


'''simple docstring'''

import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase							:       Any  						=     logging.get_logger(__name__)
lowerCamelCase							:       List[Any]  						=     {'vocab_file': 'spiece.model'}

lowerCamelCase							:       Optional[int]  						=     {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

lowerCamelCase							:       Optional[int]  						=     {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase							:       str  						=     '▁'
class   __lowerCAmelCase						(lowercase_					):





      '''simple docstring'''






      lowerCAmelCase__ :					Optional[Any]   =							VOCAB_FILES_NAMES
      lowerCAmelCase__ :					str   =							PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__ :					Tuple   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__ :					Dict   =							["""input_ids""", """token_type_ids"""]



      def __init__(self   :   int       ,						UpperCamelCase   :   Optional[Any]       ,						UpperCamelCase   :   Dict=False       ,						UpperCamelCase   :   List[Any]=True       ,						UpperCamelCase   :   Dict=True       ,						UpperCamelCase   :   List[Any]="<unk>"       ,						UpperCamelCase   :   Dict="[SEP]"       ,						UpperCamelCase   :   int="<pad>"       ,						UpperCamelCase   :   Tuple="[CLS]"       ,						UpperCamelCase   :   Union[str, Any]="[MASK]"       ,						UpperCamelCase   :   Optional[Dict[str, Any]] = None       ,						**UpperCamelCase   :   Optional[Any]       ,						):

             '''simple docstring'''







             lowercase__				      =			(
                 AddedToken(UpperCamelCase       ,						lstrip=UpperCamelCase       ,						rstrip=UpperCamelCase       ,						normalized=UpperCamelCase			)
                 if isinstance(UpperCamelCase       ,						UpperCamelCase			)
                 else mask_token
             )

             lowercase__				      =			{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 do_lower_case=UpperCamelCase       ,						remove_space=UpperCamelCase       ,						keep_accents=UpperCamelCase       ,						unk_token=UpperCamelCase       ,						sep_token=UpperCamelCase       ,						pad_token=UpperCamelCase       ,						cls_token=UpperCamelCase       ,						mask_token=UpperCamelCase       ,						sp_model_kwargs=self.sp_model_kwargs       ,						**UpperCamelCase       ,						)

             lowercase__				      =			do_lower_case
             lowercase__				      =			remove_space
             lowercase__				      =			keep_accents
             lowercase__				      =			vocab_file

             lowercase__				      =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
             self.sp_model.Load(UpperCamelCase			)



      @property
      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             return len(self.sp_model			)



      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







             lowercase__				      =			{self.convert_ids_to_tokens(UpperCamelCase			): i for i in range(self.vocab_size			)}
             vocab.update(self.added_tokens_encoder			)
             return vocab



      def __getstate__(self   :   List[str]			):

             '''simple docstring'''







             lowercase__				      =			self.__dict__.copy()
             lowercase__				      =			None
             return state



      def __setstate__(self   :   Optional[int]       ,						UpperCamelCase   :   List[str]			):

             '''simple docstring'''







             lowercase__				      =			d

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

             lowercase__				      =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
             self.sp_model.Load(self.vocab_file			)



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   str			):

             '''simple docstring'''







             if self.remove_space:
                    lowercase__				      =			''' '''.join(inputs.strip().split()			)
             else:
                    lowercase__				      =			inputs
             lowercase__				      =			outputs.replace('''``'''       ,						'''"'''			).replace('''\'\''''       ,						'''"'''			)

             if not self.keep_accents:
                    lowercase__				      =			unicodedata.normalize('''NFKD'''       ,						UpperCamelCase			)
                    lowercase__				      =			''''''.join([c for c in outputs if not unicodedata.combining(UpperCamelCase			)]			)
             if self.do_lower_case:
                    lowercase__				      =			outputs.lower()

             return outputs



      def 				UpperCamelCase__		(self   :   List[Any]       ,						UpperCamelCase   :   str			):

             '''simple docstring'''







             lowercase__				      =			self.preprocess_text(UpperCamelCase			)
             lowercase__				      =			self.sp_model.encode(UpperCamelCase       ,						out_type=UpperCamelCase			)
             lowercase__				      =			[]
             for piece in pieces:
                    if len(UpperCamelCase			) > 1 and piece[-1] == str(''','''			) and piece[-2].isdigit():
                           lowercase__				      =			self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase       ,						''''''			)			)
                           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                  if len(cur_pieces[0]			) == 1:
                                         lowercase__				      =			cur_pieces[1:]
                                  else:
                                         lowercase__				      =			cur_pieces[0][1:]
                           cur_pieces.append(piece[-1]			)
                           new_pieces.extend(UpperCamelCase			)
                    else:
                           new_pieces.append(UpperCamelCase			)

             return new_pieces



      def 				UpperCamelCase__		(self   :   List[str]       ,						UpperCamelCase   :   Optional[int]			):

             '''simple docstring'''







             return self.sp_model.PieceToId(UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   Union[str, Any]       ,						UpperCamelCase   :   Any			):

             '''simple docstring'''







             return self.sp_model.IdToPiece(UpperCamelCase			)



      def 				UpperCamelCase__		(self   :   List[str]       ,						UpperCamelCase   :   str			):

             '''simple docstring'''







             lowercase__				      =			[]
             lowercase__				      =			''''''
             lowercase__				      =			False
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           if not prev_is_special:
                                  out_string += " "
                           out_string += self.sp_model.decode(UpperCamelCase			) + token
                           lowercase__				      =			True
                           lowercase__				      =			[]
                    else:
                           current_sub_tokens.append(UpperCamelCase			)
                           lowercase__				      =			False
             out_string += self.sp_model.decode(UpperCamelCase			)
             return out_string.strip()



      def 				UpperCamelCase__		(self   :   Dict       ,						UpperCamelCase   :   List[int]       ,						UpperCamelCase   :   bool = False       ,						UpperCamelCase   :   bool = None       ,						UpperCamelCase   :   bool = True       ,						**UpperCamelCase   :   Any       ,						):

             '''simple docstring'''







             lowercase__				      =			kwargs.pop('''use_source_tokenizer'''       ,						UpperCamelCase			)

             lowercase__				      =			self.convert_ids_to_tokens(UpperCamelCase       ,						skip_special_tokens=UpperCamelCase			)

             # To avoid mixing byte-level and unicode for byte-level BPT
             # we need to build string separately for added tokens and byte-level tokens
             # cf. https://github.com/huggingface/transformers/issues/1133
             lowercase__				      =			[]
             lowercase__				      =			[]
             for token in filtered_tokens:
                    if skip_special_tokens and token in self.all_special_ids:
                           continue
                    if token in self.added_tokens_encoder:
                           if current_sub_text:
                                  sub_texts.append(self.convert_tokens_to_string(UpperCamelCase			)			)
                                  lowercase__				      =			[]
                           sub_texts.append(UpperCamelCase			)
                    else:
                           current_sub_text.append(UpperCamelCase			)
             if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(UpperCamelCase			)			)

             # Mimic the behavior of the Rust tokenizer:
             # No space after <unk>
             if spaces_between_special_tokens:
                    lowercase__				      =			re.sub(R'''(<unk>) '''       ,						R'''\1'''       ,						''' '''.join(UpperCamelCase			)			)
             else:
                    lowercase__				      =			''''''.join(UpperCamelCase			)

             lowercase__				      =			(
                 clean_up_tokenization_spaces
                 if clean_up_tokenization_spaces is not None
                 else self.clean_up_tokenization_spaces
             )
             if clean_up_tokenization_spaces:
                    lowercase__				      =			self.clean_up_tokenization(UpperCamelCase			)
                    return clean_text
             else:
                    return text



      def 				UpperCamelCase__		(self   :   Tuple       ,						UpperCamelCase   :   List[int]       ,						UpperCamelCase   :   Optional[List[int]] = None			):

             '''simple docstring'''







             lowercase__				      =			[self.sep_token_id]
             lowercase__				      =			[self.cls_token_id]
             if token_ids_a is None:
                    return cls + token_ids_a + sep
             return cls + token_ids_a + sep + token_ids_a + sep



      def 				UpperCamelCase__		(self   :   Union[str, Any]       ,						UpperCamelCase   :   List[int]       ,						UpperCamelCase   :   Optional[List[int]] = None       ,						UpperCamelCase   :   bool = False			):

             '''simple docstring'''







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

             if token_ids_a is not None:
                    return [1] + ([0] * len(UpperCamelCase			)) + [1] + ([0] * len(UpperCamelCase			)) + [1]
             return [1] + ([0] * len(UpperCamelCase			)) + [1]



      def 				UpperCamelCase__		(self   :   int       ,						UpperCamelCase   :   List[int]       ,						UpperCamelCase   :   Optional[List[int]] = None			):

             '''simple docstring'''







             lowercase__				      =			[self.sep_token_id]
             lowercase__				      =			[self.cls_token_id]

             if token_ids_a is None:
                    return len(cls + token_ids_a + sep			) * [0]
             return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]






      def 				UpperCamelCase__		(self   :   List[str]       ,						UpperCamelCase   :   str       ,						UpperCamelCase   :   Optional[str] = None			):

             '''simple docstring'''







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

             if os.path.abspath(self.vocab_file			) != os.path.abspath(UpperCamelCase			) and os.path.isfile(self.vocab_file			):
                    copyfile(self.vocab_file       ,						UpperCamelCase			)
             elif not os.path.isfile(self.vocab_file			):
                    with open(UpperCamelCase       ,						'''wb'''			) as fi:
                           lowercase__				      =			self.sp_model.serialized_model_proto()
                           fi.write(UpperCamelCase			)

             return (out_vocab_file,)








'''simple docstring'''

import math
import os

import cva
import numpy as np

lowerCamelCase							:       Optional[Any]  						=     2_5_5.0

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	float:






       """simple docstring"""







       lowercase__				      =			np.mean((original - contrast) ** 2	)
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(A	)	)

def _SCREAMING_SNAKE_CASE				()	->	None:






       """simple docstring"""







       lowercase__				      =			os.path.dirname(os.path.realpath(A	)	)
       # Loading images (original image and compressed image)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/original_image.png'''	)	)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/compressed_image.png'''	)  ,			1	)

       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/PSNR-example-base.png'''	)	)
       lowercase__				      =			cva.imread(
           os.path.join(A  ,			'''image_data/PSNR-example-comp-10.jpg'''	)  ,			1	)

       # Value expected: 29.73dB
       print('''-- First Test --'''	)
       print(f"PSNR value is {peak_signal_to_noise_ratio(A  ,			A	)} dB"	)

       # # Value expected: 31.53dB (Wikipedia Example)
       print('''\n-- Second Test --'''	)
       print(f"PSNR value is {peak_signal_to_noise_ratio(A  ,			A	)} dB"	)


if __name__ == "__main__":
 main()




