



'''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      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  Optional[int]   ,		__A		:  Tuple   ,		__A		:  Union[str, Any]=sys.maxsize					)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   """bilinear"""
								lowerCAmelCase__  =   max_size
								lowerCAmelCase__  =   short_edge_length




	def __call__(  self		:  Any   ,		__A		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





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

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

								return img_augs





class      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  str   ,		__A		:  Union[str, Any]					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]   ,		cfg.INPUT.MAX_SIZE_TEST					)
								lowerCAmelCase__  =   cfg.INPUT.FORMAT
								lowerCAmelCase__  =   cfg.SIZE_DIVISIBILITY
								lowerCAmelCase__  =   cfg.PAD_VALUE
								lowerCAmelCase__  =   cfg.INPUT.MAX_SIZE_TEST
								lowerCAmelCase__  =   cfg.MODEL.DEVICE
								lowerCAmelCase__  =   torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)   ,		1   ,		1					)
								lowerCAmelCase__  =   torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)   ,		1   ,		1					)
								lowerCAmelCase__  =   lambda __A					: (x - self.pixel_mean) / self.pixel_std




	def      lowercase__							(  self		:  Any   ,		__A		:  List[Any]					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   tuple(max(__A					) for s in zip(*[img.shape for img in images]					)					)
								lowerCAmelCase__  =   [im.shape[-2:] for im in images]
								lowerCAmelCase__  =   [
								    nn.functional.pad(
								        __A   ,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]   ,		value=self.pad_value   ,		)
								    for size, im in zip(__A   ,		__A					)
								]

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




	def __call__(  self		:  Any   ,		__A		:  Optional[int]   ,		__A		:  List[Any]=False					)     ->			Optional[Any]:




								'''simple docstring'''





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

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





def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	List[Any]						) ->							str:
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes






def 			_lowerCAmelCase(      UpperCAmelCase_      :	Any					, UpperCAmelCase_      :	Tuple[int, int]						) ->							List[Any]:
							assert torch.isfinite(UpperCAmelCase_						).all(), "Box tensor contains infinite or NaN!"
							lowerCAmelCase__  ,lowerCAmelCase__  =   box_size
							tensor[:, 0].clamp_(min=0					, max=UpperCAmelCase_						)
							tensor[:, 1].clamp_(min=0					, max=UpperCAmelCase_						)
							tensor[:, 2].clamp_(min=0					, max=UpperCAmelCase_						)
							tensor[:, 3].clamp_(min=0					, max=UpperCAmelCase_						)










'''simple docstring'''







from ..utils import DummyObject, requires_backends

class      lowerCamelCase__     (     metaclass=_A				):






	'''simple docstring'''

	A__		=	['''torch''', '''librosa''']




	def __init__(  self		:  Optional[Any]   ,		*__A		:  Tuple   ,		**__A		:  Tuple					)     ->			Union[str, Any]:




								'''simple docstring'''





								requires_backends(self   ,		["""torch""", """librosa"""]					)




	@classmethod
	def      lowercase__							(  cls		:  Dict   ,		*__A		:  List[Any]   ,		**__A		:  List[str]					)     ->			Union[str, Any]:




								'''simple docstring'''





								requires_backends(cls   ,		["""torch""", """librosa"""]					)




	@classmethod
	def      lowercase__							(  cls		:  Optional[int]   ,		*__A		:  List[str]   ,		**__A		:  Tuple					)     ->			Any:




								'''simple docstring'''





								requires_backends(cls   ,		["""torch""", """librosa"""]					)





class      lowerCamelCase__     (     metaclass=_A				):






	'''simple docstring'''

	A__		=	['''torch''', '''librosa''']




	def __init__(  self		:  int   ,		*__A		:  Dict   ,		**__A		:  List[Any]					)     ->			List[Any]:




								'''simple docstring'''





								requires_backends(self   ,		["""torch""", """librosa"""]					)




	@classmethod
	def      lowercase__							(  cls		:  Optional[int]   ,		*__A		:  Optional[int]   ,		**__A		:  List[Any]					)     ->			Union[str, Any]:




								'''simple docstring'''





								requires_backends(cls   ,		["""torch""", """librosa"""]					)




	@classmethod
	def      lowercase__							(  cls		:  List[str]   ,		*__A		:  List[str]   ,		**__A		:  int					)     ->			List[str]:




								'''simple docstring'''





								requires_backends(cls   ,		["""torch""", """librosa"""]					)





