




'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


UpperCamelCase_		:    Optional[Any]									=		logging.get_logger(__name__)

class      _a       (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		["""pixel_values"""]






     def __init__(				self      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = 1 / 255      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    None:
          super().__init__(**_SCREAMING_SNAKE_CASE					)
          _snake_case							=   crop_size if crop_size is not None else {"height": 256, "width": 256}
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE      ,param_name="crop_size"					)
          _snake_case							=   size if size is not None else {"height": 224, "width": 224}
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   do_center_crop
          _snake_case							=   crop_size
          _snake_case							=   do_resize
          _snake_case							=   size
          _snake_case							=   resample
          _snake_case							=   do_rescale
          _snake_case							=   rescale_factor
          _snake_case							=   do_normalize
          _snake_case							=   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          _snake_case							=   image_std if image_std is not None else IMAGENET_DEFAULT_STD






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          _snake_case							=   self.size if size is None else size
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE      ,param_name="crop_size"					)

          _snake_case							,      _snake_case							=   get_image_size(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   min(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   (size["height"] / crop_size["height"]) * min_dim
          _snake_case							=   (size["width"] / crop_size["width"]) * min_dim
          return center_crop(_SCREAMING_SNAKE_CASE      ,size=(cropped_height, cropped_width)      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)
          if "height" not in size or "width" not in size:
               raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""					)
          return resize(
              _SCREAMING_SNAKE_CASE      ,size=(size["height"], size["width"])      ,resample=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    Dict:
          return rescale(_SCREAMING_SNAKE_CASE      ,scale=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          return normalize(_SCREAMING_SNAKE_CASE      ,mean=_SCREAMING_SNAKE_CASE      ,std=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,**_SCREAMING_SNAKE_CASE      ,)							->    PIL.Image.Image:
          _snake_case							=   do_center_crop if do_center_crop is not None else self.do_center_crop
          _snake_case							=   crop_size if crop_size is not None else self.crop_size
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE      ,param_name="crop_size"					)
          _snake_case							=   do_resize if do_resize is not None else self.do_resize
          _snake_case							=   size if size is not None else self.size
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   resample if resample is not None else self.resample
          _snake_case							=   do_rescale if do_rescale is not None else self.do_rescale
          _snake_case							=   rescale_factor if rescale_factor is not None else self.rescale_factor
          _snake_case							=   do_normalize if do_normalize is not None else self.do_normalize
          _snake_case							=   image_mean if image_mean is not None else self.image_mean
          _snake_case							=   image_std if image_std is not None else self.image_std

          _snake_case							=   make_list_of_images(_SCREAMING_SNAKE_CASE					)

          if not valid_images(_SCREAMING_SNAKE_CASE					):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."					)

          if do_center_crop and crop_size is None:
               raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."					)

          if do_resize and size is None:
               raise ValueError("Size must be specified if do_resize is True."					)

          if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."					)

          if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and image standard deviation must be specified if do_normalize is True."					)

          # All transformations expect numpy arrays.
          _snake_case							=   [to_numpy_array(_SCREAMING_SNAKE_CASE					) for image in images]

          if do_center_crop:
               _snake_case							=   [self.center_crop(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,size=_SCREAMING_SNAKE_CASE					) for image in images]

          if do_resize:
               _snake_case							=   [self.resize(image=_SCREAMING_SNAKE_CASE      ,size=_SCREAMING_SNAKE_CASE      ,resample=_SCREAMING_SNAKE_CASE					) for image in images]

          if do_rescale:
               _snake_case							=   [self.rescale(image=_SCREAMING_SNAKE_CASE      ,scale=_SCREAMING_SNAKE_CASE					) for image in images]

          if do_normalize:
               _snake_case							=   [self.normalize(image=_SCREAMING_SNAKE_CASE      ,mean=_SCREAMING_SNAKE_CASE      ,std=_SCREAMING_SNAKE_CASE					) for image in images]

          _snake_case							=   [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					) for image in images]

          _snake_case							=   {"pixel_values": images}
          return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,tensor_type=_SCREAMING_SNAKE_CASE					)








'''simple docstring'''





import argparse
import os


UpperCamelCase_		:    Union[str, Any]									=		{
    '''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_		:    Optional[Any]									=		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_		:    Tuple									=		parser.parse_args()

   UpperCamelCase_		:    Optional[int]									=		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_		:    List[Any]									=		''''''
   if args.framework == "pytorch":
      UpperCamelCase_		:    str									=		info['''cuda''']
   os.system(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')
   print(F'echo \"export CUDA=\'{cuda}\'\" >> ~/.profile')


