




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]      ,		lowercase__   :							Dict      ,		lowercase__   :							Union[str, Any]=sys.maxsize       ):
       a_		    =  '''bilinear'''
       a_		    =  max_size
       a_		    =  short_edge_length



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

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

       return img_augs



class 							__lowercase   :



     def __init__(  self   :							Dict      ,		lowercase__   :							Dict       ):
       a_		    =  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,		cfg.INPUT.MAX_SIZE_TEST       )
       a_		    =  cfg.INPUT.FORMAT
       a_		    =  cfg.SIZE_DIVISIBILITY
       a_		    =  cfg.PAD_VALUE
       a_		    =  cfg.INPUT.MAX_SIZE_TEST
       a_		    =  cfg.MODEL.DEVICE
       a_		    =  torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )      ,		1      ,		1       )
       a_		    =  torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       )      ,		1      ,		1       )
       a_		    =  lambda lowercase__       : (x - self.pixel_mean) / self.pixel_std



     def 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							List[str]       ):
       a_		    =  tuple(max(lowercase__       ) for s in zip(*[img.shape for img in images]       )       )
       a_		    =  [im.shape[-2:] for im in images]
       a_		    =  [
           nn.functional.pad(
               lowercase__      ,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,		value=self.pad_value      ,		)
           for size, im in zip(lowercase__      ,		lowercase__       )
       ]

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



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

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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  assert torch.isfinite(_A			).all(), "Box tensor contains infinite or NaN!"
  a_    , a_		    =  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			)






import io
import json
import subprocess


UpperCamelCase__       				=	[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase__       				=	8






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  {}
  for src, tgt in pairs:
    a_		    =  f"{src}-{tgt}"
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo src".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
  return text


UpperCamelCase__       				=	get_all_data(pairs, n_objs)
UpperCamelCase__       				=	'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
  UpperCamelCase__       				=	json.dump(text, f, indent=2, ensure_ascii=False)
