

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						:     Union[str, Any]   ,A						:     Tuple   ,A						:     Tuple=sys.maxsize      ):






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




  def __call__(					self						:     Tuple   ,A						:     Optional[int]      ):






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

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

   return img_augs







class        __lowercase     :

  def __init__(					self						:     Optional[Any]   ,A						:     int      ):






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

  def 				__lowercase    (					self						:     Dict   ,A						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   tuple(max(__snake_case      ) for s in zip(*[img.shape for img in images]      )      )
   UpperCAmelCase__   :					Optional[Any]   				=   [im.shape[-2:] for im in images]
   UpperCAmelCase__   :					List[str]   				=   [
       nn.functional.pad(
           __snake_case   ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]   ,value=self.pad_value   ,)
       for size, im in zip(__snake_case   ,__snake_case      )
   ]

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




  def __call__(					self						:     Optional[int]   ,A						:     Optional[Any]   ,A						:     Any=False      ):






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

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







def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





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




def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase ):
 '''simple docstring'''





 assert torch.isfinite(_lowerCAmelCase ).all(), "Box tensor contains infinite or NaN!"
 UpperCAmelCase__   :					List[Any]   				=   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 )








"""simple docstring"""




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_reformer import ReformerTokenizer
else:
      __UpperCAmelCase	=			None


__UpperCAmelCase	=			logging.get_logger(__name__)


__UpperCAmelCase	=			'▁'

__UpperCAmelCase	=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase	=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

__UpperCAmelCase	=			{
    'google/reformer-crime-and-punishment': 52_4288,
}







class        __lowercase     (      __lowerCamelCase   ):
  snake_case_			    =    VOCAB_FILES_NAMES
  snake_case_			    =    PRETRAINED_VOCAB_FILES_MAP
  snake_case_			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_			    =    ["""input_ids""", """attention_mask"""]
  snake_case_			    =    ReformerTokenizer

  def __init__(					self						:     List[Any]   ,A						:     Dict=None   ,A						:     List[str]=None   ,A						:     Union[str, Any]="</s>"   ,A						:     List[str]="<unk>"   ,A						:     Tuple=[]   ,**A						:     Optional[int]   ,):






   '''simple docstring'''
   super().__init__(
       A   ,tokenizer_file=A   ,eos_token=A   ,unk_token=A   ,additional_special_tokens=A   ,**A   ,)

   UpperCAmelCase__   :					Tuple   				=   vocab_file
   UpperCAmelCase__   :					Optional[Any]   				=   False if not self.vocab_file else True




  def 				__lowercase    (					self						:     Any   ,A						:     str   ,A						:     Optional[str] = None      ):






   '''simple docstring'''
   if not self.can_save_slow_tokenizer:
    raise ValueError(
        """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
        """tokenizer."""      )

   if not os.path.isdir(A      ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
    return
   UpperCAmelCase__   :					Tuple   				=   os.path.join(
       A   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

   if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
    copyfile(self.vocab_file   ,A      )

   return (out_vocab_file,)





