"""simple docstring"""




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a      :		Union[str, Any]           =   logging.get_logger(__name__)


_a      :		Union[str, Any]           =   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

_a      :		int           =   {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

_a      :		Optional[Any]           =   {
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 							SCREAMING_SNAKE_CASE     (			)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     (
      list(range(ord("""!"""	)			,ord("""~"""	) + 1	)	) + list(range(ord("""¡"""	)			,ord("""¬"""	) + 1	)	) + list(range(ord("""®"""	)			,ord("""ÿ"""	) + 1	)	)
  )
  _lowerCAmelCase						:       Dict  			=     bs[:]
  _lowerCAmelCase						:       Union[str, Any]  			=     0
  for b in range(2**8	):
    if b not in bs:
      bs.append(_lowerCamelCase	)
      cs.append(2**8 + n	)
      n += 1
  _lowerCAmelCase						:       List[Any]  			=     [chr(_lowerCamelCase	) for n in cs]
  return dict(zip(_lowerCamelCase			,_lowerCamelCase	)	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Dict:
  _lowerCAmelCase						:       int  			=     set()
  _lowerCAmelCase						:       List[Any]  			=     word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)	)
    _lowerCAmelCase						:       Tuple  			=     char
  return pairs






class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			List[str]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Dict							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			List[str]							=		["input_ids", "attention_mask"]






     def __init__(		self				,     a__				,     a__				,     a__="replace"				,     a__="<s>"				,     a__="</s>"				,     a__="</s>"				,     a__="<s>"				,     a__="<unk>"				,     a__="<pad>"				,     a__="<mask>"				,     a__=False				,     **a__				,     ):
       _lowerCAmelCase						:       Union[str, Any]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else bos_token
       _lowerCAmelCase						:       Optional[Any]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else eos_token
       _lowerCAmelCase						:       List[Any]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else sep_token
       _lowerCAmelCase						:       Optional[int]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else cls_token
       _lowerCAmelCase						:       List[Any]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else unk_token
       _lowerCAmelCase						:       Dict  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       _lowerCAmelCase						:       Optional[Any]  			=     AddedToken(a__				,     lstrip=a__				,     rstrip=a__  ) if isinstance(a__				,     a__  ) else mask_token

       super().__init__(
           errors=a__				,     bos_token=a__				,     eos_token=a__				,     unk_token=a__				,     sep_token=a__				,     cls_token=a__				,     pad_token=a__				,     mask_token=a__				,     add_prefix_space=a__				,     **a__				,     )

       with open(a__				,     encoding="""utf-8"""  ) as vocab_handle:
         _lowerCAmelCase						:       List[Any]  			=     json.load(a__  )
       _lowerCAmelCase						:       int  			=     {v: k for k, v in self.encoder.items()}
       _lowerCAmelCase						:       Dict  			=     errors  # how to handle errors in decoding
       _lowerCAmelCase						:       Any  			=     bytes_to_unicode()
       _lowerCAmelCase						:       Union[str, Any]  			=     {v: k for k, v in self.byte_encoder.items()}
       with open(a__				,     encoding="""utf-8"""  ) as merges_handle:
         _lowerCAmelCase						:       Dict  			=     merges_handle.read().split("""\n"""  )[1:-1]
       _lowerCAmelCase						:       Union[str, Any]  			=     [tuple(merge.split()  ) for merge in bpe_merges]
       _lowerCAmelCase						:       Optional[int]  			=     dict(zip(a__				,     range(len(a__  )  )  )  )
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       str  			=     add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       _lowerCAmelCase						:       int  			=     re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""  )






     @property
     def     __A  (		self  ):
       return len(self.encoder  )






     def     __A  (		self  ):
       return dict(self.encoder				,     **self.added_tokens_encoder  )






     def     __A  (		self				,     a__  ):
       if token in self.cache:
         return self.cache[token]
       _lowerCAmelCase						:       Any  			=     tuple(a__  )
       _lowerCAmelCase						:       Tuple  			=     get_pairs(a__  )

       if not pairs:
         return token

       while True:
         _lowerCAmelCase						:       Optional[Any]  			=     min(a__				,     key=lambda a__  : self.bpe_ranks.get(a__				,     float("""inf"""  )  )  )
         if bigram not in self.bpe_ranks:
           break
         _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     bigram
         _lowerCAmelCase						:       Union[str, Any]  			=     []
         _lowerCAmelCase						:       List[str]  			=     0
         while i < len(a__  ):
           try:
             _lowerCAmelCase						:       str  			=     word.index(a__				,     a__  )
           except ValueError:
             new_word.extend(word[i:]  )
             break
           else:
             new_word.extend(word[i:j]  )
             _lowerCAmelCase						:       Tuple  			=     j

           if word[i] == first and i < len(a__  ) - 1 and word[i + 1] == second:
             new_word.append(first + second  )
             i += 2
           else:
             new_word.append(word[i]  )
             i += 1
         _lowerCAmelCase						:       List[Any]  			=     tuple(a__  )
         _lowerCAmelCase						:       Optional[int]  			=     new_word
         if len(a__  ) == 1:
           break
         else:
           _lowerCAmelCase						:       Any  			=     get_pairs(a__  )
       _lowerCAmelCase						:       str  			=     """ """.join(a__  )
       _lowerCAmelCase						:       str  			=     word
       return word






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     []
       for token in re.findall(self.pat				,     a__  ):
         _lowerCAmelCase						:       List[Any]  			=     """""".join(
             self.byte_encoder[b] for b in token.encode("""utf-8"""  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
         bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a__  ).split(""" """  )  )
       return bpe_tokens






     def     __A  (		self				,     a__  ):
       return self.encoder.get(a__				,     self.encoder.get(self.unk_token  )  )






     def     __A  (		self				,     a__  ):
       return self.decoder.get(a__  )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     """""".join(a__  )
       _lowerCAmelCase						:       int  			=     bytearray([self.byte_decoder[c] for c in text]  ).decode("""utf-8"""				,     errors=self.errors  )
       return text






     def     __A  (		self				,     a__				,     a__ = None  ):
       if not os.path.isdir(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
         return
       _lowerCAmelCase						:       Tuple  			=     os.path.join(
           a__				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
       _lowerCAmelCase						:       List[str]  			=     os.path.join(
           a__				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

       with open(a__				,     """w"""				,     encoding="""utf-8"""  ) as f:
         f.write(json.dumps(self.encoder				,     indent=2				,     sort_keys=a__				,     ensure_ascii=a__  ) + """\n"""  )

       _lowerCAmelCase						:       Optional[int]  			=     0
       with open(a__				,     """w"""				,     encoding="""utf-8"""  ) as writer:
         writer.write("""#version: 0.2\n"""  )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,     key=lambda a__  : kv[1]  ):
           if index != token_index:
             logger.warning(
                 F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                 """ Please check that the tokenizer is not corrupted!"""  )
             _lowerCAmelCase						:       List[str]  			=     token_index
           writer.write(""" """.join(a__  ) + """\n"""  )
           index += 1

       return vocab_file, merge_file






     def     __A  (		self				,     a__				,     a__ = None  ):
       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _lowerCAmelCase						:       Union[str, Any]  			=     [self.cls_token_id]
       _lowerCAmelCase						:       Optional[Any]  			=     [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = False  ):
       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=a__				,     token_ids_a=a__				,     already_has_special_tokens=a__  )

       if token_ids_a is None:
         return [1] + ([0] * len(a__  )) + [1]
       return [1] + ([0] * len(a__  )) + [1, 1] + ([0] * len(a__  )) + [1]






     def     __A  (		self				,     a__				,     a__ = None  ):
       _lowerCAmelCase						:       Dict  			=     [self.sep_token_id]
       _lowerCAmelCase						:       Optional[int]  			=     [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]






     def     __A  (		self				,     a__				,     a__=False				,     **a__  ):
       _lowerCAmelCase						:       int  			=     kwargs.pop("""add_prefix_space"""				,     self.add_prefix_space  )
       if (is_split_into_words or add_prefix_space) and (len(a__  ) > 0 and not text[0].isspace()):
         _lowerCAmelCase						:       int  			=     """ """ + text
       return (text, kwargs)





"""simple docstring"""




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       int	)       ->      List[str]:
  _lowerCAmelCase						:       Tuple  			=     k_size // 2
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						:       Union[str, Any]  			=     1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase	) + square(_lowerCamelCase	)) / (2 * square(_lowerCamelCase	))	)
  return g





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int	)       ->      Dict:
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     image.shape[0], image.shape[1]
  # dst image height and width
  _lowerCAmelCase						:       Optional[int]  			=     height - k_size + 1
  _lowerCAmelCase						:       Dict  			=     width - k_size + 1

  # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
  _lowerCAmelCase						:       Tuple  			=     zeros((dst_height * dst_width, k_size * k_size)	)
  _lowerCAmelCase						:       int  			=     0
  for i, j in product(range(_lowerCamelCase	)			,range(_lowerCamelCase	)	):
    _lowerCAmelCase						:       Any  			=     ravel(image[i : i + k_size, j : j + k_size]	)
    _lowerCAmelCase						:       Union[str, Any]  			=     window
    row += 1

  #  turn the kernel into shape(k*k, 1)
  _lowerCAmelCase						:       List[Any]  			=     gen_gaussian_kernel(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     ravel(_lowerCamelCase	)

  # reshape and get the dst image
  _lowerCAmelCase						:       int  			=     dot(_lowerCamelCase			,_lowerCamelCase	).reshape(_lowerCamelCase			,_lowerCamelCase	).astype(_lowerCamelCase	)

  return dst


if __name__ == "__main__":
     # read original image
     _a      :		Optional[Any]           =   imread(r'../image_data/lena.jpg')
     # turn image in gray scale value
     _a      :		Dict           =   cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     _a      :		Union[str, Any]           =   gaussian_filter(gray, 3, sigma=1)
     _a      :		List[Any]           =   gaussian_filter(gray, 5, sigma=0.8)

     # show result images
     imshow('gaussian filter with 3x3 mask', gaussianaxa)
     imshow('gaussian filter with 5x5 mask', gaussianaxa)
     waitKey()




