

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							:			int      		= logging.get_logger(__name__)


if is_vision_available():
	import PIL



class 					A__(lowerCamelCase__       ):
						"""simple docstring"""






						_A						:							Union[str, Any]									= ['''pixel_values''']





						def __init__(						self     ,			_lowercase = True     ,			_lowercase = None     ,			_lowercase = PILImageResampling.BICUBIC     ,			_lowercase = True     ,			_lowercase = None     ,			_lowercase = True     ,			_lowercase = 1 / 255     ,			_lowercase = True     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = True     ,			**_lowercase     ,			)    ->   Tuple:
									super().__init__(**SCREAMING_SNAKE_CASE_						)
									a_	:	Union[str, Any]          =   size if size is not None else {"""shortest_edge""": 224}
									a_	:	Any          =   get_size_dict(SCREAMING_SNAKE_CASE_     ,			default_to_square=SCREAMING_SNAKE_CASE_						)
									a_	:	Optional[Any]          =   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									a_	:	Optional[Any]          =   get_size_dict(SCREAMING_SNAKE_CASE_						)

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





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase = PILImageResampling.BICUBIC     ,			_lowercase = None     ,			**_lowercase     ,			)    ->   Any:
									a_	:	Union[str, Any]          =   get_size_dict(SCREAMING_SNAKE_CASE_     ,			default_to_square=SCREAMING_SNAKE_CASE_						)
									a_	:	List[Any]          =   get_resize_output_image_size(
									    SCREAMING_SNAKE_CASE_     ,			size=(size["""height"""], size["""width"""])     ,			default_to_square=SCREAMING_SNAKE_CASE_						)
									return resize(SCREAMING_SNAKE_CASE_     ,			size=SCREAMING_SNAKE_CASE_     ,			resample=SCREAMING_SNAKE_CASE_     ,			data_format=SCREAMING_SNAKE_CASE_     ,			**SCREAMING_SNAKE_CASE_						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase = None     ,			**_lowercase     ,			)    ->   str:
									a_	:	str          =   get_size_dict(SCREAMING_SNAKE_CASE_						)
									return center_crop(SCREAMING_SNAKE_CASE_     ,			size=(size["""height"""], size["""width"""])     ,			data_format=SCREAMING_SNAKE_CASE_     ,			**SCREAMING_SNAKE_CASE_						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase = None     ,			**_lowercase     ,			)    ->   Optional[int]:
									return rescale(SCREAMING_SNAKE_CASE_     ,			scale=SCREAMING_SNAKE_CASE_     ,			data_format=SCREAMING_SNAKE_CASE_     ,			**SCREAMING_SNAKE_CASE_						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase = None     ,			**_lowercase     ,			)    ->   Dict:
									return normalize(SCREAMING_SNAKE_CASE_     ,			mean=SCREAMING_SNAKE_CASE_     ,			std=SCREAMING_SNAKE_CASE_     ,			data_format=SCREAMING_SNAKE_CASE_     ,			**SCREAMING_SNAKE_CASE_						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = None     ,			_lowercase = ChannelDimension.FIRST     ,			**_lowercase     ,			)    ->   List[str]:
									a_	:	int          =   do_resize if do_resize is not None else self.do_resize
									a_	:	List[Any]          =   size if size is not None else self.size
									a_	:	List[str]          =   get_size_dict(SCREAMING_SNAKE_CASE_     ,			default_to_square=SCREAMING_SNAKE_CASE_						)
									a_	:	int          =   resample if resample is not None else self.resample
									a_	:	Optional[Any]          =   do_center_crop if do_center_crop is not None else self.do_center_crop
									a_	:	List[Any]          =   crop_size if crop_size is not None else self.crop_size
									a_	:	int          =   get_size_dict(SCREAMING_SNAKE_CASE_						)
									a_	:	Tuple          =   do_rescale if do_rescale is not None else self.do_rescale
									a_	:	Optional[Any]          =   rescale_factor if rescale_factor is not None else self.rescale_factor
									a_	:	int          =   do_normalize if do_normalize is not None else self.do_normalize
									a_	:	Union[str, Any]          =   image_mean if image_mean is not None else self.image_mean
									a_	:	Optional[Any]          =   image_std if image_std is not None else self.image_std
									a_	:	int          =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									a_	:	Tuple          =   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_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:
												a_	:	Tuple          =   [convert_to_rgb(SCREAMING_SNAKE_CASE_						) for image in images]

									# All transformations expect numpy arrays.
									a_	:	str          =   [to_numpy_array(SCREAMING_SNAKE_CASE_						) for image in images]

									if do_resize:
												a_	:	int          =   [self.resize(image=SCREAMING_SNAKE_CASE_     ,			size=SCREAMING_SNAKE_CASE_     ,			resample=SCREAMING_SNAKE_CASE_						) for image in images]

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

									if do_rescale:
												a_	:	Union[str, Any]          =   [self.rescale(image=SCREAMING_SNAKE_CASE_     ,			scale=SCREAMING_SNAKE_CASE_						) for image in images]

									if do_normalize:
												a_	:	Dict          =   [self.normalize(image=SCREAMING_SNAKE_CASE_     ,			mean=SCREAMING_SNAKE_CASE_     ,			std=SCREAMING_SNAKE_CASE_						) for image in images]

									a_	:	Any          =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,			SCREAMING_SNAKE_CASE_						) for image in images]

									a_	:	Optional[Any]          =   {"""pixel_values""": images}
									return BatchFeature(data=SCREAMING_SNAKE_CASE_     ,			tensor_type=SCREAMING_SNAKE_CASE_						)





import argparse
import os


__snake_case							:			List[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__":
	__snake_case							:			List[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)
	__snake_case							:			int      		= parser.parse_args()

	__snake_case							:			Optional[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""")

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


