



"""simple docstring"""

import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__					:						Dict												=			'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowercase__					:						str												=			direct_transformers_import(PATH_TO_TRANSFORMERS)

lowercase__					:						int												=			transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowercase__					:						Any												=			re.compile(r'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


lowercase__					:						Dict												=			{
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}




def __lowercase    (							_a					):
    snake_case_				:  List[str]       =			None

    # source code of `config_class`
    snake_case_				:  Union[str, Any]       =			inspect.getsource(_a					)
    snake_case_				:  List[Any]       =			_re_checkpoint.findall(_a					)

    # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
    # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
    for ckpt_name, ckpt_link in checkpoints:
        # allow the link to end with `/`
        if ckpt_link.endswith('''/'''					):
            snake_case_				:  List[str]       =			ckpt_link[:-1]

        # verify the checkpoint name corresponds to the checkpoint link
        snake_case_				:  List[Any]       =			f"https://huggingface.co/{ckpt_name}"
        if ckpt_link == ckpt_link_from_name:
            snake_case_				:  int       =			ckpt_name
            break

    return checkpoint




def __lowercase    (							):
    snake_case_				:  Any       =			[]

    for config_class in list(CONFIG_MAPPING.values()					):
        # Skip deprecated models
        if "models.deprecated" in config_class.__module__:
            continue
        snake_case_				:  Dict       =			get_checkpoint_from_config_class(_a					)

        snake_case_				:  List[Any]       =			config_class.__name__
        if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
            configs_without_checkpoint.append(_a					)

    if len(_a					) > 0:
        snake_case_				:  int       =			'''\n'''.join(sorted(_a					)					)
        raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"					)


if __name__ == "__main__":
     check_config_docstrings_have_checkpoints()










"""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()





