


'''simple docstring'''




import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
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      :      List[Any]       				=      {
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

_a      :      Optional[Any]       				=      {
    "openai-gpt": 512,
}

def      _a		(lowercase__ :      Dict	) -> Union[str, Any]:






     """simple docstring"""
     __snake_case    			=	text.strip()
     if not text:
          return []
     __snake_case    			=	text.split()
     return tokens




class  _lowercase   (  lowerCamelCase__       ):
   def __init__(	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[str]=True					,      SCREAMING_SNAKE_CASE_					:			Tuple=None					,      SCREAMING_SNAKE_CASE_					:			Any=True					,      SCREAMING_SNAKE_CASE_					:			int=None					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=True					,      )   ->       Union[str, Any]:
        if never_split is None:
             __snake_case    			=	[]
        __snake_case    			=	do_lower_case
        __snake_case    			=	set(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	tokenize_chinese_chars
        __snake_case    			=	strip_accents
        __snake_case    			=	do_split_on_punc
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=None			)   ->       Optional[Any]:
        __snake_case    			=	self.never_split.union(set(SCREAMING_SNAKE_CASE_			)			) if never_split else self.never_split
        __snake_case    			=	self._clean_text(SCREAMING_SNAKE_CASE_			)

        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        if self.tokenize_chinese_chars:
             __snake_case    			=	self._tokenize_chinese_chars(SCREAMING_SNAKE_CASE_			)
        # prevents treating the same character with different unicode codepoints as different characters
        __snake_case    			=	unicodedata.normalize('NFC'					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	whitespace_tokenize(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	[]
        for token in orig_tokens:
             if token not in never_split:
                  if self.do_lower_case:
                       __snake_case    			=	token.lower()
                       if self.strip_accents is not False:
                            __snake_case    			=	self._run_strip_accents(SCREAMING_SNAKE_CASE_			)
                  elif self.strip_accents:
                       __snake_case    			=	self._run_strip_accents(SCREAMING_SNAKE_CASE_			)
             split_tokens.extend(self._run_split_on_punc(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)			)

        __snake_case    			=	whitespace_tokenize(' '.join(SCREAMING_SNAKE_CASE_			)			)
        return output_tokens
   def a     (	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Dict			)   ->       str:
        __snake_case    			=	unicodedata.normalize('NFD'					,      SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	[]
        for char in text:
             __snake_case    			=	unicodedata.category(SCREAMING_SNAKE_CASE_			)
             if cat == "Mn":
                  continue
             output.append(SCREAMING_SNAKE_CASE_			)
        return "".join(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=None			)   ->       List[str]:
        if not self.do_split_on_punc or (never_split is not None and text in never_split):
             return [text]
        __snake_case    			=	list(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	0
        __snake_case    			=	True
        __snake_case    			=	[]
        while i < len(SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	chars[i]
             if _is_punctuation(SCREAMING_SNAKE_CASE_			):
                  output.append([char]			)
                  __snake_case    			=	True
             else:
                  if start_new_word:
                       output.append([]			)
                  __snake_case    			=	False
                  output[-1].append(SCREAMING_SNAKE_CASE_			)
             i += 1

        return ["".join(SCREAMING_SNAKE_CASE_			) for x in output]
   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       Union[str, Any]:
        __snake_case    			=	[]
        for char in text:
             __snake_case    			=	ord(SCREAMING_SNAKE_CASE_			)
             if self._is_chinese_char(SCREAMING_SNAKE_CASE_			):
                  output.append(' '			)
                  output.append(SCREAMING_SNAKE_CASE_			)
                  output.append(' '			)
             else:
                  output.append(SCREAMING_SNAKE_CASE_			)
        return "".join(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       Tuple:
        # This defines a "chinese character" as anything in the CJK Unicode block:
        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
        #
        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
        # despite its name. The modern Korean Hangul alphabet is a different block,
        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
        # space-separated words, so they are not treated specially and handled
        # like the all of the other languages.
        if (
            (cp >= 0x4_E00 and cp <= 0x9_FFF)
            or (cp >= 0x3_400 and cp <= 0x4_DBF)  #
            or (cp >= 0x20_000 and cp <= 0x2A_6DF)  #
            or (cp >= 0x2A_700 and cp <= 0x2B_73F)  #
            or (cp >= 0x2B_740 and cp <= 0x2B_81F)  #
            or (cp >= 0x2B_820 and cp <= 0x2C_EAF)  #
            or (cp >= 0xF_900 and cp <= 0xF_AFF)
            or (cp >= 0x2F_800 and cp <= 0x2F_A1F)  #
        ):  #
             return True

        return False





   def a     (	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Tuple			)   ->       List[Any]:
        __snake_case    			=	[]
        for char in text:
             __snake_case    			=	ord(SCREAMING_SNAKE_CASE_			)
             if cp == 0 or cp == 0xF_FFD or _is_control(SCREAMING_SNAKE_CASE_			):
                  continue
             if _is_whitespace(SCREAMING_SNAKE_CASE_			):
                  output.append(' '			)
             else:
                  output.append(SCREAMING_SNAKE_CASE_			)
        return "".join(SCREAMING_SNAKE_CASE_			)




def      _a		(lowercase__ :      Dict	) -> Optional[Any]:






     """simple docstring"""
     __snake_case    			=	set()
     __snake_case    			=	word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)	)
          __snake_case    			=	char
     return pairs

def      _a		(lowercase__ :      List[str]	) -> List[str]:






     """simple docstring"""
     __snake_case    			=	text.replace('—'       ,      '-'	)
     __snake_case    			=	text.replace('–'       ,      '-'	)
     __snake_case    			=	text.replace('―'       ,      '-'	)
     __snake_case    			=	text.replace('…'       ,      '...'	)
     __snake_case    			=	text.replace('´'       ,      '\''	)
     __snake_case    			=	re.sub(r'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'       ,      r' \1 '       ,      _UpperCAmelCase	)
     __snake_case    			=	re.sub(r'\s*\n\s*'       ,      ' \n '       ,      _UpperCAmelCase	)
     __snake_case    			=	re.sub(r'[^\S\n]+'       ,      ' '       ,      _UpperCAmelCase	)
     return text.strip()




class  _lowercase   (  lowerCamelCase__       ):
   _SCREAMING_SNAKE_CASE     :     List[Any]									=  VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE     :     str									=  PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE     :     Union[str, Any]									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE     :     Dict									=  ["input_ids", "attention_mask"]
   def __init__(	self					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Tuple="<unk>"					,      **SCREAMING_SNAKE_CASE_					:			int			)   ->       Any:
        super().__init__(unk_token=SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)

        try:
             import ftfy
             from spacy.lang.en import English

             __snake_case    			=	English()
             __snake_case    			=	_nlp.tokenizer
             __snake_case    			=	ftfy.fix_text
        except ImportError:
             logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.'			)
             __snake_case    			=	BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	None

        with open(SCREAMING_SNAKE_CASE_					,      encoding='utf-8'			) as vocab_handle:
             __snake_case    			=	json.load(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	{v: k for k, v in self.encoder.items()}
        with open(SCREAMING_SNAKE_CASE_					,      encoding='utf-8'			) as merges_handle:
             __snake_case    			=	merges_handle.read().split('\n'			)[1:-1]
        __snake_case    			=	[tuple(merge.split()			) for merge in merges]
        __snake_case    			=	dict(zip(SCREAMING_SNAKE_CASE_					,      range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
        __snake_case    			=	{}
   @property
   def a     (	self					:			int			)   ->       str:
        return True
   @property
   def a     (	self					:			Union[str, Any]			)   ->       Optional[int]:
        return len(self.encoder			)
   def a     (	self					:			Union[str, Any]			)   ->       Optional[int]:
        return dict(self.encoder					,      **self.added_tokens_encoder			)
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[Any]			)   ->       Tuple:
        __snake_case    			=	tuple(token[:-1]			) + (token[-1] + '</w>',)
        if token in self.cache:
             return self.cache[token]
        __snake_case    			=	get_pairs(SCREAMING_SNAKE_CASE_			)

        if not pairs:
             return token + "</w>"

        while True:
             __snake_case    			=	min(SCREAMING_SNAKE_CASE_					,      key=lambda SCREAMING_SNAKE_CASE_			: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_					,      float('inf'			)			)			)
             if bigram not in self.bpe_ranks:
                  break
             __snake_case			,					__snake_case    			=	bigram
             __snake_case    			=	[]
             __snake_case    			=	0
             while i < len(SCREAMING_SNAKE_CASE_			):
                  try:
                       __snake_case    			=	word.index(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
                  except ValueError:
                       new_word.extend(word[i:]			)
                       break
                  else:
                       new_word.extend(word[i:j]			)
                       __snake_case    			=	j

                  if word[i] == first and i < len(SCREAMING_SNAKE_CASE_			) - 1 and word[i + 1] == second:
                       new_word.append(first + second			)
                       i += 2
                  else:
                       new_word.append(word[i]			)
                       i += 1
             __snake_case    			=	tuple(SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	new_word
             if len(SCREAMING_SNAKE_CASE_			) == 1:
                  break
             else:
                  __snake_case    			=	get_pairs(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	' '.join(SCREAMING_SNAKE_CASE_			)
        if word == "\n  </w>":
             __snake_case    			=	'\n</w>'
        __snake_case    			=	word
        return word
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       Tuple:
        __snake_case    			=	[]
        if self.fix_text is None:
             # Using BERT's BasicTokenizer
             __snake_case    			=	self.nlp.tokenize(SCREAMING_SNAKE_CASE_			)
             for token in text:
                  split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_			).split(' '			)			)			)
        else:
             # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
             __snake_case    			=	self.nlp(text_standardize(self.fix_text(SCREAMING_SNAKE_CASE_			)			)			)
             for token in text:
                  split_tokens.extend(list(self.bpe(token.text.lower()			).split(' '			)			)			)
        return split_tokens
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[str]			)   ->       Optional[Any]:
        return self.encoder.get(SCREAMING_SNAKE_CASE_					,      self.encoder.get(self.unk_token			)			)
   def a     (	self					:			Any					,      SCREAMING_SNAKE_CASE_					:			List[Any]			)   ->       str:
        return self.decoder.get(SCREAMING_SNAKE_CASE_					,      self.unk_token			)
   def a     (	self					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			int			)   ->       Any:
        __snake_case    			=	''.join(SCREAMING_SNAKE_CASE_			).replace('</w>'					,      ' '			).strip()
        return out_string





   def a     (	self					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			Optional[str] = None			)   ->       Dict:
        if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
             return
        __snake_case    			=	os.path.join(
            SCREAMING_SNAKE_CASE_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
        __snake_case    			=	os.path.join(
            SCREAMING_SNAKE_CASE_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

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

        __snake_case    			=	0
        with open(SCREAMING_SNAKE_CASE_					,      '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 SCREAMING_SNAKE_CASE_			: 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!'			)
                       __snake_case    			=	token_index
                  writer.write(' '.join(SCREAMING_SNAKE_CASE_			) + '\n'			)
                  index += 1

        return vocab_file, merge_file








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]	):
          for y in range(image.shape[0]	):
               __snake_case    			=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __snake_case    			=	int(summation == 5	)
     return output


# kernel to be applied
_a      :      Tuple       				=      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
       # read original image
       _a      :      Any       				=      np.array(Image.open(R"..\image_data\lena.jpg"))
       # Apply erosion operation to a binary image
       _a      :      List[Any]       				=      erosion(grayabinary(rgbagray(image)), structuring_element)
       # Save the output image
       _a      :      int       				=      Image.fromarray(output).convert("RGB")
       pil_img.save("result_erosion.png")




