code: 
  
  
  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:
      lowerCAmelCase__   	=				None
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  lowerCAmelCase__   	=				{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCAmelCase__   	=				{
      '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'
          ),
      },
  }
  
  lowerCAmelCase__   	=				{
      'facebook/mbart-large-50-one-to-many-mmt': 10_24,
  }
  
  # fmt: off
  lowerCAmelCase__   	=				['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__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE						    =				PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE						    =				["input_ids", "attention_mask"]
       __SCREAMING_SNAKE_CASE						    =				MBartaaTokenizer
  
       __SCREAMING_SNAKE_CASE						    =				[]
       __SCREAMING_SNAKE_CASE						    =				[]
  
  
       def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase="</s>"				,		__lowerCamelCase="</s>"				,		__lowerCamelCase="<s>"				,		__lowerCamelCase="<unk>"				,		__lowerCamelCase="<pad>"				,		__lowerCamelCase="<mask>"				,		**__lowerCamelCase				,		)    ->       str:
              # Mask token behave like a normal word, i.e. include the space before it
              _A   : List[str]       					=				AddedToken(__lowerCamelCase				,		lstrip=__lowerCamelCase				,		rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase				,		__lowerCamelCase) else mask_token
  
              _A   : List[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__(
                  __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   : str       					=				vocab_file
              _A   : Any       					=				False if not self.vocab_file else True
  
              _A   : Optional[Any]       					=				{
                  lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
              }
  
              _A   : List[str]       					=				src_lang if src_lang is not None else "en_XX"
              _A   : int       					=				tgt_lang
              _A   : Any       					=				self.lang_code_to_id[self._src_lang]
              self.set_src_lang_special_tokens(self._src_lang)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       str:
              return self._src_lang
  
  
       @src_lang.setter
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
              _A   : Optional[Any]       					=				new_src_lang
              self.set_src_lang_special_tokens(self._src_lang)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       List[int]:
              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				,		__lowerCamelCase				,		__lowerCamelCase = "en_XX"				,		__lowerCamelCase = None				,		__lowerCamelCase = "ro_RO"				,		**__lowerCamelCase				,		)    ->       BatchEncoding:
              _A   : int       					=				src_lang
              _A   : List[str]       					=				tgt_lang
              return super().prepare_seqaseq_batch(__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              return self.set_src_lang_special_tokens(self.src_lang)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              return self.set_tgt_lang_special_tokens(self.tgt_lang)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       None:
              _A   : List[Any]       					=				self.convert_tokens_to_ids(__lowerCamelCase)
              _A   : Union[str, Any]       					=				[self.cur_lang_code_id]
              _A   : List[str]       					=				[self.eos_token_id]
  
              _A   : Tuple       					=				self.convert_ids_to_tokens(self.prefix_tokens)
              _A   : List[str]       					=				self.convert_ids_to_tokens(self.suffix_tokens)
  
              _A   : List[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				,		__lowerCamelCase)    ->       None:
              _A   : Union[str, Any]       					=				self.convert_tokens_to_ids(__lowerCamelCase)
              _A   : Dict       					=				[self.cur_lang_code_id]
              _A   : Optional[int]       					=				[self.eos_token_id]
  
              _A   : List[str]       					=				self.convert_ids_to_tokens(self.prefix_tokens)
              _A   : Tuple       					=				self.convert_ids_to_tokens(self.suffix_tokens)
  
              _A   : int       					=				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				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       int:
              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   : Optional[int]       					=				src_lang
              _A   : Union[str, Any]       					=				self(__lowerCamelCase				,		add_special_tokens=__lowerCamelCase				,		return_tensors=__lowerCamelCase				,		**__lowerCamelCase)
              _A   : int       					=				self.convert_tokens_to_ids(__lowerCamelCase)
              _A   : List[Any]       					=				tgt_lang_id
              return inputs
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None)    ->       Tuple[str]:
              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   : List[str]       					=				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,)
style_context: 
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  lowerCAmelCase__   	=				1_80
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
         _A   : int       					=				k_size // 2
         _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
         _A   : Union[str, Any]       					=				(
             1
             / (2 * np.pi * sigma)
             * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
         )
         return g
  def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
         _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)
  
         for row in range(1       ,							image_shape[0] - 1		):
                for col in range(1       ,							image_shape[1] - 1		):
                       _A   : Dict       					=				gradient_direction[row, col]
  
                       if (
                           0 <= direction < PI / 8
                           or 15 * PI / 8 <= direction <= 2 * PI
                           or 7 * PI / 8 <= direction <= 9 * PI / 8
                       ):
                              _A   : Dict       					=				sobel_grad[row, col - 1]
                              _A   : Any       					=				sobel_grad[row, col + 1]
                              if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           PI / 8 <= direction < 3 * PI / 8
                           or 9 * PI / 8 <= direction < 11 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row + 1, col - 1]
                              _A   : str       					=				sobel_grad[row - 1, col + 1]
                              if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
                       elif (
                           3 * PI / 8 <= direction < 5 * PI / 8
                           or 11 * PI / 8 <= direction < 13 * PI / 8
                       ):
                              _A   : Any       					=				sobel_grad[row - 1, col]
                              _A   : int       					=				sobel_grad[row + 1, col]
                              if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                     _A   : Any       					=				sobel_grad[row, col]
  
                       elif (
                           5 * PI / 8 <= direction < 7 * PI / 8
                           or 13 * PI / 8 <= direction < 15 * PI / 8
                       ):
                              _A   : List[str]       					=				sobel_grad[row - 1, col - 1]
                              _A   : Optional[Any]       					=				sobel_grad[row + 1, col + 1]
                              if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                     _A   : List[Any]       					=				sobel_grad[row, col]
  
         return destination
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any		):
         for row in range(1       ,							image_shape[0] - 1		):
                for col in range(1       ,							image_shape[1] - 1		):
                       if destination[row, col] >= threshold_high:
                              _A   : Optional[int]       					=				strong
                       elif destination[row, col] <= threshold_low:
                              _A   : Optional[int]       					=				0
                       else:
                              _A   : Union[str, Any]       					=				weak
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
         for row in range(1       ,							image_shape[0]		):
                for col in range(1       ,							image_shape[1]		):
                       if destination[row, col] == weak:
                              if 255 in (
                                  destination[row, col + 1],
                                  destination[row, col - 1],
                                  destination[row - 1, col],
                                  destination[row + 1, col],
                                  destination[row - 1, col - 1],
                                  destination[row + 1, col - 1],
                                  destination[row - 1, col + 1],
                                  destination[row + 1, col + 1],
                              ):
                                     _A   : str       					=				strong
                              else:
                                     _A   : Any       					=				0
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
         # gaussian_filter
         _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
         # get the gradient and degree by sobel_filter
         _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
         _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)
  
         _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         detect_high_low_threshold(
             image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
  
         return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      lowerCAmelCase__   	=				cva.imread(R'../image_data/lena.jpg', 0)
      # canny edge detection
      lowerCAmelCase__   	=				canny(lena)
      cva.imshow('canny', canny_destination)
      cva.waitKey(0)
  