






import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__a					:				Union[str, Any]      =      logging.get_logger(__name__)

__a					:				Any      =      {
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

__a					:				Dict      =      {
    "source_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
    },
    "target_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
    },
    "vocab": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
    },
    "tokenizer_config_file": {
        "Helsinki-NLP/opus-mt-en-de": (
            "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
        )
    },
}

__a					:				Tuple      =      {"Helsinki-NLP/opus-mt-en-de": 512}
__a					:				Optional[Any]      =      {}


class 					__lowercase					(		lowercase_     ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE        = VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE        = PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE        = PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE        = ["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE        = re.compile(">>.+<<"     )  # type: re.Pattern




     def __init__( self				:	str       ,		UpperCamelCase_				:	int       ,		UpperCamelCase_				:	Optional[int]       ,		UpperCamelCase_				:	Optional[Any]       ,		UpperCamelCase_				:	List[Any]=None       ,		UpperCamelCase_				:	Tuple=None       ,		UpperCamelCase_				:	str=None       ,		UpperCamelCase_				:	List[str]="<unk>"       ,		UpperCamelCase_				:	Optional[int]="</s>"       ,		UpperCamelCase_				:	Optional[Any]="<pad>"       ,		UpperCamelCase_				:	List[str]=512       ,		UpperCamelCase_				:	Optional[Dict[str, Any]] = None       ,		UpperCamelCase_				:	List[str]=False       ,		**UpperCamelCase_				:	Optional[int]       ,		):


         """simple docstring"""



         __A   			=		{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
             source_lang=lowercase_       ,		target_lang=lowercase_       ,		unk_token=lowercase_       ,		eos_token=lowercase_       ,		pad_token=lowercase_       ,		model_max_length=lowercase_       ,		sp_model_kwargs=self.sp_model_kwargs       ,		target_vocab_file=lowercase_       ,		separate_vocabs=lowercase_       ,		**lowercase_       ,		)
         assert Path(lowercase_ ).exists(), F"cannot find spm source {source_spm}"

         __A   			=		separate_vocabs
         __A   			=		load_json(lowercase_ )
         if self.unk_token not in self.encoder:
             raise KeyError("""<unk> token must be in vocab""" )
         assert self.pad_token in self.encoder

         if separate_vocabs:
             __A   			=		load_json(lowercase_ )
             __A   			=		{v: k for k, v in self.target_encoder.items()}
             __A   			=		[]
         else:
             __A   			=		{v: k for k, v in self.encoder.items()}
             __A   			=		[k for k in self.encoder if k.startswith(""">>""" ) and k.endswith("""<<""" )]

         __A   			=		source_lang
         __A   			=		target_lang
         __A   			=		[source_spm, target_spm]

         # load SentencePiece model for pre-processing
         __A   			=		load_spm(lowercase_       ,		self.sp_model_kwargs )
         __A   			=		load_spm(lowercase_       ,		self.sp_model_kwargs )
         __A   			=		self.spm_source
         __A   			=		self.encoder

         # Multilingual target side: default to using first supported language code.

         self._setup_normalizer()




     def 						lowerCAmelCase_   ( self				:	Tuple ):


         """simple docstring"""



         try:
             from sacremoses import MosesPunctNormalizer

             __A   			=		MosesPunctNormalizer(self.source_lang ).normalize
         except (ImportError, FileNotFoundError):
             warnings.warn("""Recommended: pip install sacremoses.""" )
             __A   			=		lambda UpperCamelCase_ : x




     def 						lowerCAmelCase_   ( self				:	Tuple       ,		UpperCamelCase_				:	str ):


         """simple docstring"""



         return self.punc_normalizer(lowercase_ ) if x else ""




     def 						lowerCAmelCase_   ( self				:	Tuple       ,		UpperCamelCase_				:	List[str] ):


         """simple docstring"""



         return self.current_encoder.get(lowercase_       ,		self.current_encoder[self.unk_token] )




     def 						lowerCAmelCase_   ( self				:	List[Any]       ,		UpperCamelCase_				:	str ):


         """simple docstring"""



         __A   			=		self.language_code_re.match(lowercase_ )
         __A   			=		[match.group(0 )] if match else []
         return code, self.language_code_re.sub(""""""       ,		lowercase_ )




     def 						lowerCAmelCase_   ( self				:	List[str]       ,		UpperCamelCase_				:	str ):


         """simple docstring"""



         __A			,		__A   			=		self.remove_language_code(lowercase_ )
         __A   			=		self.current_spm.encode(lowercase_       ,		out_type=lowercase_ )
         return code + pieces




     def 						lowerCAmelCase_   ( self				:	int       ,		UpperCamelCase_				:	int ):


         """simple docstring"""



         return self.decoder.get(lowercase_       ,		self.unk_token )




     def 						lowerCAmelCase_   ( self				:	List[str]       ,		UpperCamelCase_				:	Dict       ,		**UpperCamelCase_				:	Optional[int] ):


         """simple docstring"""



         return super().batch_decode(lowercase_       ,		**lowercase_ )




     def 						lowerCAmelCase_   ( self				:	List[Any]       ,		UpperCamelCase_				:	Optional[Any]       ,		**UpperCamelCase_				:	List[str] ):


         """simple docstring"""



         return super().decode(lowercase_       ,		**lowercase_ )




     def 						lowerCAmelCase_   ( self				:	Optional[Any]       ,		UpperCamelCase_				:	List[str] ):


         """simple docstring"""



         __A   			=		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         __A   			=		[]
         __A   			=		""""""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += sp_model.decode_pieces(lowercase_ ) + token + " "
                 __A   			=		[]
             else:
                 current_sub_tokens.append(lowercase_ )
         out_string += sp_model.decode_pieces(lowercase_ )
         return out_string.strip()




     def 						lowerCAmelCase_   ( self				:	Union[str, Any]       ,		UpperCamelCase_				:	Any       ,		UpperCamelCase_				:	Dict=None ):


         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]




     def 						lowerCAmelCase_   ( self				:	Optional[int] ):


         """simple docstring"""



         __A   			=		self.spm_source
         __A   			=		self.encoder




     def 						lowerCAmelCase_   ( self				:	Any ):


         """simple docstring"""



         __A   			=		self.spm_target
         if self.separate_vocabs:
             __A   			=		self.target_encoder




     @property
     def 						lowerCAmelCase_   ( self				:	Any ):


         """simple docstring"""



         return len(self.encoder )




     def 						lowerCAmelCase_   ( self				:	str       ,		UpperCamelCase_				:	str       ,		UpperCamelCase_				:	Optional[str] = None ):


         """simple docstring"""



         if not os.path.isdir(lowercase_ ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
         __A   			=		[]

         if self.separate_vocabs:
             __A   			=		os.path.join(
                 lowercase_       ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]       ,		)
             __A   			=		os.path.join(
                 lowercase_       ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]       ,		)
             save_json(self.encoder       ,		lowercase_ )
             save_json(self.target_encoder       ,		lowercase_ )
             saved_files.append(lowercase_ )
             saved_files.append(lowercase_ )
         else:
             __A   			=		os.path.join(
                 lowercase_       ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""] )
             save_json(self.encoder       ,		lowercase_ )
             saved_files.append(lowercase_ )

         for spm_save_filename, spm_orig_path, spm_model in zip(
             [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]       ,		self.spm_files       ,		[self.spm_source, self.spm_target]       ,		):
             __A   			=		os.path.join(
                 lowercase_       ,		(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename )
             if os.path.abspath(lowercase_ ) != os.path.abspath(lowercase_ ) and os.path.isfile(lowercase_ ):
                 copyfile(lowercase_       ,		lowercase_ )
                 saved_files.append(lowercase_ )
             elif not os.path.isfile(lowercase_ ):
                 with open(lowercase_       ,		"""wb""" ) as fi:
                     __A   			=		spm_model.serialized_model_proto()
                     fi.write(lowercase_ )
                 saved_files.append(lowercase_ )

         return tuple(lowercase_ )




     def 						lowerCAmelCase_   ( self				:	Any ):


         """simple docstring"""



         return self.get_src_vocab()




     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



         return dict(self.encoder       ,		**self.added_tokens_encoder )




     def 						lowerCAmelCase_   ( self				:	Tuple ):


         """simple docstring"""



         return dict(self.target_encoder       ,		**self.added_tokens_decoder )




     def __getstate__( self				:	Union[str, Any] ):


         """simple docstring"""



         __A   			=		self.__dict__.copy()
         state.update(
             {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]} )
         return state




     def __setstate__( self				:	Dict       ,		UpperCamelCase_				:	Dict ):


         """simple docstring"""



         __A   			=		d

         # for backward compatibility
         if not hasattr(self       ,		"""sp_model_kwargs""" ):
             __A   			=		{}

         __A			,		__A   			=		(load_spm(lowercase_       ,		self.sp_model_kwargs ) for f in self.spm_files)
         __A   			=		self.spm_source
         self._setup_normalizer()




     def 						lowerCAmelCase_   ( self				:	List[str]       ,		*UpperCamelCase_				:	List[str]       ,		**UpperCamelCase_				:	Optional[int] ):


         """simple docstring"""



         return 1




     def 						lowerCAmelCase_   ( self				:	Dict       ,		UpperCamelCase_				:	List[str] ):


         """simple docstring"""



         __A   			=		set(self.all_special_ids )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special
         return [1 if x in all_special_ids else 0 for x in seq]





     def 						lowerCAmelCase_   ( self				:	Any       ,		UpperCamelCase_				:	List       ,		UpperCamelCase_				:	Optional[List] = None       ,		UpperCamelCase_				:	bool = False ):


         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(lowercase_ )
         elif token_ids_a is None:
             return self._special_token_mask(lowercase_ ) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a ) + [1]



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Union[str, Any]	, __lowercase      :	Optional[Any]			)  ->					sentencepiece.SentencePieceProcessor:





    """simple docstring"""

    __A   			=		sentencepiece.SentencePieceProcessor(**a__			)
    spm.Load(a__			)
    return spm



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	List[str]	, __lowercase      :	Optional[int]			)  ->					None:





    """simple docstring"""

    with open(a__	, """w"""			) as f:
        json.dump(a__	, a__	, indent=2			)






def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Dict			)  ->					Union[Dict, List]:





    """simple docstring"""

    with open(a__	, """r"""			) as f:
        return json.load(a__			)





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__   = 180


def 							lowerCAmelCase__    (       a__  ,			a__      )	->List[str]:




  '''simple docstring'''

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


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  np.zeros(a__      )

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      _UpperCamelCase				=  gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row, col - 1]
        _UpperCamelCase				=  sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row + 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col]
        _UpperCamelCase				=  sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _UpperCamelCase				=  sobel_grad[row, col]

  return destination


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->Union[str, Any]:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      if destination[row, col] >= threshold_high:
        _UpperCamelCase				=  strong
      elif destination[row, col] <= threshold_low:
        _UpperCamelCase				=  0
      else:
        _UpperCamelCase				=  weak


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__      )	->str:




  '''simple docstring'''

  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],
        ):
          _UpperCamelCase				=  strong
        else:
          _UpperCamelCase				=  0


def 							lowerCAmelCase__    (       a__  ,			a__=15  ,			a__=30  ,			a__=128  ,			a__=255      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  img_convolve(a__  ,			gen_gaussian_kernel(9  ,			sigma=1.4      )      )
  # get the gradient and degree by sobel_filter
  _UpperCamelCase	,		_UpperCamelCase				=  sobel_filter(a__      )
  _UpperCamelCase				=  PI + np.radadeg(a__      )

  _UpperCamelCase				=  suppress_non_maximum(image.shape  ,			a__  ,			a__      )

  detect_high_low_threshold(
      image.shape  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  track_edge(image.shape  ,			a__  ,			a__  ,			a__      )

  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)




