
"""simple docstring"""





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						np.ndarray:


					"""simple docstring"""





					_UpperCamelCase									=    cva.getAffineTransform(__snake_case,	__snake_case      )
					return cva.warpAffine(__snake_case,	__snake_case,	(rows, cols)      )


if __name__ == "__main__":
				# read original image
				_a								=						cva.imread(
				    str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
				)
				# turn image in gray scale value
				_a								=						cva.cvtColor(image, cva.COLOR_BGR2GRAY)
				# get image shape
				_a				,				_a								=						gray_img.shape

				# set different points to rotate image
				_a								=						np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
				_a								=						np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
				_a								=						np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
				_a								=						np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

				# add all rotated images in a list
				_a								=						[
				    gray_img,
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
				]

				# plot different image rotations
				_a								=						plt.figure(1)
				_a								=						["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
				for i, image in enumerate(images):
								plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
								plt.title(titles[i])
								plt.axis("""off""")
								plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
				plt.show()














'''simple docstring'''



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 				lowercase_					(     __A			:							List[Any]      )							->   Union[str, Any]:



 """simple docstring"""




 return EnvironmentCommand()


def 				lowercase_					(     __A			:							int      )							->   Dict:



 """simple docstring"""




 return EnvironmentCommand(args.accelerate_config_file      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @staticmethod
      def 					A__   (			UpperCAmelCase      :		ArgumentParser		)	->	List[str]:


       '''simple docstring'''

       lowercase       : Optional[int]						=parser.add_parser('''env'''		)
       download_parser.set_defaults(func=UpperCAmelCase		)
       download_parser.add_argument(
           '''--accelerate-config_file'''  ,   default=UpperCAmelCase  ,   help='''The accelerate config file to use for the default values in the launching script.'''  ,   )
       download_parser.set_defaults(func=UpperCAmelCase		)






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		Tuple  ,   *UpperCAmelCase      :		Tuple		)	->	None:


       '''simple docstring'''

       lowercase       : List[str]						=accelerate_config_file






      def 					A__   (			self      :		Tuple		)	->	int:


       '''simple docstring'''

       lowercase       : Optional[Any]						='''not installed'''
       if is_safetensors_available():
        import safetensors

        lowercase       : List[Any]						=safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''		) is not None:
        import safetensors

        lowercase       : List[str]						=f'{safetensors.__version__} but is ignored because of PyTorch version too old.'

       lowercase       : List[str]						='''not installed'''
       lowercase       : str						='''not found'''
       if is_accelerate_available():
        import accelerate
        from accelerate.commands.config import default_config_file, load_config_from_file

        lowercase       : List[str]						=accelerate.__version__
        # Get the default from the config file.
        if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase		):
         lowercase       : Dict						=load_config_from_file(self._accelerate_config_file		).to_dict()

        lowercase       : Optional[int]						=(
            '''\n'''.join([f'\t- {prop}: {val}' for prop, val in accelerate_config.items()]		)
            if isinstance(UpperCAmelCase  ,   UpperCAmelCase		)
            else f'\t{accelerate_config}'
        )

       lowercase       : Optional[int]						='''not installed'''
       lowercase       : Optional[Any]						='''NA'''
       if is_torch_available():
        import torch

        lowercase       : List[Any]						=torch.__version__
        lowercase       : Dict						=torch.cuda.is_available()

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_tf_available():
        import tensorflow as tf

        lowercase       : List[Any]						=tf.__version__
        try:
         # deprecated in v2.1
         lowercase       : Optional[Any]						=tf.test.is_gpu_available()
        except AttributeError:
         # returns list of devices, convert to bool
         lowercase       : Optional[int]						=bool(tf.config.list_physical_devices('''GPU'''		)		)

       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : Optional[Any]						='''not installed'''
       lowercase       : List[str]						='''NA'''
       if is_flax_available():
        import flax
        import jax
        import jaxlib

        lowercase       : Dict						=flax.__version__
        lowercase       : List[Any]						=jax.__version__
        lowercase       : Any						=jaxlib.__version__
        lowercase       : Tuple						=jax.lib.xla_bridge.get_backend().platform

       lowercase       : str						={
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f'{safetensors_version}',
           '''Accelerate version''': f'{accelerate_version}',
           '''Accelerate config''': f'{accelerate_config_str}',
           '''PyTorch version (GPU?)''': f'{pt_version} ({pt_cuda_available})',
           '''Tensorflow version (GPU?)''': f'{tf_version} ({tf_cuda_available})',
           '''Flax version (CPU?/GPU?/TPU?)''': f'{flax_version} ({jax_backend})',
           '''Jax version''': f'{jax_version}',
           '''JaxLib version''': f'{jaxlib_version}',
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''		)
       print(self.format_dict(UpperCAmelCase		)		)

       return info






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Any		)	->	Any:


       '''simple docstring'''

       return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]		) + "\n"



