


'''simple docstring'''




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

import regex as re

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


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

_a      :      int       				=      logging.get_logger(__name__)

_a      :      int       				=      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_a      :      Dict       				=      {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

_a      :      List[str]       				=      {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

_a      :      str       				=      {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}





def      _a		() -> Any:






     """simple docstring"""
     __snake_case    			=	(
         list(range(ord('!'	)       ,      ord('~'	) + 1	)	) + list(range(ord('¡'	)       ,      ord('¬'	) + 1	)	) + list(range(ord('®'	)       ,      ord('ÿ'	) + 1	)	)
     )
     __snake_case    			=	bs[:]
     __snake_case    			=	0
     for b in range(2**8	):
          if b not in bs:
               bs.append(lowercase__	)
               cs.append(2**8 + n	)
               n += 1
     __snake_case    			=	[chr(lowercase__	) for n in cs]
     return dict(zip(lowercase__       ,      lowercase__	)	)





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




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE     :     Optional[Any]									=  PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE     :     Union[str, Any]									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE     :     Union[str, Any]									=  ["input_ids", "attention_mask", "token_type_ids"]
   def __init__(	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			int="replace"					,      SCREAMING_SNAKE_CASE_					:			List[str]="[CLS]"					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]="[SEP]"					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]="[SEP]"					,      SCREAMING_SNAKE_CASE_					:			Dict="[CLS]"					,      SCREAMING_SNAKE_CASE_					:			Optional[int]="[UNK]"					,      SCREAMING_SNAKE_CASE_					:			Tuple="[PAD]"					,      SCREAMING_SNAKE_CASE_					:			List[Any]="[MASK]"					,      SCREAMING_SNAKE_CASE_					:			List[str]=False					,      SCREAMING_SNAKE_CASE_					:			List[str]=False					,      **SCREAMING_SNAKE_CASE_					:			List[str]					,      )   ->       Any:
        __snake_case    			=	AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) else bos_token
        __snake_case    			=	AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) else eos_token
        __snake_case    			=	AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) else sep_token
        __snake_case    			=	AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) else cls_token
        __snake_case    			=	AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) else unk_token
        __snake_case    			=	AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __snake_case    			=	AddedToken(SCREAMING_SNAKE_CASE_					,      lstrip=SCREAMING_SNAKE_CASE_					,      rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) else mask_token

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

        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()}
        __snake_case    			=	errors  # how to handle errors in decoding
        __snake_case    			=	bytes_to_unicode()
        __snake_case    			=	{v: k for k, v in self.byte_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 bpe_merges]
        __snake_case    			=	dict(zip(SCREAMING_SNAKE_CASE_					,      range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
        __snake_case    			=	{}
        __snake_case    			=	add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __snake_case    			=	re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'			)
   @property
   # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def a     (	self					:			Union[str, Any]			)   ->       Optional[int]:
        return len(self.encoder			)
   def a     (	self					:			Any			)   ->       Optional[int]:
        return dict(self.encoder					,      **self.added_tokens_encoder			)
   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Any			)   ->       str:
        if token in self.cache:
             return self.cache[token]
        __snake_case    			=	tuple(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	get_pairs(SCREAMING_SNAKE_CASE_			)

        if not pairs:
             return token

        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_			)
        __snake_case    			=	word
        return word
   def a     (	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			List[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[List[int]] = None			)   ->       List[int]:
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __snake_case    			=	[self.cls_token_id]
        __snake_case    			=	[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep
   def a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			List[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[List[int]] = None					,      SCREAMING_SNAKE_CASE_					:			bool = False			)   ->       List[int]:
        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_					,      token_ids_a=SCREAMING_SNAKE_CASE_					,      already_has_special_tokens=SCREAMING_SNAKE_CASE_			)

        if token_ids_a is None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
        return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			List[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[List[int]] = None			)   ->       List[int]:
        __snake_case    			=	[self.sep_token_id]
        __snake_case    			=	[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			) * [0] + len(token_ids_a + sep			) * [1]
   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			List[str]			)   ->       int:
        __snake_case    			=	[]
        for token in re.findall(self.pat					,      SCREAMING_SNAKE_CASE_			):
             __snake_case    			=	''.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(SCREAMING_SNAKE_CASE_			).split(' '			)			)
        return bpe_tokens
   def a     (	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			List[Any]			)   ->       Dict:
        return self.encoder.get(SCREAMING_SNAKE_CASE_					,      self.encoder.get(self.unk_token			)			)
   def a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			int			)   ->       List[str]:
        return self.decoder.get(SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]			)   ->       Any:
        __snake_case    			=	''.join(SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'					,      errors=self.errors			)
        return text
   def a     (	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			Optional[str] = None			)   ->       Tuple[str]:
        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
   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Any=False					,      **SCREAMING_SNAKE_CASE_					:			int			)   ->       Dict:
        __snake_case    			=	kwargs.pop('add_prefix_space'					,      self.add_prefix_space			)
        if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_			) > 0 and not text[0].isspace()):
             __snake_case    			=	' ' + text
        return (text, kwargs)





   def a     (	self					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			"Conversation"			)   ->       List[int]:
        __snake_case    			=	[]
        for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_					,      add_special_tokens=SCREAMING_SNAKE_CASE_			) + [self.eos_token_id]			)
        if len(SCREAMING_SNAKE_CASE_			) > self.model_max_length:
             __snake_case    			=	input_ids[-self.model_max_length :]
        return input_ids








'''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")




