

import math
import sys

import cva
import numpy as np

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   float      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	math.sqrt(__snake_case      )
     lowercase__   :       Union[str, Any]				=	1 / (sigma * math.sqrt(2 * math.pi      ))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     lowercase__   :       Union[str, Any]				=	kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   float      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	np.zeros((kernel_size, kernel_size)      )
     for i in range(0     ,					__snake_case      ):
          for j in range(0     ,					__snake_case      ):
               lowercase__   :       List[str]				=	math.sqrt(
                   abs(i - kernel_size // 2      ) ** 2 + abs(j - kernel_size // 2      ) ** 2      )
     return vec_gaussian(__snake_case     ,					__snake_case      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   float     ,					SCREAMING_SNAKE_CASE_		:   float     ,					SCREAMING_SNAKE_CASE_		:   int     ,					):



     '''simple docstring'''





     lowercase__   :       int				=	np.zeros(img.shape      )
     lowercase__   :       Optional[int]				=	get_gauss_kernel(__snake_case     ,					__snake_case      )
     lowercase__				,			lowercase__   :       Union[str, 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      ):
               lowercase__   :       Dict				=	get_slice(__snake_case     ,					__snake_case     ,					__snake_case     ,					__snake_case      )
               lowercase__   :       int				=	img_s - img_s[kernel_size // 2, kernel_size // 2]
               lowercase__   :       int				=	vec_gaussian(__snake_case     ,					__snake_case      )
               lowercase__   :       str				=	np.multiply(__snake_case     ,					__snake_case      )
               lowercase__   :       Union[str, Any]				=	np.multiply(__snake_case     ,					__snake_case      )
               lowercase__   :       Dict				=	np.sum(__snake_case      ) / np.sum(__snake_case      )
               lowercase__   :       Dict				=	val
     return imga

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   list      ):



     '''simple docstring'''





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


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

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




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	kwargs.get('is_split_into_words'						,       a)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Optional[Any]				=	kwargs.get('is_split_into_words'						,       a)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

