



"""simple docstring"""

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment




def __lowercase    (							_a	,    _a=()	,    _a=None	,    _a="no"	,    _a="29500"					):
    snake_case_				:  Tuple       =			False
    snake_case_				:  Optional[int]       =			False
    if any(key.startswith('''KAGGLE'''					) for key in os.environ.keys()					):
        snake_case_				:  int       =			True
    elif "IPython" in sys.modules:
        snake_case_				:  List[str]       =			'''google.colab''' in str(sys.modules['''IPython'''].get_ipython()					)

    try:
        snake_case_				:  Optional[Any]       =			PrecisionType(mixed_precision.lower()					)
    except ValueError:
        raise ValueError(
            f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."					)

    if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''	,    _a					) is not None):
        # TPU launch
        import torch_xla.distributed.xla_multiprocessing as xmp

        if len(AcceleratorState._shared_state					) > 0:
            raise ValueError(
                '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
                '''your training function. Restart your notebook and make sure no cells initializes an '''
                '''`Accelerator`.'''					)
        if num_processes is None:
            snake_case_				:  Any       =			8

        snake_case_				:  Any       =			PrepareForLaunch(_a	,    distributed_type='''TPU'''					)
        print(f"Launching a training on {num_processes} TPU cores."					)
        xmp.spawn(_a	,    args=_a	,    nprocs=_a	,    start_method='''fork'''					)
    elif in_colab:
        # No need for a distributed launch otherwise as it's either CPU or one GPU.
        if torch.cuda.is_available():
            print('''Launching training on one GPU.'''					)
        else:
            print('''Launching training on one CPU.'''					)
        function(*_a					)
    else:
        if num_processes is None:
            raise ValueError(
                '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'''					)

        if num_processes > 1:
            # Multi-GPU launch
            from torch.multiprocessing import start_processes
            from torch.multiprocessing.spawn import ProcessRaisedException

            if len(AcceleratorState._shared_state					) > 0:
                raise ValueError(
                    '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
                    '''inside your training function. Restart your notebook and make sure no cells initializes an '''
                    '''`Accelerator`.'''					)

            if torch.cuda.is_initialized():
                raise ValueError(
                    '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
                    '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
                    '''function.'''					)

            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=_a	,    master_addr='''127.0.01'''	,    master_port=_a	,    mixed_precision=_a					):
                snake_case_				:  Tuple       =			PrepareForLaunch(_a	,    distributed_type='''MULTI_GPU'''					)
                print(f"Launching training on {num_processes} GPUs."					)
                try:
                    start_processes(_a	,    args=_a	,    nprocs=_a	,    start_method='''fork'''					)
                except ProcessRaisedException as e:
                    if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                        raise RuntimeError(
                            '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
                            '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
                            '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
                            '''which one is problematic.'''					) from e

        else:
            # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
            if is_mps_available():
                snake_case_				:  Union[str, Any]       =			'''1'''
                print('''Launching training on MPS.'''					)
            elif torch.cuda.is_available():
                print('''Launching training on one GPU.'''					)
            else:
                print('''Launching training on CPU.'''					)
            function(*_a					)






def __lowercase    (							_a	,    _a=()	,    _a=2					):
    from torch.multiprocessing import start_processes

    with tempfile.NamedTemporaryFile() as tmp_file:
        # torch.distributed will expect a few environment variable to be here. We set the ones common to each
        # process here (the other ones will be set be the launcher).
        with patch_environment(
            world_size=_a	,    master_addr='''127.0.01'''	,    master_port='''29500'''	,    accelerate_mixed_precision='''no'''	,    accelerate_debug_rdv_file=tmp_file.name	,    accelerate_use_cpu='''yes'''	,    ):
            snake_case_				:  Dict       =			PrepareForLaunch(_a	,    debug=_a					)
            start_processes(_a	,    args=_a	,    nprocs=_a	,    start_method='''fork'''					)










"""simple docstring"""

import math
import sys

import cva
import numpy as np




def __lowercase    (							_a	,    _a					):
    # For applying gaussian function for each element in matrix.
    snake_case_				:  Dict       =			math.sqrt(_a					)
    snake_case_				:  Tuple       =			1 / (sigma * math.sqrt(2 * math.pi					))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5					)




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Optional[int]       =			kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]




def __lowercase    (							_a	,    _a					):
    # Creates a gaussian kernel of given dimension.
    snake_case_				:  Union[str, Any]       =			np.zeros((kernel_size, kernel_size)					)
    for i in range(0	,    _a					):
        for j in range(0	,    _a					):
            snake_case_				:  Union[str, Any]       =			math.sqrt(
                abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
    return vec_gaussian(_a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    ):
    snake_case_				:  int       =			np.zeros(img.shape					)
    snake_case_				:  str       =			get_gauss_kernel(_a	,    _a					)
    snake_case_,			snake_case_				:  Any       =			img.shape
    for i in range(kernel_size // 2	,    size_x - kernel_size // 2					):
        for j in range(kernel_size // 2	,    size_y - kernel_size // 2					):
            snake_case_				:  List[Any]       =			get_slice(_a	,    _a	,    _a	,    _a					)
            snake_case_				:  Union[str, Any]       =			img_s - img_s[kernel_size // 2, kernel_size // 2]
            snake_case_				:  Optional[Any]       =			vec_gaussian(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  List[Any]       =			np.sum(_a					) / np.sum(_a					)
            snake_case_				:  Dict       =			val
    return imga




def __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			args[1] if args[1:] else '''../image_data/lena.jpg'''
    snake_case_				:  int       =			float(args[2]					) if args[2:] else 1.0
    snake_case_				:  Dict       =			float(args[3]					) if args[3:] else 1.0
    if args[4:]:
        snake_case_				:  Optional[Any]       =			int(args[4]					)
        snake_case_				:  Union[str, Any]       =			kernel_size + abs(kernel_size % 2 - 1					)
    else:
        snake_case_				:  Union[str, Any]       =			5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
     lowercase__					,lowercase__					,lowercase__					,lowercase__					:						Tuple												=			parse_args(sys.argv)
     lowercase__					:						str												=			cva.imread(filename, 0)
     cva.imshow('''input image''', img)

     lowercase__					:						Tuple												=			img / 2_55
     lowercase__					:						Optional[int]												=			out.astype('''float32''')
     lowercase__					:						Union[str, Any]												=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     lowercase__					:						List[str]												=			out * 2_55
     lowercase__					:						List[str]												=			np.uinta(out)
     cva.imshow('''output image''', out)
     cva.waitKey(0)
     cva.destroyAllWindows()





