






"""simple docstring"""



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		:	Tuple      =		logging.get_logger(__name__)

__A		:	int      =		{
    "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		:	Union[str, Any]      =		{"Helsinki-NLP/opus-mt-en-de": 512}
__A		:	str      =		{}





class  _UpperCAmelCase	(			SCREAMING_SNAKE_CASE__							):
					SCREAMING_SNAKE_CASE_		:    Union[str, Any]							   =       VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:    Optional[int]							   =       PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:    Dict							   =       PRETRAINED_INIT_CONFIGURATION
					SCREAMING_SNAKE_CASE_		:    List[str]							   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:    str							   =       ['input_ids', 'attention_mask']
					SCREAMING_SNAKE_CASE_		:    Optional[int]							   =       re.compile(">>.+<<"							)  # type: re.Pattern




					def __init__(    self					:						Tuple   ,						A					:						str   ,						A					:						Tuple   ,						A					:						Optional[int]   ,						A					:						Optional[Any]=None   ,						A					:						Optional[Any]=None   ,						A					:						str=None   ,						A					:						str="<unk>"   ,						A					:						Dict="</s>"   ,						A					:						List[str]="<pad>"   ,						A					:						str=5_12   ,						A					:						Optional[Dict[str, Any]] = None   ,						A					:						Union[str, Any]=False   ,						**A					:						int   ,						)  ->  int:
							lowercase_			:			Union[str, Any]								=       {} 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=A   ,						target_lang=A   ,						unk_token=A   ,						eos_token=A   ,						pad_token=A   ,						model_max_length=A   ,						sp_model_kwargs=self.sp_model_kwargs   ,						target_vocab_file=A   ,						separate_vocabs=A   ,						**A   ,						)
							assert Path(A       ).exists(), F'''cannot find spm source {source_spm}'''

							lowercase_			:			Optional[Any]								=       separate_vocabs
							lowercase_			:			str								=       load_json(A       )
							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:
									lowercase_			:			Dict								=       load_json(A       )
									lowercase_			:			Union[str, Any]								=       {v: k for k, v in self.target_encoder.items()}
									lowercase_			:			Union[str, Any]								=       []
							else:
									lowercase_			:			Union[str, Any]								=       {v: k for k, v in self.encoder.items()}
									lowercase_			:			Tuple								=       [k for k in self.encoder if k.startswith('''>>'''       ) and k.endswith('''<<'''       )]

							lowercase_			:			Union[str, Any]								=       source_lang
							lowercase_			:			Dict								=       target_lang
							lowercase_			:			str								=       [source_spm, target_spm]

							# load SentencePiece model for pre-processing
							lowercase_			:			Tuple								=       load_spm(A   ,						self.sp_model_kwargs       )
							lowercase_			:			Tuple								=       load_spm(A   ,						self.sp_model_kwargs       )
							lowercase_			:			Any								=       self.spm_source
							lowercase_			:			int								=       self.encoder

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

							self._setup_normalizer()




					def   A  (    self					:						Dict       )  ->  Union[str, Any]:

							try:
									from sacremoses import MosesPunctNormalizer

									lowercase_			:			List[str]								=       MosesPunctNormalizer(self.source_lang       ).normalize
							except (ImportError, FileNotFoundError):
									warnings.warn('''Recommended: pip install sacremoses.'''       )
									lowercase_			:			List[Any]								=       lambda A       : x




					def   A  (    self					:						int   ,						A					:						str       )  ->  Optional[int]:
							return self.punc_normalizer(A       ) if x else ""




					def   A  (    self					:						List[str]   ,						A					:						List[str]       )  ->  Any:
							return self.current_encoder.get(A   ,						self.current_encoder[self.unk_token]       )




					def   A  (    self					:						int   ,						A					:						str       )  ->  Tuple:
							lowercase_			:			int								=       self.language_code_re.match(A       )
							lowercase_			:			Tuple								=       [match.group(0       )] if match else []
							return code, self.language_code_re.sub(''''''   ,						A       )




					def   A  (    self					:						str   ,						A					:						str       )  ->  Optional[int]:
							lowercase_	, lowercase_			:			Any								=       self.remove_language_code(A       )
							lowercase_			:			List[Any]								=       self.current_spm.encode(A   ,						out_type=A       )
							return code + pieces




					def   A  (    self					:						Tuple   ,						A					:						int       )  ->  Any:
							return self.decoder.get(A   ,						self.unk_token       )




					def   A  (    self					:						Tuple   ,						A					:						Optional[int]   ,						**A					:						Union[str, Any]       )  ->  Dict:
							return super().batch_decode(A   ,						**A       )




					def   A  (    self					:						Dict   ,						A					:						Union[str, Any]   ,						**A					:						Optional[int]       )  ->  Optional[Any]:
							return super().decode(A   ,						**A       )




					def   A  (    self					:						Optional[Any]   ,						A					:						List[str]       )  ->  List[Any]:
							lowercase_			:			Union[str, Any]								=       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
							lowercase_			:			str								=       []
							lowercase_			:			List[str]								=       ''''''
							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(A       ) + token + " "
											lowercase_			:			Tuple								=       []
									else:
											current_sub_tokens.append(A       )
							out_string += sp_model.decode_pieces(A       )
							return out_string.strip()




					def   A  (    self					:						int   ,						A					:						Dict   ,						A					:						Union[str, Any]=None       )  ->  str:

							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   A  (    self					:						Optional[int]       )  ->  List[Any]:
							lowercase_			:			Dict								=       self.spm_source
							lowercase_			:			int								=       self.encoder




					def   A  (    self					:						List[Any]       )  ->  Any:
							lowercase_			:			str								=       self.spm_target
							if self.separate_vocabs:
									lowercase_			:			Any								=       self.target_encoder




					@property
					def   A  (    self					:						int       )  ->  Optional[Any]:
							return len(self.encoder       )




					def   A  (    self					:						Optional[int]   ,						A					:						str   ,						A					:						Optional[str] = None       )  ->  Optional[Any]:

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

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

							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]   ,						):
									lowercase_			:			str								=       os.path.join(
									    A   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename       )
									if os.path.abspath(A       ) != os.path.abspath(A       ) and os.path.isfile(A       ):
											copyfile(A   ,						A       )
											saved_files.append(A       )
									elif not os.path.isfile(A       ):
											with open(A   ,						'''wb'''       ) as fi:
													lowercase_			:			Optional[int]								=       spm_model.serialized_model_proto()
													fi.write(A       )
											saved_files.append(A       )

							return tuple(A       )




					def   A  (    self					:						Any       )  ->  Any:
							return self.get_src_vocab()




					def   A  (    self					:						Tuple       )  ->  List[Any]:
							return dict(self.encoder   ,						**self.added_tokens_encoder       )




					def   A  (    self					:						List[Any]       )  ->  Optional[int]:
							return dict(self.target_encoder   ,						**self.added_tokens_decoder       )




					def __getstate__(    self					:						List[Any]       )  ->  int:
							lowercase_			:			Tuple								=       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   ,						A					:						Dict       )  ->  Tuple:
							lowercase_			:			List[str]								=       d

							# for backward compatibility
							if not hasattr(self   ,						'''sp_model_kwargs'''       ):
									lowercase_			:			Optional[int]								=       {}

							lowercase_	, lowercase_			:			List[Any]								=       (load_spm(A   ,						self.sp_model_kwargs       ) for f in self.spm_files)
							lowercase_			:			Any								=       self.spm_source
							self._setup_normalizer()




					def   A  (    self					:						List[str]   ,						*A					:						Any   ,						**A					:						Tuple       )  ->  str:
							return 1




					def   A  (    self					:						str   ,						A					:						Optional[Any]       )  ->  Tuple:
							lowercase_			:			int								=       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   A  (    self					:						Optional[int]   ,						A					:						List   ,						A					:						Optional[List] = None   ,						A					:						bool = False       )  ->  str:

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



def 						lowercase   (    __snake_case    :			str		, __snake_case    :			Dict[str, Any]     ):
		lowercase_			:			List[str]								=       sentencepiece.SentencePieceProcessor(**UpperCamelCase_     )
		spm.Load(UpperCamelCase_     )
		return spm



def 						lowercase   (    __snake_case    :			Optional[Any]		, __snake_case    :			str     ):



		with open(UpperCamelCase_		, '''w'''     ) as f:
				json.dump(UpperCamelCase_		, UpperCamelCase_		, indent=2     )






def 						lowercase   (    __snake_case    :			str     ):



		with open(UpperCamelCase_		, '''r'''     ) as f:
				return json.load(UpperCamelCase_     )











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''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(__snake_case		, __snake_case     )} dB'''     )

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


if __name__ == "__main__":
						main()



