


'''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 A__       :






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




             '''simple docstring'''
             _a    : Union[str, Any]     					='''bilinear'''
             _a    : Any     					=max_size
             _a    : str     					=short_edge_length






      def __call__( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:List[Any]	)       ->					Any:




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

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

             return img_augs







class A__       :






      def __init__( self			:int     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]	)       ->					Union[str, Any]:




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






      def __UpperCAmelCase       ( self			:List[Any]     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]	)       ->					List[Any]:




             '''simple docstring'''
             _a    : str     					=tuple(max(UpperCamelCase_	) for s in zip(*[img.shape for img in images]	)	)
             _a    : Optional[Any]     					=[im.shape[-2:] for im in images]
             _a    : Dict     					=[
                 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			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Any=False	)       ->					List[str]:




             '''simple docstring'''


             with torch.no_grad():
                    if not isinstance(UpperCamelCase_     ,						UpperCamelCase_	):
                           _a    : Union[str, Any]     					=[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
                    _a    : Tuple     					=torch.tensor([im.shape[:2] for im in images]	)
                    _a    : Optional[Any]     					=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
                    _a    : Tuple     					=[self.normalizer(UpperCamelCase_	) for x in images]
                    # now pad them to do the following operations
                    _a    : Dict     					=self.pad(UpperCamelCase_	)
                    # Normalize

                    if self.size_divisibility > 0:
                           raise NotImplementedError()
                    # pad
                    _a    : 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 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	str  )  ->			List[Any]:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[str]    ,_UpperCAmelCase	:	Optional[int]  )  ->			str:
       assert torch.isfinite(__a  ).all(), "Box tensor contains infinite or NaN!"
       _a    : Tuple     					=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'''
from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    features=UpperCamelCase_    ,   cache_dir=UpperCamelCase_    ,   keep_in_memory=UpperCamelCase_    ,   streaming=UpperCamelCase_    ,   num_proc=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Any												= Generator(
							    cache_dir=UpperCamelCase_    ,   features=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   gen_kwargs=UpperCamelCase_    ,   **UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.streaming:
												UpperCamelCase__   :Optional[Any]												= self.builder.as_streaming_dataset(split='''train'''							)
							# Build regular (map-style) dataset
							else:
												UpperCamelCase__   :Optional[int]												= None
												UpperCamelCase__   :int												= None
												UpperCamelCase__   :Any												= None
												UpperCamelCase__   :Any												= None

												self.builder.download_and_prepare(
												    download_config=UpperCamelCase_    ,   download_mode=UpperCamelCase_    ,   verification_mode=UpperCamelCase_    ,   base_path=UpperCamelCase_    ,   num_proc=self.num_proc    ,   )
												UpperCamelCase__   :List[Any]												= self.builder.as_dataset(
												    split='''train'''    ,   verification_mode=UpperCamelCase_    ,   in_memory=self.keep_in_memory							)
							return dataset