"""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 							__snake_case:


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase=sys.maxsize	):




         '''simple docstring'''



         __A					:					Tuple								=		'bilinear'
         __A					:					Dict								=		max_size
         __A					:					Optional[Any]								=		short_edge_length

    def __call__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					int								=		[]
         for img in imgs:
              __A						,		__A					:					List[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					:					Any								=		size * 1.0 / min(__lowerCamelCase					,       __lowerCamelCase	)
              if h < w:
                   __A						,		__A					:					Any								=		size, scale * w
              else:
                   __A						,		__A					:					str								=		scale * h, size
              if max(__lowerCamelCase					,       __lowerCamelCase	) > self.max_size:
                   __A					:					Any								=		self.max_size * 1.0 / max(__lowerCamelCase					,       __lowerCamelCase	)
                   __A					:					List[Any]								=		newh * scale
                   __A					:					Optional[int]								=		neww * scale
              __A					:					int								=		int(neww + 0.5	)
              __A					:					int								=		int(newh + 0.5	)

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

         return img_augs







class 							__snake_case:


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



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




         '''simple docstring'''



         __A					:					Optional[Any]								=		tuple(max(__lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
         __A					:					Optional[int]								=		[im.shape[-2:] for im in images]
         __A					:					Optional[int]								=		[
             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	):




         '''simple docstring'''



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

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







def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				Dict						)    ->     Dict:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any  ,       _SCREAMING_SNAKE_CASE				:				Tuple[int, int]						)    ->     int:







     '''simple docstring'''


     assert torch.isfinite(_SCREAMING_SNAKE_CASE						).all(), "Box tensor contains infinite or NaN!"
     __A						,		__A					:					Dict								=		box_size
     tensor[:, 0].clamp_(min=0  ,       max=_SCREAMING_SNAKE_CASE						)
     tensor[:, 1].clamp_(min=0  ,       max=_SCREAMING_SNAKE_CASE						)
     tensor[:, 2].clamp_(min=0  ,       max=_SCREAMING_SNAKE_CASE						)
     tensor[:, 3].clamp_(min=0  ,       max=_SCREAMING_SNAKE_CASE						)




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      : Dict       ={'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[str]       =[
           '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''SEWDForCTC''',
           '''SEWDForSequenceClassification''',
           '''SEWDModel''',
           '''SEWDPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase      : str       =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



