

"""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   :

			"""simple docstring"""

			def __init__(							self     :					List[Any]						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					List[str]=sys.maxsize						):
								UpperCamelCase__		:    Any         ='''bilinear'''
								UpperCamelCase__		:    Tuple         =max_size
								UpperCamelCase__		:    List[str]         =short_edge_length






			def __call__(							self     :					Tuple						,  lowercase_     :					str						):
								UpperCamelCase__		:    Tuple         =[]
								for img in imgs:
													UpperCamelCase__  ,			UpperCamelCase__		:    str         =img.shape[:2]
													# later: provide list and randomly choose index for resize
													UpperCamelCase__		:    Optional[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(lowercase_						,  lowercase_						)
													if h < w:
																		UpperCamelCase__  ,			UpperCamelCase__		:    List[Any]         =size, scale * w
													else:
																		UpperCamelCase__  ,			UpperCamelCase__		:    Tuple         =scale * h, size
													if max(lowercase_						,  lowercase_						) > self.max_size:
																		UpperCamelCase__		:    List[Any]         =self.max_size * 1.0 / max(lowercase_						,  lowercase_						)
																		UpperCamelCase__		:    int         =newh * scale
																		UpperCamelCase__		:    Optional[Any]         =neww * scale
													UpperCamelCase__		:    Optional[Any]         =int(neww + 0.5						)
													UpperCamelCase__		:    Any         =int(newh + 0.5						)

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

								return img_augs



class     __a   :

			"""simple docstring"""

			def __init__(							self     :					Dict						,  lowercase_     :					List[Any]						):
								UpperCamelCase__		:    str         =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,  cfg.INPUT.MAX_SIZE_TEST						)
								UpperCamelCase__		:    Tuple         =cfg.INPUT.FORMAT
								UpperCamelCase__		:    Union[str, Any]         =cfg.SIZE_DIVISIBILITY
								UpperCamelCase__		:    str         =cfg.PAD_VALUE
								UpperCamelCase__		:    Optional[Any]         =cfg.INPUT.MAX_SIZE_TEST
								UpperCamelCase__		:    str         =cfg.MODEL.DEVICE
								UpperCamelCase__		:    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__		:    Any         =lambda lowercase_						: (x - self.pixel_mean) / self.pixel_std






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					List[str]						):
								UpperCamelCase__		:    Optional[int]         =tuple(max(lowercase_						) for s in zip(*[img.shape for img in images]						)						)
								UpperCamelCase__		:    Optional[int]         =[im.shape[-2:] for im in images]
								UpperCamelCase__		:    Tuple         =[
								    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     :					Optional[int]						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					int=False						):
								with torch.no_grad():
													if not isinstance(lowercase_						,  lowercase_						):
																		UpperCamelCase__		:    Union[str, Any]         =[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
													UpperCamelCase__		:    List[Any]         =torch.tensor([im.shape[:2] for im in images]						)
													UpperCamelCase__		:    Union[str, Any]         =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
													UpperCamelCase__		:    List[Any]         =[self.normalizer(lowercase_						) for x in images]
													# now pad them to do the following operations
													UpperCamelCase__  ,			UpperCamelCase__		:    Dict         =self.pad(lowercase_						)
													# Normalize

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



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[Any]      ,							UpperCAmelCase     :							Dict				):


					'''simple docstring'''






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







def        _lowerCAmelCase      (						UpperCAmelCase     :							int      ,							UpperCAmelCase     :							Tuple[int, int]				):


					'''simple docstring'''






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





"""simple docstring"""





import io
import json
import subprocess


_SCREAMING_SNAKE_CASE       :  List[Any]        = [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_SCREAMING_SNAKE_CASE       :  str        = 8



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							List[Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    Any         ={}
					for src, tgt in pairs:
										UpperCamelCase__		:    Optional[int]         =F'''{src}-{tgt}'''
										UpperCamelCase__		:    int         =F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    List[Any]         =F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    Any         ={'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
					return text


_SCREAMING_SNAKE_CASE       :  List[str]        = get_all_data(pairs, n_objs)
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
				_SCREAMING_SNAKE_CASE       :  Any        = json.dump(text, f, indent=2, ensure_ascii=False)


