'''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 					_lowercase						:
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=sys.maxsize				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	'''bilinear'''
         UpperCAmelCase__				:					Optional[Any] 					=	max_size
         UpperCAmelCase__				:					List[str] 					=	short_edge_length




      def __call__(					self       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



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

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

         return img_augs




class 					_lowercase						:
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



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

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




      def __call__(					self       ,lowerCamelCase_       ,lowerCamelCase_=False				)      ->      int:



         '''simple docstring'''



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

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







def __UpperCamelCase( _A       :					List[str]	,      _A       :					Optional[Any]     ):
   '''simple docstring'''
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes







def __UpperCamelCase( _A       :					Optional[int]	,      _A       :					Tuple[int, int]     ):
   '''simple docstring'''
   assert torch.isfinite(_A     ).all(), "Box tensor contains infinite or NaN!"
   UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate


@dataclass(frozen=lowerCAmelCase )
class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= field(default='''question-answering-extractive'''		,metadata={'''include_in_asdict_even_if_is_default''': True} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
      UpperCAmelCase_   :   ClassVar[Features]   				= Features(
          {
              '''answers''': Sequence(
                  {
                      '''text''': Value('''string''' ),
                      '''answer_start''': Value('''int32''' ),
                  } )
          } )
      UpperCAmelCase_   :   str   				= "question"
      UpperCAmelCase_   :   str   				= "context"
      UpperCAmelCase_   :   str   				= "answers"




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Dict[str, str]:



         '''simple docstring'''



         return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}




