





'''simple docstring'''




import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str					,				**__snake_case    :					List[Any]     )	->	Any:
     __A		:					Optional[Any]						=			AutoConfig.from_pretrained(__snake_case					,				**__snake_case     )
     __A		:					int						=			AutoModelForSeqaSeqLM.from_config(__snake_case     )
     model.save_pretrained(__snake_case     )
     AutoTokenizer.from_pretrained(__snake_case     ).save_pretrained(__snake_case     )
     return model


if __name__ == "__main__":
  fire.Fire(save_randomly_initialized_version)






'''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 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=sys.maxsize):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			'bilinear'
          __A		:					int						=			max_size
          __A		:					Optional[Any]						=			short_edge_length


     def __call__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






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

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

          return img_augs




class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[Any]						=			tuple(max(_UpperCAmelCase) for s in zip(*[img.shape for img in images]))
          __A		:					Dict						=			[im.shape[-2:] for im in images]
          __A		:					Optional[int]						=			[
              nn.functional.pad(
                  _UpperCAmelCase		,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,      value=self.pad_value		,      )
              for size, im in zip(_UpperCAmelCase		,      _UpperCAmelCase)
          ]

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


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






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

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






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					str     )	->	Dict:
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes







def 			_lowerCAmelCase     (    __snake_case    :					List[str]					,				__snake_case    :					Tuple[int, int]     )	->	int:
     assert torch.isfinite(__snake_case     ).all(), "Box tensor contains infinite or NaN!"
     __A					,__A		:					int						=			box_size
     tensor[:, 0].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 1].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 2].clamp_(min=0					,				max=__snake_case     )
     tensor[:, 3].clamp_(min=0					,				max=__snake_case     )