



'''simple docstring'''





import argparse
import os


A__						:						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__":
						A__						:						Union[str, 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)
						A__						:						int          =      parser.parse_args()

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










import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor



class        __snake_case		(  unittest.TestCase		):




							def __init__( self  : Union[str, Any]       ,       A_  : Optional[Any]       ,       A_  : Optional[Any]=7       ,       A_  : str=3       ,       A_  : Optional[Any]=1_8       ,       A_  : Union[str, Any]=3_0       ,       A_  : str=4_0_0       ,       A_  : List[str]=True       ,       A_  : str=None       ,       A_  : str=True       ,       A_  : List[Any]=None       ,       A_  : List[Any]=True       ,       A_  : Optional[Any]=[0.5, 0.5, 0.5]       ,       A_  : Optional[int]=[0.5, 0.5, 0.5]       ,       ):
									lowerCAmelCase_    :  List[str]             =				size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									lowerCAmelCase_    :  List[Any]             =				crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_    :  str             =				parent
									lowerCAmelCase_    :  Union[str, Any]             =				batch_size
									lowerCAmelCase_    :  Optional[Any]             =				num_channels
									lowerCAmelCase_    :  List[str]             =				image_size
									lowerCAmelCase_    :  List[str]             =				min_resolution
									lowerCAmelCase_    :  Union[str, Any]             =				max_resolution
									lowerCAmelCase_    :  Optional[int]             =				do_resize
									lowerCAmelCase_    :  Dict             =				size
									lowerCAmelCase_    :  Dict             =				do_center_crop
									lowerCAmelCase_    :  str             =				crop_size
									lowerCAmelCase_    :  List[Any]             =				do_normalize
									lowerCAmelCase_    :  str             =				image_mean
									lowerCAmelCase_    :  Dict             =				image_std







							def      UpperCAmelCase__    ( self  : int):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}





@require_torch
@require_vision
class        __snake_case		(  UpperCamelCase_		,unittest.TestCase		):
							_a       =				DeiTImageProcessor if is_vision_available() else None
							_a       =				True




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  int             =				DeiTImageProcessingTester(self)




							@property
							def      UpperCAmelCase__    ( self  : Optional[Any]):
									return self.image_processor_tester.prepare_image_processor_dict()




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  List[Any]             =				self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(A_       ,       '''do_resize'''))
									self.assertTrue(hasattr(A_       ,       '''size'''))
									self.assertTrue(hasattr(A_       ,       '''do_center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''do_normalize'''))
									self.assertTrue(hasattr(A_       ,       '''image_mean'''))
									self.assertTrue(hasattr(A_       ,       '''image_std'''))




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Tuple             =				self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,       {'''height''': 2_0, '''width''': 2_0})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_    :  Any             =				self.image_processing_class.from_dict(self.image_processor_dict       ,       size=4_2       ,       crop_size=8_4)
									self.assertEqual(image_processor.size       ,       {'''height''': 4_2, '''width''': 4_2})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 8_4, '''width''': 8_4})




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									pass




							def      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  List[str]             =				self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									lowerCAmelCase_    :  Any             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       Image.Image)

									# Test not batched input
									lowerCAmelCase_    :  Union[str, Any]             =				image_processing(image_inputs[0]       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )

									# Test batched
									lowerCAmelCase_    :  Dict             =				image_processing(A_       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )




							def      UpperCAmelCase__    ( self  : List[str]):
									# Initialize image_processing
									lowerCAmelCase_    :  Union[str, Any]             =				self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       numpify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       np.ndarray)

									# Test not batched input
									lowerCAmelCase_    :  Optional[Any]             =				image_processing(image_inputs[0]       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )

									# Test batched
									lowerCAmelCase_    :  str             =				image_processing(A_       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )







							def      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  str             =				self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_    :  Union[str, Any]             =				prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=A_       ,       torchify=A_)
									for image in image_inputs:
											self.assertIsInstance(A_       ,       torch.Tensor)

									# Test not batched input
									lowerCAmelCase_    :  Dict             =				image_processing(image_inputs[0]       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )

									# Test batched
									lowerCAmelCase_    :  Tuple             =				image_processing(A_       ,       return_tensors='''pt''').pixel_values
									self.assertEqual(
									    encoded_images.shape       ,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )       ,       )





