
"""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


A__   :						str 							=     logging.get_logger(__name__)


if is_vision_available():
    import PIL







class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     _A		    =				['pixel_values']



     def __init__(   self			,      __UpperCamelCase = True			,      __UpperCamelCase = None			,      __UpperCamelCase = PILImageResampling.BICUBIC			,      __UpperCamelCase = True			,      __UpperCamelCase = None			,      __UpperCamelCase = True			,      __UpperCamelCase = 1 / 2_55			,      __UpperCamelCase = True			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = True			,      **__UpperCamelCase			,      )->       None:
          super().__init__(**__UpperCamelCase	)
          UpperCAmelCase__				:			List[Any]							=    size if size is not None else {"shortest_edge": 2_24}
          UpperCAmelCase__				:			str							=    get_size_dict(__UpperCamelCase			,      default_to_square=__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCAmelCase__				:			Any							=    get_size_dict(__UpperCamelCase	)

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



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = PILImageResampling.BICUBIC			,      __UpperCamelCase = None			,      **__UpperCamelCase			,      )->       np.ndarray:
          UpperCAmelCase__				:			List[str]							=    get_size_dict(__UpperCamelCase			,      default_to_square=__UpperCamelCase	)
          UpperCAmelCase__				:			int							=    get_resize_output_image_size(
              __UpperCamelCase			,      size=(size["height"], size["width"])			,      default_to_square=__UpperCamelCase	)
          return resize(__UpperCamelCase			,      size=__UpperCamelCase			,      resample=__UpperCamelCase			,      data_format=__UpperCamelCase			,      **__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = None			,      **__UpperCamelCase			,      )->       np.ndarray:
          UpperCAmelCase__				:			List[str]							=    get_size_dict(__UpperCamelCase	)
          return center_crop(__UpperCamelCase			,      size=(size["height"], size["width"])			,      data_format=__UpperCamelCase			,      **__UpperCamelCase	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = None			,      **__UpperCamelCase			,      )->       Any:
          return rescale(__UpperCamelCase			,      scale=__UpperCamelCase			,      data_format=__UpperCamelCase			,      **__UpperCamelCase	)



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



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = None			,      __UpperCamelCase = ChannelDimension.FIRST			,      **__UpperCamelCase			,      )->       PIL.Image.Image:
          UpperCAmelCase__				:			Union[str, Any]							=    do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__				:			List[str]							=    size if size is not None else self.size
          UpperCAmelCase__				:			Optional[int]							=    get_size_dict(__UpperCamelCase			,      default_to_square=__UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    resample if resample is not None else self.resample
          UpperCAmelCase__				:			List[Any]							=    do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__				:			Any							=    crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__				:			Dict							=    get_size_dict(__UpperCamelCase	)
          UpperCAmelCase__				:			Union[str, Any]							=    do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__				:			Union[str, Any]							=    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__				:			List[Any]							=    image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__				:			Optional[int]							=    image_std if image_std is not None else self.image_std
          UpperCAmelCase__				:			List[str]							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          UpperCAmelCase__				:			int							=    make_list_of_images(__UpperCamelCase	)

          if not valid_images(__UpperCamelCase	):
               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__				:			str							=    [convert_to_rgb(__UpperCamelCase	) for image in images]

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

          if do_resize:
               UpperCAmelCase__				:			Optional[Any]							=    [self.resize(image=__UpperCamelCase			,      size=__UpperCamelCase			,      resample=__UpperCamelCase	) for image in images]

          if do_center_crop:
               UpperCAmelCase__				:			Optional[Any]							=    [self.center_crop(image=__UpperCamelCase			,      size=__UpperCamelCase	) for image in images]

          if do_rescale:
               UpperCAmelCase__				:			Optional[int]							=    [self.rescale(image=__UpperCamelCase			,      scale=__UpperCamelCase	) for image in images]

          if do_normalize:
               UpperCAmelCase__				:			Union[str, Any]							=    [self.normalize(image=__UpperCamelCase			,      mean=__UpperCamelCase			,      std=__UpperCamelCase	) for image in images]

          UpperCAmelCase__				:			Dict							=    [to_channel_dimension_format(__UpperCamelCase			,      __UpperCamelCase	) for image in images]

          UpperCAmelCase__				:			int							=    {"pixel_values": images}
          return BatchFeature(data=__UpperCamelCase			,      tensor_type=__UpperCamelCase	)





"""simple docstring"""




import argparse
import os


A__   :						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__":
    A__   :						str 							=     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)
    A__   :						Any 							=     parser.parse_args()

    A__   :						List[Any] 							=     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""")

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



