
"""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 :str      ,  _lowerCamelCase :str      ,  _lowerCamelCase :str=sys.maxsize   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	='bilinear'
      UpperCamelCase_     :					int   	=max_size
      UpperCamelCase_     :					str   	=short_edge_length




     def __call__(							self :Optional[int]      ,  _lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



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

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

      return img_augs





class 				a__					:



     def __init__(							self :str      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



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



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[str]   	=tuple(max(_lowerCamelCase   ) for s in zip(*[img.shape for img in images]   )   )
      UpperCamelCase_     :					List[Any]   	=[im.shape[-2:] for im in images]
      UpperCamelCase_     :					Dict   	=[
          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 :str      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[int]=False   ):







      '''simple docstring'''



      with torch.no_grad():
       if not isinstance(_lowerCamelCase      ,  _lowerCamelCase   ):
        UpperCamelCase_     :					List[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_     :					Union[str, Any]   	=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_     :					int   	=[self.normalizer(_lowerCamelCase   ) for x in images]
       # now pad them to do the following operations
       UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=self.pad(_lowerCamelCase   )
       # Normalize

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




def       A_  (				__lowercase						,	__lowercase	):
 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes





def       A_  (				__lowercase						,	__lowercase	):
 assert torch.isfinite(__lowercase	).all(), "Box tensor contains infinite or NaN!"
 UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=box_size
 tensor[:, 0].clamp_(min=0						,	max=__lowercase	)
 tensor[:, 1].clamp_(min=0						,	max=__lowercase	)
 tensor[:, 2].clamp_(min=0						,	max=__lowercase	)
 tensor[:, 3].clamp_(min=0						,	max=__lowercase	)


"""simple docstring"""






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							=     {
    'nielsr/canine-s': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
__SCREAMING_SNAKE_CASE							=     1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__SCREAMING_SNAKE_CASE							=     0

__SCREAMING_SNAKE_CASE							=     0XE_0_0_0
__SCREAMING_SNAKE_CASE							=     0XE_0_0_1
__SCREAMING_SNAKE_CASE							=     0XE_0_0_2
__SCREAMING_SNAKE_CASE							=     0XE_0_0_3
__SCREAMING_SNAKE_CASE							=     0XE_0_0_4

# Maps special codepoints to human-readable names.
__SCREAMING_SNAKE_CASE							=     {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
__SCREAMING_SNAKE_CASE							=     {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(							self :List[str]      ,  _lowerCamelCase :Optional[Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :List[str]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Any=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Optional[int]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Tuple=chr(_lowerCamelCase   )      ,  _lowerCamelCase :Union[str, Any]=chr(_lowerCamelCase   )      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :Union[str, Any]=2_048      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else bos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else eos_token
      UpperCamelCase_     :					List[str]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else sep_token
      UpperCamelCase_     :					Tuple   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else cls_token
      UpperCamelCase_     :					Optional[int]   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCamelCase_     :					Dict   	=AddedToken(_lowerCamelCase      ,  lstrip=_lowerCamelCase      ,  rstrip=_lowerCamelCase   ) if isinstance(_lowerCamelCase      ,  _lowerCamelCase   ) else mask_token

      super().__init__(
          bos_token=_lowerCamelCase      ,  eos_token=_lowerCamelCase      ,  sep_token=_lowerCamelCase      ,  cls_token=_lowerCamelCase      ,  pad_token=_lowerCamelCase      ,  mask_token=_lowerCamelCase      ,  add_prefix_space=_lowerCamelCase      ,  model_max_length=_lowerCamelCase      ,  **_lowerCamelCase      ,  )

      # Creates a mapping for looking up the IDs of special symbols.
      UpperCamelCase_     :					Dict[str, int]   	={}
      for codepoint, name in SPECIAL_CODEPOINTS.items():
       UpperCamelCase_     :					List[str]   	=codepoint

      # Creates a mapping for looking up the string forms of special symbol IDs.
      UpperCamelCase_     :					Dict[int, str]   	={
          codepoint: name for name, codepoint in self._special_codepoints.items()
      }

      UpperCamelCase_     :					str   	=UNICODE_VOCAB_SIZE
      UpperCamelCase_     :					Any   	=len(self._special_codepoints   )



     @property
     def  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      return self._unicode_vocab_size



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return list(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      try:
       return ord(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid token: \'{token}\''''   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :int   ):







      '''simple docstring'''



      try:
       if index in SPECIAL_CODEPOINTS:
        return SPECIAL_CODEPOINTS[index]
       return chr(_lowerCamelCase   )
      except TypeError:
       raise ValueError(f'''invalid id: {index}'''   )



     def  lowerCamelCase_						(							self :List[Any]      ,  _lowerCamelCase :str   ):







      '''simple docstring'''



      return "".join(_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=[self.sep_token_id]
      UpperCamelCase_     :					Tuple   	=[self.cls_token_id]

      UpperCamelCase_     :					List[str]   	=cls + token_ids_a + sep
      if token_ids_a is not None:
       result += token_ids_a + sep
      return result



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None      ,  _lowerCamelCase :bool = False   ):







      '''simple docstring'''



      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase      ,  token_ids_a=_lowerCamelCase      ,  already_has_special_tokens=_lowerCamelCase   )

      UpperCamelCase_     :					int   	=[1] + ([0] * len(_lowerCamelCase   )) + [1]
      if token_ids_a is not None:
       result += ([0] * len(_lowerCamelCase   )) + [1]
      return result



     def  lowerCamelCase_						(							self :List[str]      ,  _lowerCamelCase :List[int]      ,  _lowerCamelCase :Optional[List[int]] = None   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=[self.sep_token_id]
      UpperCamelCase_     :					Optional[int]   	=[self.cls_token_id]

      UpperCamelCase_     :					Dict   	=len(cls + token_ids_a + sep   ) * [0]
      if token_ids_a is not None:
       result += len(token_ids_a + sep   ) * [1]
      return result




     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :str      ,  _lowerCamelCase :Optional[str] = None   ):







      '''simple docstring'''



      return ()
