





'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case_  :							Any            =	logging.get_logger(__name__)


if is_vision_available():
					import PIL


class        __a   (lowerCamelCase  ):
							__a       : Any          =							["pixel_values"]

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  bool = True							,  __magic_name__				:  Union[int, float] = 1 / 2_55							,  __magic_name__				:  bool = True							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = True							,  **__magic_name__				:  Union[str, Any]							,  )       ->							None:







													"""simple docstring"""
													super().__init__(**__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	size if size is not None else {'''shortest_edge''': 2_24}
													UpperCAmelCase_      :   List[str]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   int      =	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__ )

													UpperCAmelCase_      :   str      =	do_resize
													UpperCAmelCase_      :   Optional[Any]      =	size
													UpperCAmelCase_      :   int      =	resample
													UpperCAmelCase_      :   List[str]      =	do_center_crop
													UpperCAmelCase_      :   Optional[Any]      =	crop_size
													UpperCAmelCase_      :   List[str]      =	do_rescale
													UpperCAmelCase_      :   Union[str, Any]      =	rescale_factor
													UpperCAmelCase_      :   int      =	do_normalize
													UpperCAmelCase_      :   Any      =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													UpperCAmelCase_      :   Optional[Any]      =	image_std if image_std is not None else OPENAI_CLIP_STD
													UpperCAmelCase_      :   int      =	do_convert_rgb

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   Any      =	get_resize_output_image_size(
													    __magic_name__							,  size=(size['''height'''], size['''width'''])							,  default_to_square=__magic_name__ )
													return resize(__magic_name__							,  size=__magic_name__							,  resample=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	get_size_dict(__magic_name__ )
													return center_crop(__magic_name__							,  size=(size['''height'''], size['''width'''])							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[int, float]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							Union[str, Any]:







													"""simple docstring"""
													return rescale(__magic_name__							,  scale=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													return normalize(__magic_name__							,  mean=__magic_name__							,  std=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )



							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  ImageInput							,  __magic_name__				:  bool = None							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  int = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  float = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[str, TensorType]] = None							,  __magic_name__				:  Optional[ChannelDimension] = ChannelDimension.FIRST							,  **__magic_name__				:  Union[str, Any]							,  )       ->							PIL.Image.Image:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_      :   Dict      =	size if size is not None else self.size
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   str      =	resample if resample is not None else self.resample
													UpperCAmelCase_      :   Tuple      =	do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_      :   List[str]      =	crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__ )
													UpperCAmelCase_      :   str      =	do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_      :   Tuple      =	rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_      :   List[Any]      =	do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_      :   Tuple      =	image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_      :   int      =	image_std if image_std is not None else self.image_std
													UpperCAmelCase_      :   Optional[Any]      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													UpperCAmelCase_      :   Any      =	make_list_of_images(__magic_name__ )

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

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

													if do_center_crop and crop_size is None:
																			raise ValueError('''Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.''' )

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			UpperCAmelCase_      :   Optional[int]      =	[convert_to_rgb(__magic_name__ ) for image in images]

													# All transformations expect numpy arrays.
													UpperCAmelCase_      :   Optional[Any]      =	[to_numpy_array(__magic_name__ ) for image in images]

													if do_resize:
																			UpperCAmelCase_      :   Dict      =	[self.resize(image=__magic_name__							,  size=__magic_name__							,  resample=__magic_name__ ) for image in images]

													if do_center_crop:
																			UpperCAmelCase_      :   Union[str, Any]      =	[self.center_crop(image=__magic_name__							,  size=__magic_name__ ) for image in images]

													if do_rescale:
																			UpperCAmelCase_      :   Dict      =	[self.rescale(image=__magic_name__							,  scale=__magic_name__ ) for image in images]

													if do_normalize:
																			UpperCAmelCase_      :   Optional[int]      =	[self.normalize(image=__magic_name__							,  mean=__magic_name__							,  std=__magic_name__ ) for image in images]

													UpperCAmelCase_      :   Optional[Any]      =	[to_channel_dimension_format(__magic_name__							,  __magic_name__ ) for image in images]

													UpperCAmelCase_      :   int      =	{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__							,  tensor_type=__magic_name__ )












import argparse
import os


UpperCAmelCase__    :      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__    :      Tuple       					=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__    :      List[str]       					=parser.parse_args()

							UpperCAmelCase__    :      Dict       					=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__    :      Optional[int]       					=info['''cuda''']
							os.system(F"echo \"export CUDA='{cuda}'\" >> ~/.profile")
							print(F"echo \"export CUDA='{cuda}'\" >> ~/.profile")





