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

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A   :     Any       =				logging.get_logger(__name__)


A   :     int       =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

A   :     Any       =				{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

A   :     Optional[Any]       =				{"facebook/blenderbot_small-90M": 5_12}


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     set()
      SCREAMING_SNAKE_CASE_      =     word[0]
      for char in word[1:]:
            pairs.add((prev_char, char) )
            SCREAMING_SNAKE_CASE_      =     char

      SCREAMING_SNAKE_CASE_      =     set(__UpperCamelCase )
      return pairs





class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  VOCAB_FILES_NAMES
  lowerCamelCase__   =  PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase__   =  ['''input_ids''', '''attention_mask''']







  def __init__(							self       :				Dict     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Optional[Any]="__start__"     ,   __magic_name__       :				Union[str, Any]="__end__"     ,   __magic_name__       :				int="__unk__"     ,   __magic_name__       :				Any="__null__"     ,   **__magic_name__       :				List[str]     ,   )     ->		str:
        super().__init__(unk_token=__magic_name__     ,   bos_token=__magic_name__     ,   eos_token=__magic_name__     ,   pad_token=__magic_name__     ,   **__magic_name__      )

        with open(__magic_name__     ,   encoding="utf-8"      ) as vocab_handle:
              SCREAMING_SNAKE_CASE_      =     json.load(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {v: k for k, v in self.encoder.items()}
        with open(__magic_name__     ,   encoding="utf-8"      ) as merges_handle:
              SCREAMING_SNAKE_CASE_      =     merges_handle.read().split("\n"      )[1:-1]
        SCREAMING_SNAKE_CASE_      =     [tuple(merge.split()      ) for merge in merges]
        SCREAMING_SNAKE_CASE_      =     dict(zip(__magic_name__     ,   range(len(__magic_name__      )      )      )      )
        SCREAMING_SNAKE_CASE_      =     {}







  @property
  def       __A   (							self       :				List[Any]      )     ->		int:
        return len(self.encoder      )







  def       __A   (							self       :				Optional[Any]      )     ->		Dict:
        return dict(self.encoder     ,   **self.added_tokens_encoder      )







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				str      )     ->		str:
        if token in self.cache:
              return self.cache[token]
        SCREAMING_SNAKE_CASE_      =     re.sub("([.,!?()])"     ,   r" \1"     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     re.sub("(')"     ,   r" \1 "     ,   __magic_name__      )
        SCREAMING_SNAKE_CASE_      =     re.sub(r"\s{2,}"     ,   " "     ,   __magic_name__      )
        if "\n" in token:
              SCREAMING_SNAKE_CASE_      =     token.replace("\n"     ,   " __newln__"      )

        SCREAMING_SNAKE_CASE_      =     token.split(" "      )
        SCREAMING_SNAKE_CASE_      =     []
        for token in tokens:
              if not len(__magic_name__      ):
                    continue

              SCREAMING_SNAKE_CASE_      =     token.lower()
              SCREAMING_SNAKE_CASE_      =     tuple(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     tuple(list(word[:-1]      ) + [word[-1] + "</w>"]      )
              SCREAMING_SNAKE_CASE_      =     get_pairs(__magic_name__      )

              if not pairs:
                    words.append(__magic_name__      )
                    continue

              while True:
                    SCREAMING_SNAKE_CASE_      =     min(__magic_name__     ,   key=lambda __magic_name__      : self.bpe_ranks.get(__magic_name__     ,   float("inf"      )      )      )
                    if bigram not in self.bpe_ranks:
                          break
                    SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     bigram
                    SCREAMING_SNAKE_CASE_      =     []
                    SCREAMING_SNAKE_CASE_      =     0

                    while i < len(__magic_name__      ):
                          try:
                                SCREAMING_SNAKE_CASE_      =     word.index(__magic_name__     ,   __magic_name__      )
                                new_word.extend(word[i:j]      )
                                SCREAMING_SNAKE_CASE_      =     j
                          except ValueError:
                                new_word.extend(word[i:]      )
                                break

                          if word[i] == first and i < len(__magic_name__      ) - 1 and word[i + 1] == second:
                                new_word.append(first + second      )
                                i += 2
                          else:
                                new_word.append(word[i]      )
                                i += 1
                    SCREAMING_SNAKE_CASE_      =     tuple(__magic_name__      )
                    SCREAMING_SNAKE_CASE_      =     new_word
                    if len(__magic_name__      ) == 1:
                          break
                    else:
                          SCREAMING_SNAKE_CASE_      =     get_pairs(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     "@@ ".join(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     word[:-4]

              SCREAMING_SNAKE_CASE_      =     word
              words.append(__magic_name__      )
        return " ".join(__magic_name__      )







  def       __A   (							self       :				Any     ,   __magic_name__       :				str      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     []

        SCREAMING_SNAKE_CASE_      =     re.findall(r"\S+\n?"     ,   __magic_name__      )

        for token in words:
              split_tokens.extend(list(self.bpe(__magic_name__      ).split(" "      )      )      )
        return split_tokens







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				str      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     token.lower()
        return self.encoder.get(__magic_name__     ,   self.encoder.get(self.unk_token      )      )







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				int      )     ->		str:
        return self.decoder.get(__magic_name__     ,   self.unk_token      )







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				List[str]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     " ".join(__magic_name__      ).replace("@@ "     ,   ""      ).strip()
        return out_string







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

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

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

        return vocab_file, merge_file



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    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

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

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


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

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				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()


