




'''simple docstring'''







import json
import os
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


lowercase      :   Any      =       logging.get_logger(__name__)

lowercase      :   List[str]      =       """▁"""

lowercase      :   Union[str, Any]      =       {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

lowercase      :   Union[str, Any]      =       {
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

lowercase      :   Any      =       {
    """facebook/s2t-small-librispeech-asr""": 1024,
}

lowercase      :   int      =       ["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

lowercase      :   Dict      =       {"""mustc""": MUSTC_LANGS}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					VOCAB_FILES_NAMES
     lowerCAmelCase_  :     Union[str, Any]          =					PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_  :     Optional[int]          =					MAX_MODEL_INPUT_SIZES
     lowerCAmelCase_  :     List[Any]          =					["""input_ids""", """attention_mask"""]

     lowerCAmelCase_  :     List[int]          =					[]







     def __init__(				self							,__a							,__a							,__a="<s>"							,__a="</s>"							,__a="<pad>"							,__a="<unk>"							,__a=False							,__a=False							,__a=None							,__a=None							,__a = None							,**__a							,)      ->				None:
          snake_case			:      str        =			{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=__a							,eos_token=__a							,unk_token=__a							,pad_token=__a							,do_upper_case=__a							,do_lower_case=__a							,tgt_lang=__a							,lang_codes=__a							,sp_model_kwargs=self.sp_model_kwargs							,**__a							,)
          snake_case			:      int        =			do_upper_case
          snake_case			:      List[Any]        =			do_lower_case

          snake_case			:      Optional[int]        =			load_json(__a							)
          snake_case			:      Optional[int]        =			{v: k for k, v in self.encoder.items()}
          snake_case			:      List[str]        =			spm_file
          snake_case			:      Optional[Any]        =			load_spm(__a							,self.sp_model_kwargs							)

          if lang_codes is not None:
               snake_case			:      List[Any]        =			lang_codes
               snake_case			:      Optional[Any]        =			LANGUAGES[lang_codes]
               snake_case			:      Optional[Any]        =			[F'''<lang:{lang}>''' for lang in self.langs]
               snake_case			:      List[Any]        =			{lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''							) for lang in self.langs}

               snake_case			:      int        =			self.lang_tokens
               snake_case			:      str        =			tgt_lang if tgt_lang is not None else self.langs[0]

               self.set_tgt_lang_special_tokens(self._tgt_lang							)
          else:
               snake_case			:      List[str]        =			{}







     @property
     def 			snake_case_      (				self							)      ->				int:
          return len(self.encoder							)







     @property
     def 			snake_case_      (				self							)      ->				str:
          return self._tgt_lang







     @tgt_lang.setter
     def 			snake_case_      (				self							,__a							)      ->				None:
          snake_case			:      int        =			new_tgt_lang
          self.set_tgt_lang_special_tokens(__a							)







     def 			snake_case_      (				self							,__a							)      ->				None:
          snake_case			:      str        =			self.lang_code_to_id[tgt_lang]
          snake_case			:      Optional[int]        =			[lang_code_id]







     def 			snake_case_      (				self							,__a							)      ->				List[str]:
          return self.sp_model.encode(__a							,out_type=__a							)







     def 			snake_case_      (				self							,__a							)      ->				Any:
          return self.encoder.get(__a							,self.encoder[self.unk_token]							)







     def 			snake_case_      (				self							,__a							)      ->				str:
          return self.decoder.get(__a							,self.unk_token							)







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Optional[int]        =			[]
          snake_case			:      Dict        =			""""""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    snake_case			:      Union[str, Any]        =			self.sp_model.decode(__a							)
                    out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                    snake_case			:      Optional[Any]        =			[]
               else:
                    current_sub_tokens.append(__a							)
          snake_case			:      Dict        =			self.sp_model.decode(__a							)
          out_string += decoded.upper() if self.do_upper_case else decoded
          return out_string.strip()







     def 			snake_case_      (				self							,__a							,__a=None							)      ->				List[int]:
          if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + [self.eos_token_id]
          # 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.eos_token_id]







     def 			snake_case_      (				self							,__a							,__a = None							,__a = False							)      ->				List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__a							,token_ids_a=__a							,already_has_special_tokens=__a							)

          snake_case			:      List[Any]        =			[1] * len(self.prefix_tokens							)
          snake_case			:      Dict        =			[1]
          if token_ids_a is None:
               return prefix_ones + ([0] * len(__a							)) + suffix_ones
          return prefix_ones + ([0] * len(__a							)) + ([0] * len(__a							)) + suffix_ones







     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      Optional[Any]        =			self.encoder.copy()
          vocab.update(self.added_tokens_encoder							)
          return vocab







     def __getstate__(				self							)      ->				Dict:
          snake_case			:      Optional[Any]        =			self.__dict__.copy()
          snake_case			:      List[Any]        =			None
          return state







     def __setstate__(				self							,__a							)      ->				None:
          snake_case			:      Union[str, Any]        =			d

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

          snake_case			:      str        =			load_spm(self.spm_file							,self.sp_model_kwargs							)







     def 			snake_case_      (				self							,__a							,__a = None							)      ->				Tuple[str]:
          snake_case			:      Tuple        =			Path(__a							)
          assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
          snake_case			:      Optional[Any]        =			save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
          )
          snake_case			:      Optional[int]        =			save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
          )

          save_json(self.encoder							,__a							)

          if os.path.abspath(self.spm_file							) != os.path.abspath(__a							) and os.path.isfile(self.spm_file							):
               copyfile(self.spm_file							,__a							)
          elif not os.path.isfile(self.spm_file							):
               with open(__a							,"""wb"""							) as fi:
                    snake_case			:      Any        =			self.sp_model.serialized_model_proto()
                    fi.write(__a							)

          return (str(__a							), str(__a							))



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     snake_case			:      Union[str, Any]        =			sentencepiece.SentencePieceProcessor(**__lowercase				)
     spm.Load(str(__lowercase				)				)
     return spm



def        lowerCamelCase__				(      __lowercase				):
     with open(__lowercase     ,   """r"""				) as f:
          return json.load(__lowercase				)







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









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # assert negative_img array for at least one True
     assert negative_img.any()



def        lowerCamelCase__				(      ):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def        lowerCamelCase__				(      ):
     snake_case			:      Union[str, Any]        =			canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
     # Assert ambiguous array
     assert resp.all()



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[Any]        =			imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
     # assert ambiguous array for all == True
     assert canny_img.all()
     snake_case			:      Any        =			canny.canny(__lowercase				)
     # assert canny array for at least one True
     assert canny_array.any()



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



def        lowerCamelCase__				(      ):
     # laplace diagonals
     snake_case			:      int        =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
     snake_case			:      Tuple        =			conv.img_convolve(__lowercase     ,   __lowercase				).astype(__lowercase				)
     assert res.any()



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     snake_case			:      Tuple        =			imread(__lowercase     ,   0				)

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     snake_case			:      List[Any]        =			np.zeros((image.shape[0], image.shape[1])				)

     # Iterating through the image and calculating the local binary pattern value
     # for each pixel.
     for i in range(0     ,   image.shape[0]				):
          for j in range(0     ,   image.shape[1]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



