

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   lowercase_   :



						def __init__(      self				:    Optional[Any]      , A__				:    Any      , A__				:    Optional[Any]=sys.maxsize		)   ->					Optional[Any]:
								_snake_case						=							"""bilinear"""
								_snake_case						=							max_size
								_snake_case						=							short_edge_length







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

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

								return img_augs



class   lowercase_   :



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



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

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







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

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Tuple:



		"""simple docstring"""






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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     List[str]:



		"""simple docstring"""






		assert torch.isfinite(lowerCAmelCase__					).all(), "Box tensor contains infinite or NaN!"
		_snake_case						=							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__					)




from ..utils import DummyObject, requires_backends



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[int]										=    ["speech"]



      def __init__(      self				:    str      , *A__				:    List[str]      , **A__				:    Tuple		)   ->					Optional[Any]:
        requires_backends(self      , ['''speech''']		)



class   lowercase_   (  metaclass=__lowercase					):
      UpperCamelCase_      :		Optional[Any]										=    ["speech"]



      def __init__(      self				:    Dict      , *A__				:    int      , **A__				:    int		)   ->					Tuple:
        requires_backends(self      , ['''speech''']		)

