




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 		_A(       UpperCamelCase__							:			int				)    -> Optional[int]:
							'''simple docstring'''


							return EnvironmentCommand()







def 		_A(       UpperCamelCase__							:			Tuple				)    -> List[str]:
							'''simple docstring'''


							return EnvironmentCommand(args.accelerate_config_file				)







class a (     __lowerCAmelCase  ):
						"""simple docstring"""

						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     str				)    ->						Tuple:
													"""simple docstring"""


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






						def __init__( self :     Dict				,							lowerCamelCase__ :     str				,							*lowerCamelCase__ :     Optional[Any]				)    ->						None:
													"""simple docstring"""


													__lowercase      =    accelerate_config_file






						def     UpperCAmelCase_      ( self :     str				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    '''not installed'''
													if is_safetensors_available():
																				import safetensors

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

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

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

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

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

													__lowercase      =    '''not installed'''
													__lowercase      =    '''NA'''
													if is_torch_available():
																				import torch

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

													__lowercase      =    '''not installed'''
													__lowercase      =    '''NA'''
													if is_tf_available():
																				import tensorflow as tf

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

													__lowercase      =    '''not installed'''
													__lowercase      =    '''not installed'''
													__lowercase      =    '''not installed'''
													__lowercase      =    '''NA'''
													if is_flax_available():
																				import flax
																				import jax
																				import jaxlib

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

													__lowercase      =    {
													    '''`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(lowerCAmelCase_				)				)

													return info






						@staticmethod
						def     UpperCAmelCase_      ( lowerCamelCase__ :     str				)    ->						Tuple:
													"""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 :     Dict				,							lowerCamelCase__ :     float				,							lowerCamelCase__ :     int				)    ->						Dict:
													"""simple docstring"""


													if k in (0.0_4, 0.0_6):
																				__lowercase      =    k
																				__lowercase      =    window_size
													else:
																				raise ValueError('''invalid k value'''				)






						def __str__( self :     int				)    ->						str:
													"""simple docstring"""


													return str(self.k				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     str				)    ->						tuple[cva.Mat, list[list[int]]]:
													"""simple docstring"""


													__lowercase      =    cva.imread(lowerCamelCase__				,							0				)
													__lowercase     ,     __lowercase      =    img.shape
													__lowercase      =    []
													__lowercase      =    img.copy()
													__lowercase      =    cva.cvtColor(lowerCamelCase__				,							cva.COLOR_GRAY2RGB				)
													__lowercase     ,     __lowercase      =    np.gradient(lowerCamelCase__				)
													__lowercase      =    dx**2
													__lowercase      =    dy**2
													__lowercase      =    dx * dy
													__lowercase      =    0.0_4
													__lowercase      =    self.window_size // 2
													for y in range(lowerCamelCase__				,							h - offset				):
																				for x in range(lowerCamelCase__				,							w - offset				):
																											__lowercase      =    ixx[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    iyy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    ixy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()

																											__lowercase      =    (wxx * wyy) - (wxy**2)
																											__lowercase      =    wxx + wyy
																											__lowercase      =    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__":
			UpperCAmelCase__				     = HarrisCorner(0.04, 3)
			UpperCAmelCase__     ,							UpperCAmelCase__				     = edge_detect.detect("path_to_image")
			cva.imwrite("detect.png", color_img)






