'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A_	   = logging.get_logger(__name__)


if is_vision_available():
						import PIL




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[Any]   							=       ["""pixel_values"""]



			def __init__(							self				,			snake_case_ = True				,			snake_case_ = None				,			snake_case_ = PILImageResampling.BICUBIC				,			snake_case_ = True				,			snake_case_ = None				,			snake_case_ = True				,			snake_case_ = 1 / 255				,			snake_case_ = True				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = True				,			**snake_case_				,			):
									super().__init__(**snake_case_					)
									snake_case :       List[str]				=    size if size is not None else {"shortest_edge": 224}
									snake_case :       Dict				=    get_size_dict(snake_case_				,			default_to_square=snake_case_					)
									snake_case :       Any				=    crop_size if crop_size is not None else {"height": 224, "width": 224}
									snake_case :       Dict				=    get_size_dict(snake_case_					)

									snake_case :       Optional[Any]				=    do_resize
									snake_case :       Union[str, Any]				=    size
									snake_case :       Any				=    resample
									snake_case :       Any				=    do_center_crop
									snake_case :       Union[str, Any]				=    crop_size
									snake_case :       Union[str, Any]				=    do_rescale
									snake_case :       Tuple				=    rescale_factor
									snake_case :       int				=    do_normalize
									snake_case :       Dict				=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									snake_case :       Tuple				=    image_std if image_std is not None else OPENAI_CLIP_STD
									snake_case :       int				=    do_convert_rgb



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_ = PILImageResampling.BICUBIC				,			snake_case_ = None				,			**snake_case_				,			):
									snake_case :       str				=    get_size_dict(snake_case_				,			default_to_square=snake_case_					)
									snake_case :       Optional[int]				=    get_resize_output_image_size(
									    snake_case_				,			size=(size["height"], size["width"])				,			default_to_square=snake_case_					)
									return resize(snake_case_				,			size=snake_case_				,			resample=snake_case_				,			data_format=snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_ = None				,			**snake_case_				,			):
									snake_case :       Union[str, Any]				=    get_size_dict(snake_case_					)
									return center_crop(snake_case_				,			size=(size["height"], size["width"])				,			data_format=snake_case_				,			**snake_case_					)



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



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






			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = ChannelDimension.FIRST				,			**snake_case_				,			):
									snake_case :       Tuple				=    do_resize if do_resize is not None else self.do_resize
									snake_case :       str				=    size if size is not None else self.size
									snake_case :       int				=    get_size_dict(snake_case_				,			default_to_square=snake_case_					)
									snake_case :       str				=    resample if resample is not None else self.resample
									snake_case :       Optional[Any]				=    do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case :       List[Any]				=    crop_size if crop_size is not None else self.crop_size
									snake_case :       Any				=    get_size_dict(snake_case_					)
									snake_case :       List[str]				=    do_rescale if do_rescale is not None else self.do_rescale
									snake_case :       str				=    rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case :       Dict				=    do_normalize if do_normalize is not None else self.do_normalize
									snake_case :       List[str]				=    image_mean if image_mean is not None else self.image_mean
									snake_case :       List[str]				=    image_std if image_std is not None else self.image_std
									snake_case :       Optional[Any]				=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									snake_case :       List[Any]				=    make_list_of_images(snake_case_					)

									if not valid_images(snake_case_					):
															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."					)

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															snake_case :       Optional[Any]				=    [convert_to_rgb(snake_case_					) for image in images]

									# All transformations expect numpy arrays.
									snake_case :       List[str]				=    [to_numpy_array(snake_case_					) for image in images]

									if do_resize:
															snake_case :       List[str]				=    [self.resize(image=snake_case_				,			size=snake_case_				,			resample=snake_case_					) for image in images]

									if do_center_crop:
															snake_case :       List[Any]				=    [self.center_crop(image=snake_case_				,			size=snake_case_					) for image in images]

									if do_rescale:
															snake_case :       List[Any]				=    [self.rescale(image=snake_case_				,			scale=snake_case_					) for image in images]

									if do_normalize:
															snake_case :       Optional[Any]				=    [self.normalize(image=snake_case_				,			mean=snake_case_				,			std=snake_case_					) for image in images]

									snake_case :       str				=    [to_channel_dimension_format(snake_case_				,			snake_case_					) for image in images]

									snake_case :       Tuple				=    {"pixel_values": images}
									return BatchFeature(data=snake_case_				,			tensor_type=snake_case_					)






'''simple docstring'''

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 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






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

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

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






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



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

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






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# 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
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

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







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

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



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

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





