


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 lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase		,      _UpperCAmelCase=sys.maxsize					):
       snake_case_       =      '''bilinear'''
       snake_case_       =      max_size
       snake_case_       =      short_edge_length





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

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

       return img_augs







class lowerCAmelCase_						:

 '''simple docstring'''

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



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase					):
       snake_case_       =      tuple(max(_UpperCAmelCase					) for s in zip(*[img.shape for img in images]					)					)
       snake_case_       =      [im.shape[-2:] for im in images]
       snake_case_       =      [
           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					):
       with torch.no_grad():
             if not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
                   snake_case_       =      [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
             snake_case_       =      torch.tensor([im.shape[:2] for im in images]					)
             snake_case_       =      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
             snake_case_       =      [self.normalizer(_UpperCAmelCase					) for x in images]
             # now pad them to do the following operations
             snake_case_       ,    snake_case_       =      self.pad(_UpperCAmelCase					)
             # Normalize

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






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

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






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

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



import argparse
import os


UpperCAmelCase			=    {
    """pytorch""": {
        """1.13""": {
            """torch""": """1.13.1""",
            """torchvision""": """0.14.1""",
            """torchaudio""": """0.13.1""",
            """python""": 3.9,
            """cuda""": """cu116""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu116"""
            ),
            """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.12""": {
            """torch""": """1.12.1""",
            """torchvision""": """0.13.1""",
            """torchaudio""": """0.12.1""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.11""": {
            """torch""": """1.11.0""",
            """torchvision""": """0.12.0""",
            """torchaudio""": """0.11.0""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.10""": {
            """torch""": """1.10.2""",
            """torchvision""": """0.11.3""",
            """torchaudio""": """0.10.2""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        """1.9""": {
            """torch""": """1.9.1""",
            """torchvision""": """0.10.1""",
            """torchaudio""": """0.9.1""",
            """python""": 3.9,
            """cuda""": """cu111""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu111"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
    """tensorflow""": {
        """2.11""": {
            """tensorflow""": """2.11.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.10""": {
            """tensorflow""": """2.10.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.9""": {
            """tensorflow""": """2.9.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.8""": {
            """tensorflow""": """2.8.2""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.7""": {
            """tensorflow""": """2.7.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.6""": {
            """tensorflow""": """2.6.5""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.5""": {
            """tensorflow""": """2.5.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
}


if __name__ == "__main__":
   UpperCAmelCase			=    argparse.ArgumentParser("""Choose the framework and version to install""")
   parser.add_argument(
       """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
   )
   parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
   UpperCAmelCase			=    parser.parse_args()

   UpperCAmelCase			=    past_versions_testing[args.framework][args.version]

   os.system(f'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')
   print(f'''echo "export INSTALL_CMD=\'{info['install']}\'" >> ~/.profile''')

   UpperCAmelCase			=    """"""
   if args.framework == "pytorch":
      UpperCAmelCase			=    info["""cuda"""]
   os.system(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')
   print(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')