


"""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


__SCREAMING_SNAKE_CASE				:     Any		=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:     int		=     {
    "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				:     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"
        )
    },
}

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





class   __A  (lowercase__):





							'''simple docstring'''

							__lowercase:		Union[str, Any]				   =     VOCAB_FILES_NAMES
							__lowercase:		str				   =     PRETRAINED_VOCAB_FILES_MAP
							__lowercase:		Dict				   =     PRETRAINED_INIT_CONFIGURATION
							__lowercase:		Optional[Any]				   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase:		Union[str, Any]				   =     ["""input_ids""", """attention_mask"""]
							__lowercase:		str				   =     re.compile(""">>.+<<""")  # type: re.Pattern



							def __init__(					self				:   Dict		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   Optional[Any]		,							UpperCAmelCase_				:   Tuple=None		,							UpperCAmelCase_				:   Optional[Any]=None		,							UpperCAmelCase_				:   Optional[Any]=None		,							UpperCAmelCase_				:   Tuple="<unk>"		,							UpperCAmelCase_				:   Dict="</s>"		,							UpperCAmelCase_				:   Optional[Any]="<pad>"		,							UpperCAmelCase_				:   List[Any]=512		,							UpperCAmelCase_				:   Optional[Dict[str, Any]] = None		,							UpperCAmelCase_				:   Union[str, Any]=False		,							**UpperCAmelCase_				:   Tuple		,							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		{} 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}"""

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

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

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

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

														self._setup_normalizer()



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



														"""simple docstring"""



														try:
																					from sacremoses import MosesPunctNormalizer

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



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   str							)       ->List[Any]:



														"""simple docstring"""



														return self.punc_normalizer(__lowerCamelCase							) if x else ""



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   Optional[Any]							)       ->Union[str, Any]:



														"""simple docstring"""



														return self.current_encoder.get(__lowerCamelCase		,							self.current_encoder[self.unk_token]							)



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   str							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		self.language_code_re.match(__lowerCamelCase							)
														snake_case_     							=		[match.group(0							)] if match else []
														return code, self.language_code_re.sub(""""""		,							__lowerCamelCase							)



							def 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   str							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.remove_language_code(__lowerCamelCase							)
														snake_case_     							=		self.current_spm.encode(__lowerCamelCase		,							out_type=__lowerCamelCase							)
														return code + pieces



							def 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   int							)       ->Optional[Any]:



														"""simple docstring"""



														return self.decoder.get(__lowerCamelCase		,							self.unk_token							)



							def 		lowerCAmelCase							(					self				:   Union[str, Any]		,							UpperCAmelCase_				:   Optional[Any]		,							**UpperCAmelCase_				:   List[Any]							)       ->List[Any]:



														"""simple docstring"""



														return super().batch_decode(__lowerCamelCase		,							**__lowerCamelCase							)



							def 		lowerCAmelCase							(					self				:   Dict		,							UpperCAmelCase_				:   Tuple		,							**UpperCAmelCase_				:   List[Any]							)       ->Dict:



														"""simple docstring"""



														return super().decode(__lowerCamelCase		,							**__lowerCamelCase							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   List[str]							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
														snake_case_     							=		[]
														snake_case_     							=		""
														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 + " "
																												snake_case_     							=		[]
																					else:
																												current_sub_tokens.append(__lowerCamelCase							)
														out_string += sp_model.decode_pieces(__lowerCamelCase							)
														return out_string.strip()



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Union[str, Any]=None							)       ->Tuple:



														"""simple docstring"""



														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 		lowerCAmelCase							(					self				:   List[str]							)       ->str:



														"""simple docstring"""



														snake_case_     							=		self.spm_source
														snake_case_     							=		self.encoder



							def 		lowerCAmelCase							(					self				:   int							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.spm_target
														if self.separate_vocabs:
																					snake_case_     							=		self.target_encoder



							@property
							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->Dict:



														"""simple docstring"""



														return len(self.encoder							)



							def 		lowerCAmelCase							(					self				:   Tuple		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Optional[str] = None							)       ->Any:



														"""simple docstring"""



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

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

														return tuple(__lowerCamelCase							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->Dict:



														"""simple docstring"""



														return self.get_src_vocab()



							def 		lowerCAmelCase							(					self				:   int							)       ->Union[str, Any]:



														"""simple docstring"""



														return dict(self.encoder		,							**self.added_tokens_encoder							)



							def 		lowerCAmelCase							(					self				:   Any							)       ->List[Any]:



														"""simple docstring"""



														return dict(self.target_encoder		,							**self.added_tokens_decoder							)



							def __getstate__(					self				:   str							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		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				:   Tuple		,							UpperCAmelCase_				:   Dict							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		d

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

														snake_case_     							=		(load_spm(__lowerCamelCase		,							self.sp_model_kwargs							) for f in self.spm_files)
														snake_case_     							=		self.spm_source
														self._setup_normalizer()



							def 		lowerCAmelCase							(					self				:   Dict		,							*UpperCAmelCase_				:   List[str]		,							**UpperCAmelCase_				:   List[Any]							)       ->Any:



														"""simple docstring"""



														return 1



							def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   str							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		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 		lowerCAmelCase							(					self				:   Any		,							UpperCAmelCase_				:   List		,							UpperCAmelCase_				:   Optional[List] = None		,							UpperCAmelCase_				:   bool = False							)       ->Tuple:



														"""simple docstring"""



														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     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Dict:
							snake_case_     							=		sentencepiece.SentencePieceProcessor(**_A					)
							spm.Load(_A					)
							return spm







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					str:




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







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Any:




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







import torch

from transformers import AutoTokenizer







class      _lowercase					(				lowercase__):


       """simple docstring"""




       def __init__(  self				:     Optional[Any]   , __lowerCamelCase				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    AutoTokenizer.from_pretrained(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]   , __lowerCamelCase				:     str			):


             '''simple docstring'''



             if isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							List[Any]       =    None
                   for l in x:
                         lowerCamelCase__						:							str       =    self.tokenizer(
                             __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )
                         lowerCamelCase__						:							Dict       =    torch.tensor([len(__lowerCamelCase			)]			)
                         if d is not None:
                               for k in d.keys():
                                     lowerCamelCase__						:							Tuple       =    torch.cat((d[k], t[k])   , 0			)
                         else:
                               lowerCamelCase__						:							Union[str, Any]       =    t

                   lowerCamelCase__						:							List[Any]       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"			)			)
                   lowerCamelCase__						:							Dict       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"			)			)

             elif isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							Tuple       =    self.tokenizer(
                       __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )

             else:
                   raise Exception(
                       "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
                       " strings` for supports are supported."			)

             return d





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Optional[Any]=0.7_0			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    []
             for idx in range(len(W_query["input_ids"]			)			):
                   lowerCamelCase__						:							str       =    range(p_start.shape[1]			)

                   lowerCamelCase__						:							int       =    []
                   for start_id in start_indexes:
                         for end_id in end_indexes:
                               if start_id < end_id:
                                     output.append(
                                         (
                                             start_id,
                                             end_id,
                                             p_start[idx][start_id].item(),
                                             p_end[idx][end_id].item(),
                                         )			)

                   output.sort(key=lambda __lowerCamelCase			: (tup[2] * tup[3])   , reverse=__lowerCamelCase			)
                   lowerCamelCase__						:							str       =    []
                   for k in range(len(__lowerCamelCase			)			):
                         if output[k][2] * output[k][3] >= thresh:
                               lowerCamelCase__       ,  lowerCamelCase__						:							Optional[int]       =    output[k][0], output[k][1]
                               lowerCamelCase__						:							Tuple       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]			)
                               temp.append((decoded, output[k][2] * output[k][3])			)

                   final_outputs.append(__lowerCamelCase			)

             return final_outputs


