'''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 					__magic_name__		:







			def __init__(			self       :						str       ,    lowercase_       :						int       ,    lowercase_       :						Union[str, Any]=sys.maxsize					):
										lowercase_      :    Optional[int]       		= '''bilinear'''
										lowercase_      :    Optional[Any]       		= max_size
										lowercase_      :    List[Any]       		= short_edge_length







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

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

										return img_augs





class 					__magic_name__		:







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







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

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







			def __call__(			self       :						List[Any]       ,    lowercase_       :						str       ,    lowercase_       :						Union[str, Any]=False					):

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

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




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Union[str, Any]				)    ->   Optional[int]:
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes




def 							lowerCamelCase			(   UpperCAmelCase__ :      Dict   ,   UpperCAmelCase__ :      Tuple[int, int]				)    ->   Union[str, Any]:
							assert torch.isfinite(__A				).all(), "Box tensor contains infinite or NaN!"
							lowercase_      :    Optional[Any]       		= box_size
							tensor[:, 0].clamp_(min=0   ,   max=__A				)
							tensor[:, 1].clamp_(min=0   ,   max=__A				)
							tensor[:, 2].clamp_(min=0   ,   max=__A				)
							tensor[:, 3].clamp_(min=0   ,   max=__A				)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize("""dataset_size"""   ,   [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize("""input_in_memory_max_size"""   ,   ["""default""", 0, 100 * 2**20, 900 * 2**20]				)
def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							if input_in_memory_max_size != "default":
														monkeypatch.setattr(datasets.config   ,   """IN_MEMORY_MAX_SIZE"""   ,   UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= datasets.config.IN_MEMORY_MAX_SIZE
							if input_in_memory_max_size == "default":
														assert in_memory_max_size == 0
							else:
														assert in_memory_max_size == input_in_memory_max_size
							if dataset_size and in_memory_max_size:
														lowercase_      :    str       		= dataset_size < in_memory_max_size
							else:
														lowercase_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





