
'''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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Tuple:
	return EnvironmentCommand()







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	return EnvironmentCommand(args.accelerate_config_file	)





class 						__UpperCAmelCase      (					lowercase__						):


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		parser.add_parser('env'  )
								download_parser.set_defaults(func=__lowercase  )
								download_parser.add_argument(
								    '--accelerate-config_file'    ,   default=__lowercase    ,   help='The accelerate config file to use for the default values in the launching script.'    ,   )
								download_parser.set_defaults(func=__lowercase  )


							def __init__(       self    ,   lowerCAmelCase_    ,   *lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		accelerate_config_file


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		"""not installed"""
								if is_safetensors_available():
									import safetensors

									_snake_case   						=		safetensors.__version__
								elif importlib.util.find_spec('safetensors'  ) is not None:
									import safetensors

									_snake_case   						=		F'{safetensors.__version__} but is ignored because of PyTorch version too old.'

								_snake_case   						=		"""not installed"""
								_snake_case   						=		"""not found"""
								if is_accelerate_available():
									import accelerate
									from accelerate.commands.config import default_config_file, load_config_from_file

									_snake_case   						=		accelerate.__version__
									# Get the default from the config file.
									if self._accelerate_config_file is not None or os.path.isfile(__lowercase  ):
										_snake_case   						=		load_config_from_file(self._accelerate_config_file  ).to_dict()

									_snake_case   						=		(
									    """\n""".join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()]  )
									    if isinstance(__lowercase    ,   __lowercase  )
									    else F'\t{accelerate_config}'
									)

								_snake_case   						=		"""not installed"""
								_snake_case   						=		"""NA"""
								if is_torch_available():
									import torch

									_snake_case   						=		torch.__version__
									_snake_case   						=		torch.cuda.is_available()

								_snake_case   						=		"""not installed"""
								_snake_case   						=		"""NA"""
								if is_tf_available():
									import tensorflow as tf

									_snake_case   						=		tf.__version__
									try:
										# deprecated in v2.1
										_snake_case   						=		tf.test.is_gpu_available()
									except AttributeError:
										# returns list of devices, convert to bool
										_snake_case   						=		bool(tf.config.list_physical_devices('GPU'  )  )

								_snake_case   						=		"""not installed"""
								_snake_case   						=		"""not installed"""
								_snake_case   						=		"""not installed"""
								_snake_case   						=		"""NA"""
								if is_flax_available():
									import flax
									import jax
									import jaxlib

									_snake_case   						=		flax.__version__
									_snake_case   						=		jax.__version__
									_snake_case   						=		jaxlib.__version__
									_snake_case   						=		jax.lib.xla_bridge.get_backend().platform

								_snake_case   						=		{
								    """`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(__lowercase  )  )

								return info




							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_  ):





								"""simple docstring"""






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









import cva
import numpy as np
class     a    :



      """simple docstring"""


      def __init__(      self					:			Optional[Any]		,      __lowercase					:			float		,      __lowercase					:			int					)							->      List[Any]:

        if k in (0.04, 0.06):
          __UpperCAmelCase							:	int           =  k
          __UpperCAmelCase							:	List[str]           =  window_size
        else:
          raise ValueError("""invalid k value"""					)
      def __str__(      self					:			Union[str, Any]					)							->      str:
        return str(self.k					)




      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str					)							->      tuple[cva.Mat, list[list[int]]]:
        __UpperCAmelCase							:	Optional[Any]           =  cva.imread(__lowercase		,      0					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[Any]           =  img.shape
        __UpperCAmelCase							:	list[list[int]]           =  []
        __UpperCAmelCase							:	Any           =  img.copy()
        __UpperCAmelCase							:	Tuple           =  cva.cvtColor(__lowercase		,      cva.COLOR_GRAY2RGB					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[str]           =  np.gradient(__lowercase					)
        __UpperCAmelCase							:	int           =  dx**2
        __UpperCAmelCase							:	Any           =  dy**2
        __UpperCAmelCase							:	Any           =  dx * dy
        __UpperCAmelCase							:	Optional[int]           =  0.04
        __UpperCAmelCase							:	List[str]           =  self.window_size // 2
        for y in range(__lowercase		,      h - offset					):
          for x in range(__lowercase		,      w - offset					):
            __UpperCAmelCase							:	Tuple           =  ixx[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	List[Any]           =  iyy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	Optional[int]           =  ixy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()

            __UpperCAmelCase							:	int           =  (wxx * wyy) - (wxy**2)
            __UpperCAmelCase							:	Any           =  wxx + wyy
            __UpperCAmelCase							:	Optional[Any]           =  det - k * (trace**2)
            # Can change the value
            if r > 0.5:
              corner_list.append([x, y, r]					)
              color_img.itemset((y, x, 0)		,      0					)
              color_img.itemset((y, x, 1)		,      0					)
              color_img.itemset((y, x, 2)		,      255					)
        return color_img, corner_list


if __name__ == "__main__":
       a		:       int			     = HarrisCorner(0.04, 3)
       a     ,a		:       Any			     = edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)


