



'''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 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=sys.maxsize	):
						"""simple docstring"""

						a        =     "bilinear"
						a        =     max_size
						a        =     short_edge_length




	def __call__(self	, lowerCamelCase_	):
						"""simple docstring"""

						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(lowerCamelCase_	, lowerCamelCase_	)
											if h < w:
																a  ,       a        =     size, scale * w
											else:
																a  ,       a        =     scale * h, size
											if max(lowerCamelCase_	, lowerCamelCase_	) > self.max_size:
																a        =     self.max_size * 1.0 / max(lowerCamelCase_	, lowerCamelCase_	)
																a        =     newh * scale
																a        =     neww * scale
											a        =     int(neww + 0.5	)
											a        =     int(newh + 0.5	)

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

						return img_augs




class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						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 lowerCamelCase_	: (x - self.pixel_mean) / self.pixel_std




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     tuple(max(lowerCamelCase_	) for s in zip(*[img.shape for img in images]	)	)
						a        =     [im.shape[-2:] for im in images]
						a        =     [
						    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        =     [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        =     torch.tensor([im.shape[:2] for im in images]	)
											a        =     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        =     [self.normalizer(lowerCamelCase_	) for x in images]
											# now pad them to do the following operations
											a  ,       a        =     self.pad(lowerCamelCase_	)
											# Normalize

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






def 					a(   A :					str				,  A :					Dict )	-> Optional[int]:
					"""simple docstring"""





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







def 					a(   A :					int				,  A :					Tuple[int, int] )	-> str:
					"""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 )





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
