



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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE     =	{
    """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"""
        )
    },
}

__SCREAMING_SNAKE_CASE     =	{"""Helsinki-NLP/opus-mt-en-de""": 512}
__SCREAMING_SNAKE_CASE     =	{}

class 						lowerCamelCase_     (			__a    ):






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







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

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

									A				:       Optional[int]														=    source_lang
									A				:       str														=    target_lang
									A				:       List[Any]														=    [source_spm, target_spm]

									# load SentencePiece model for pre-processing
									A				:       Optional[Any]														=    load_spm(__lowerCamelCase						,						self.sp_model_kwargs			)
									A				:       int														=    load_spm(__lowerCamelCase						,						self.sp_model_kwargs			)
									A				:       List[str]														=    self.spm_source
									A				:       Optional[Any]														=    self.encoder

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

									self._setup_normalizer()







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					List[str]:

									try:
												from sacremoses import MosesPunctNormalizer

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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple						,						__lowerCamelCase   :      str			)   ->					List[Any]:
									return self.punc_normalizer(__lowerCamelCase			) if x else ""







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]						,						__lowerCamelCase   :      Dict			)   ->					int:
									return self.current_encoder.get(__lowerCamelCase						,						self.current_encoder[self.unk_token]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]						,						__lowerCamelCase   :      str			)   ->					List[str]:
									A				:       int														=    self.language_code_re.match(__lowerCamelCase			)
									A				:       List[Any]														=    [match.group(0			)] if match else []
									return code, self.language_code_re.sub(""						,						__lowerCamelCase			)







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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict						,						__lowerCamelCase   :      int			)   ->					Dict:
									return self.decoder.get(__lowerCamelCase						,						self.unk_token			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      List[Any]						,						**__lowerCamelCase   :      str			)   ->					List[Any]:
									return super().batch_decode(__lowerCamelCase						,						**__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      Optional[Any]						,						**__lowerCamelCase   :      Optional[Any]			)   ->					List[Any]:
									return super().decode(__lowerCamelCase						,						**__lowerCamelCase			)







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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      Optional[int]=None			)   ->					Tuple:

									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       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Tuple:
									A				:       Any														=    self.spm_source
									A				:       Optional[Any]														=    self.encoder







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







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Union[str, Any]:
									return len(self.encoder			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Optional[str] = None			)   ->					int:

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

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

									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				:       str														=    os.path.join(
												    __lowerCamelCase						,						(filename_prefix + "-" if filename_prefix else "") + spm_save_filename			)
												if os.path.abspath(__lowerCamelCase			) != os.path.abspath(__lowerCamelCase			) and os.path.isfile(__lowerCamelCase			):
															copyfile(__lowerCamelCase						,						__lowerCamelCase			)
															saved_files.append(__lowerCamelCase			)
												elif not os.path.isfile(__lowerCamelCase			):
															with open(__lowerCamelCase						,						"wb"			) as fi:
																		A				:       Tuple														=    spm_model.serialized_model_proto()
																		fi.write(__lowerCamelCase			)
															saved_files.append(__lowerCamelCase			)

									return tuple(__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					str:
									return self.get_src_vocab()







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Dict:
									return dict(self.encoder						,						**self.added_tokens_encoder			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Dict			)   ->					Optional[int]:
									return dict(self.target_encoder						,						**self.added_tokens_decoder			)







						def __getstate__(						self   :      Union[str, Any]			)   ->					Optional[Any]:
									A				:       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   :      Any						,						__lowerCamelCase   :      Dict			)   ->					List[str]:
									A				:       Any														=    d

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

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







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						*__lowerCamelCase   :      str						,						**__lowerCamelCase   :      Tuple			)   ->					str:
									return 1







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

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







def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       List[Any]														=    sentencepiece.SentencePieceProcessor(**_a     )
			spm.Load(_a     )
			return spm




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			with open(_a			,     "w"     ) as f:
						json.dump(_a			,     _a			,     indent=2     )






def 						UpperCAmelCase (    _lowerCamelCase     ):
			with open(_a			,     "r"     ) as f:
						return json.load(_a     )





'''simple docstring'''







from ..utils import DummyObject, requires_backends


class      _a    (      metaclass=__a					):
       __a							:	str  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Any  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Optional[int]  ,							*lowercase		:      List[str]  ,							**lowercase		:      str							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      List[Any]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)






class      _a    (      metaclass=__a					):
       __a							:	List[Any]  							=   ["""torch""", """librosa"""]
       def __init__(      self		:      int  ,							*lowercase		:      Optional[int]  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           requires_backends(self  ,							['''torch''', '''librosa''']							)
       @classmethod
       def        A (      cls		:      Dict  ,							*lowercase		:      Tuple  ,							**lowercase		:      List[str]							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)







       @classmethod
       def        A (      cls		:      Any  ,							*lowercase		:      Any  ,							**lowercase		:      Tuple							):



           '''simple docstring'''

           requires_backends(cls  ,							['''torch''', '''librosa''']							)




