any,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[str]=False						)				->			Optional[Any]:





														"""simple docstring"""
														with torch.no_grad():
																					if not isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      str     =						[images]
																					if single_image:
																												assert len(_UpperCAmelCase						) == 1
																					for i in range(len(_UpperCAmelCase						)						):
																												if isinstance(images[i],			torch.Tensor						):
																																			images.insert(_UpperCAmelCase,			images.pop(_UpperCAmelCase						).to(self.device						).float()						)
																												elif not isinstance(images[i],			torch.Tensor						):
																																			images.insert(
																																			    _UpperCAmelCase,			torch.as_tensor(img_tensorize(images.pop(_UpperCAmelCase						),			input_format=self.input_format						)						)
																																			    .to(self.device						)
																																			    .float(),			)
            # resize smallest edge
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor([im.shape[:2] for im in images]						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.aug(_UpperCAmelCase						)
																					# transpose images and convert to torch tensors
																					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																					# now normalize before pad to avoid useless arithmetic
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						[self.normalizer(_UpperCAmelCase						) for x in images]
																					# now pad them to do the following operations
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						self.pad(_UpperCAmelCase						)
																					# Normalize

																					if self.size_divisibility > 0:
																												raise NotImplementedError()
																					# pad
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.true_divide(_UpperCAmelCase,			_UpperCAmelCase						)
																					if single_image:
																												return images[0], sizes[0], scales_yx[0]
																					else:
																												return images, sizes, scales_yx




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Union[str, Any]:

							'''simple docstring'''






							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple[int, int]       )	->    List[Any]:

							'''simple docstring'''






							assert torch.isfinite(SCREAMING_SNAKE_CASE__       ).all(), "Box tensor contains infinite or NaN!"
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						box_size
							tensor[:, 0].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 1].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 2].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 3].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )






