from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_      =      logging.get_logger(__name__)

A_      =      {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class      __lowercase					(    _A    ):
      lowercase					 =     'roformer'

      def __init__(	self				:      Dict							,							__lowerCamelCase				:      List[str]=5_00_00							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      Dict=7_68							,							__lowerCamelCase				:      Dict=12							,							__lowerCamelCase				:      Union[str, Any]=12							,							__lowerCamelCase				:      List[Any]=30_72							,							__lowerCamelCase				:      int="gelu"							,							__lowerCamelCase				:      List[Any]=0.1							,							__lowerCamelCase				:      str=0.1							,							__lowerCamelCase				:      Optional[int]=15_36							,							__lowerCamelCase				:      str=2							,							__lowerCamelCase				:      Dict=0.02							,							__lowerCamelCase				:      List[str]=1E-12							,							__lowerCamelCase				:      Optional[Any]=0							,							__lowerCamelCase				:      Optional[Any]=False							,							__lowerCamelCase				:      int=True							,							**__lowerCamelCase				:      List[str]							,							)							->		int:


         '''simple docstring'''

         super().__init__(pad_token_id=__lowerCamelCase							,							**__lowerCamelCase   )

         lowercase							=		vocab_size
         lowercase							=		hidden_size if embedding_size is None else embedding_size
         lowercase							=		hidden_size
         lowercase							=		num_hidden_layers
         lowercase							=		num_attention_heads
         lowercase							=		hidden_act
         lowercase							=		intermediate_size
         lowercase							=		hidden_dropout_prob
         lowercase							=		attention_probs_dropout_prob
         lowercase							=		max_position_embeddings
         lowercase							=		type_vocab_size
         lowercase							=		initializer_range
         lowercase							=		layer_norm_eps
         lowercase							=		rotary_value
         lowercase							=		use_cache






class      __lowercase					(    _A    ):

      @property
      def        __a		(	self				:      Any   )							->		Mapping[str, Mapping[int, str]]:


         '''simple docstring'''

         if self.task == "multiple-choice":
            lowercase							=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
            lowercase							=		{0: '''batch''', 1: '''sequence'''}
         lowercase							=		{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ]   )






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		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       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


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

						A_      =      img / 255
						A_      =      out.astype("float32")
						A_      =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						A_      =      out * 255
						A_      =      np.uinta(out)
						cva.imshow("output image", out)
						cva.waitKey(0)
						cva.destroyAllWindows()





