
"""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


__magic_name__		       =				logging.get_logger(__name__)

__magic_name__		       =				{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__magic_name__		       =				{
    '''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'''
        )
    },
}

__magic_name__		       =				{'''Helsinki-NLP/opus-mt-en-de''': 5_12}
__magic_name__		       =				{}


class    _lowerCAmelCase						(						lowerCamelCase						):
							lowercase_     :     Dict       =							VOCAB_FILES_NAMES
							lowercase_     :     Optional[int]       =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_     :     Optional[int]       =							PRETRAINED_INIT_CONFIGURATION
							lowercase_     :     List[Any]       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_     :     Any       =							['''input_ids''', '''attention_mask''']
							lowercase_     :     Any       =							re.compile('''>>.+<<'''						)  # type: re.Pattern



							def __init__(      self ,						a_ ,						a_ ,						a_ ,						a_=None ,						a_=None ,						a_=None ,						a_="<unk>" ,						a_="</s>" ,						a_="<pad>" ,						a_=512 ,						a_ = None ,						a_=False ,						**a_ ,						)							->							None:
								_UpperCAmelCase =						{} 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}"

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

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

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

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

								self._setup_normalizer()



							def       _a   (      self   )							->							Optional[Any]:
								try:
									from sacremoses import MosesPunctNormalizer

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



							def       _a   (      self ,						a_   )							->							str:
								return self.punc_normalizer(a_   ) if x else ""



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



							def       _a   (      self ,						a_   )							->							Tuple:
								_UpperCAmelCase =						self.language_code_re.match(a_   )
								_UpperCAmelCase =						[match.group(0   )] if match else []
								return code, self.language_code_re.sub("" ,						a_   )



							def       _a   (      self ,						a_   )							->							List[str]:
								_UpperCAmelCase ,      _UpperCAmelCase =						self.remove_language_code(a_   )
								_UpperCAmelCase =						self.current_spm.encode(a_ ,						out_type=a_   )
								return code + pieces



							def       _a   (      self ,						a_   )							->							str:
								return self.decoder.get(a_ ,						self.unk_token   )



							def       _a   (      self ,						a_ ,						**a_   )							->							Optional[int]:
								return super().batch_decode(a_ ,						**a_   )



							def       _a   (      self ,						a_ ,						**a_   )							->							Optional[Any]:
								return super().decode(a_ ,						**a_   )



							def       _a   (      self ,						a_   )							->							str:
								_UpperCAmelCase =						self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								_UpperCAmelCase =						[]
								_UpperCAmelCase =						""
								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 + " "
										_UpperCAmelCase =						[]
									else:
										current_sub_tokens.append(a_   )
								out_string += sp_model.decode_pieces(a_   )
								return out_string.strip()



							def       _a   (      self ,						a_ ,						a_=None   )							->							List[int]:
								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   )							->							Union[str, Any]:
								_UpperCAmelCase =						self.spm_source
								_UpperCAmelCase =						self.encoder



							def       _a   (      self   )							->							Tuple:
								_UpperCAmelCase =						self.spm_target
								if self.separate_vocabs:
									_UpperCAmelCase =						self.target_encoder



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



							def       _a   (      self ,						a_ ,						a_ = None   )							->							Tuple[str]:
								if not os.path.isdir(a_   ):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
									return
								_UpperCAmelCase =						[]

								if self.separate_vocabs:
									_UpperCAmelCase =						os.path.join(
									    a_ ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] ,						)
									_UpperCAmelCase =						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:
									_UpperCAmelCase =						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] ,						):
									_UpperCAmelCase =						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:
											_UpperCAmelCase =						spm_model.serialized_model_proto()
											fi.write(a_   )
										saved_files.append(a_   )

								return tuple(a_   )



							def       _a   (      self   )							->							Dict:
								return self.get_src_vocab()



							def       _a   (      self   )							->							int:
								return dict(self.encoder ,						**self.added_tokens_encoder   )



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



							def __getstate__(      self   )							->							Dict:
								_UpperCAmelCase =						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 ,						a_   )							->							None:
								_UpperCAmelCase =						d

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

								_UpperCAmelCase ,      _UpperCAmelCase =						(load_spm(a_ ,						self.sp_model_kwargs   ) for f in self.spm_files)
								_UpperCAmelCase =						self.spm_source
								self._setup_normalizer()



							def       _a   (      self ,						*a_ ,						**a_   )							->							str:
								return 1



							def       _a   (      self ,						a_   )							->							Any:
								_UpperCAmelCase =						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 ,						a_ ,						a_ = None ,						a_ = False   )							->							List[int]:
								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     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						sentencepiece.SentencePieceProcessor(**UpperCamelCase__      )
	spm.Load(UpperCamelCase__      )
	return spm




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






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




def     __lowerCamelCase  (		UpperCamelCase__      ):


	"""simple docstring"""






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






"""simple docstring"""
from collections import namedtuple

__magic_name__		       =				namedtuple('''from_to''', '''from_ to''')

__magic_name__		       =				{
    '''atm''': from_to(1, 1),
    '''pascal''': from_to(0.0000098, 10_13_25),
    '''bar''': from_to(0.986923, 1.01325),
    '''kilopascal''': from_to(0.00986923, 101.325),
    '''megapascal''': from_to(9.86923, 0.101325),
    '''psi''': from_to(0.068046, 14.6959),
    '''inHg''': from_to(0.0334211, 29.9213),
    '''torr''': from_to(0.00131579, 7_60),
}




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






	if from_type not in PRESSURE_CONVERSION:
		raise ValueError(
		    f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
		    + ", ".join(UpperCamelCase__      )      )
	if to_type not in PRESSURE_CONVERSION:
		raise ValueError(
		    f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
		    + ", ".join(UpperCamelCase__      )      )
	return (
	    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




