'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 _A		:      Optional[int]						   =None


_A		:      int						   =logging.get_logger(__name__)

_A		:      Tuple						   ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_A		:      Dict						   ={
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

_A		:      Union[str, Any]						   ={
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# fmt: off
_A		:      Dict						   =['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']




class _lowerCAmelCase			(	snake_case					):
      UpperCAmelCase_	:						Tuple	 =       VOCAB_FILES_NAMES
      UpperCAmelCase_	:						Dict	 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_	:						List[str]	 =       PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_	:						str	 =       ["input_ids", "attention_mask"]
      UpperCAmelCase_	:						str	 =       MBartaaTokenizer

      UpperCAmelCase_	:						List[int]	 =       []
      UpperCAmelCase_	:						List[int]	 =       []







      def __init__(  self  ,    lowercase=None  ,    lowercase=None  ,    lowercase=None  ,    lowercase=None  ,    lowercase="</s>"  ,    lowercase="</s>"  ,    lowercase="<s>"  ,    lowercase="<unk>"  ,    lowercase="<pad>"  ,    lowercase="<mask>"  ,    **lowercase  ,    ):
        # Mask token behave like a normal word, i.e. include the space before it
        lowercase				:							List[str]									=   AddedToken(lowercase  ,    lstrip=lowercase  ,    rstrip=lowercase     ) if isinstance(lowercase  ,    lowercase     ) else mask_token

        lowercase				:							Union[str, Any]									=   kwargs.get('''additional_special_tokens'''  ,    []     )
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            lowercase  ,    src_lang=lowercase  ,    tgt_lang=lowercase  ,    tokenizer_file=lowercase  ,    eos_token=lowercase  ,    sep_token=lowercase  ,    cls_token=lowercase  ,    unk_token=lowercase  ,    pad_token=lowercase  ,    mask_token=lowercase  ,    **lowercase  ,    )

        lowercase				:							Tuple									=   vocab_file
        lowercase				:							str									=   False if not self.vocab_file else True

        lowercase				:							Union[str, Any]									=   {
            lang_code: self.convert_tokens_to_ids(lowercase     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase				:							Union[str, Any]									=   src_lang if src_lang is not None else '''en_XX'''
        lowercase				:							Tuple									=   tgt_lang
        lowercase				:							Any									=   self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang     )







      @property
      def      lowerCamelCase__      (  self     ):
        return self._src_lang







      @src_lang.setter
      def      lowerCamelCase__      (  self  ,    lowercase     ):
        lowercase				:							str									=   new_src_lang
        self.set_src_lang_special_tokens(self._src_lang     )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase = None     ):
        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase = "en_XX"  ,    lowercase = None  ,    lowercase = "ro_RO"  ,    **lowercase  ,    ):
        lowercase				:							int									=   src_lang
        lowercase				:							List[str]									=   tgt_lang
        return super().prepare_seqaseq_batch(lowercase  ,    lowercase  ,    **lowercase     )







      def      lowerCamelCase__      (  self     ):
        return self.set_src_lang_special_tokens(self.src_lang     )







      def      lowerCamelCase__      (  self     ):
        return self.set_tgt_lang_special_tokens(self.tgt_lang     )







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        lowercase				:							List[str]									=   self.convert_tokens_to_ids(lowercase     )
        lowercase				:							str									=   [self.cur_lang_code_id]
        lowercase				:							List[Any]									=   [self.eos_token_id]

        lowercase				:							int									=   self.convert_ids_to_tokens(self.prefix_tokens     )
        lowercase				:							Dict									=   self.convert_ids_to_tokens(self.suffix_tokens     )

        lowercase				:							List[Any]									=   processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str  ,    pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str  ,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,    self.prefix_tokens + self.suffix_tokens     )     )  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        lowercase				:							List[str]									=   self.convert_tokens_to_ids(lowercase     )
        lowercase				:							Tuple									=   [self.cur_lang_code_id]
        lowercase				:							int									=   [self.eos_token_id]

        lowercase				:							Tuple									=   self.convert_ids_to_tokens(self.prefix_tokens     )
        lowercase				:							List[Any]									=   self.convert_ids_to_tokens(self.suffix_tokens     )

        lowercase				:							str									=   processors.TemplateProcessing(
            single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str  ,    pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str  ,    special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,    self.prefix_tokens + self.suffix_tokens     )     )  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase  ,    lowercase  ,    **lowercase     ):
        if src_lang is None or tgt_lang is None:
          raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''     )
        lowercase				:							Union[str, Any]									=   src_lang
        lowercase				:							Dict									=   self(lowercase  ,    add_special_tokens=lowercase  ,    return_tensors=lowercase  ,    **lowercase     )
        lowercase				:							Tuple									=   self.convert_tokens_to_ids(lowercase     )
        lowercase				:							List[str]									=   tgt_lang_id
        return inputs







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase = None     ):
        if not self.can_save_slow_tokenizer:
          raise ValueError(
              '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
              '''tokenizer.'''     )

        if not os.path.isdir(lowercase     ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
          return
        lowercase				:							Tuple									=   os.path.join(
            lowercase  ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

        if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase     ):
          copyfile(self.vocab_file  ,    lowercase     )

        return (out_vocab_file,)
'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

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


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

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