




'''simple docstring'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


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

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)








'''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		:  str   			=			None


a		:  List[str]   			=			logging.get_logger(__name__)

a		:  Dict   			=			{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:  Optional[Any]   			=			{
    '''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		:  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     UpperCamelCase					(  snake_case__					):
  a    :str      					=				VOCAB_FILES_NAMES
  a    :int      					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a    :Dict      					=				PRETRAINED_VOCAB_FILES_MAP
  a    :List[str]      					=				['input_ids', 'attention_mask']
  a    :Optional[Any]      					=				MBartaaTokenizer

  a    :List[int]      					=				[]
  a    :List[int]      					=				[]



  def __init__( self		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="</s>"		,    __lowerCAmelCase="<s>"		,    __lowerCAmelCase="<unk>"		,    __lowerCAmelCase="<pad>"		,    __lowerCAmelCase="<mask>"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[str]       					=			AddedToken(__lowerCAmelCase		,    lstrip=__lowerCAmelCase		,    rstrip=__lowerCAmelCase   ) if isinstance(__lowerCAmelCase		,    __lowerCAmelCase   ) else mask_token

      _a							:       List[str]       					=			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__(
          __lowerCAmelCase		,    src_lang=__lowerCAmelCase		,    tgt_lang=__lowerCAmelCase		,    tokenizer_file=__lowerCAmelCase		,    eos_token=__lowerCAmelCase		,    sep_token=__lowerCAmelCase		,    cls_token=__lowerCAmelCase		,    unk_token=__lowerCAmelCase		,    pad_token=__lowerCAmelCase		,    mask_token=__lowerCAmelCase		,    **__lowerCAmelCase		,    )

      _a							:       Optional[int]       					=			vocab_file
      _a							:       int       					=			False if not self.vocab_file else True

      _a							:       str       					=			{
          lang_code: self.convert_tokens_to_ids(__lowerCAmelCase   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      _a							:       Dict       					=			src_lang if src_lang is not None else """en_XX"""
      _a							:       Tuple       					=			tgt_lang
      _a							:       Tuple       					=			self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang   )



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      return self._src_lang



  @src_lang.setter
  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       int       					=			new_src_lang
      self.set_src_lang_special_tokens(self._src_lang   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      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 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = "en_XX"		,    __lowerCAmelCase = None		,    __lowerCAmelCase = "ro_RO"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			src_lang
      _a							:       Any       					=			tgt_lang
      return super().prepare_seqaseq_batch(__lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_src_lang_special_tokens(self.src_lang   )



  def 						a_       ( self   ):







      '''simple docstring'''


      return self.set_tgt_lang_special_tokens(self.tgt_lang   )



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Union[str, Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			[self.cur_lang_code_id]
      _a							:       int       					=			[self.eos_token_id]

      _a							:       Any       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       Dict       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       Union[str, 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 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Optional[int]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       List[str]       					=			[self.cur_lang_code_id]
      _a							:       Tuple       					=			[self.eos_token_id]

      _a							:       List[Any]       					=			self.convert_ids_to_tokens(self.prefix_tokens   )
      _a							:       int       					=			self.convert_ids_to_tokens(self.suffix_tokens   )

      _a							:       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 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      if src_lang is None or tgt_lang is None:
          raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""   )
      _a							:       List[Any]       					=			src_lang
      _a							:       List[str]       					=			self(__lowerCAmelCase		,    add_special_tokens=__lowerCAmelCase		,    return_tensors=__lowerCAmelCase		,    **__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			self.convert_tokens_to_ids(__lowerCAmelCase   )
      _a							:       Tuple       					=			tgt_lang_id
      return inputs



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      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(__lowerCAmelCase   ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
          return
      _a							:       Union[str, Any]       					=			os.path.join(
          __lowerCAmelCase		,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

      return (out_vocab_file,)


