


"""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


_lowerCamelCase  =	logging.get_logger(__name__)

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

_lowerCamelCase  =	{
    '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'
        )
    },
}

_lowerCamelCase  =	{'Helsinki-NLP/opus-mt-en-de': 512}
_lowerCamelCase  =	{}


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







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



				def __init__(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=None							,		UpperCAmelCase__=None							,		UpperCAmelCase__=None							,		UpperCAmelCase__="<unk>"							,		UpperCAmelCase__="</s>"							,		UpperCAmelCase__="<pad>"							,		UpperCAmelCase__=512							,		UpperCAmelCase__ = None							,		UpperCAmelCase__=False							,		**UpperCAmelCase__							,		):
								SCREAMING_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=UpperCAmelCase__							,		target_lang=UpperCAmelCase__							,		unk_token=UpperCAmelCase__							,		eos_token=UpperCAmelCase__							,		pad_token=UpperCAmelCase__							,		model_max_length=UpperCAmelCase__							,		sp_model_kwargs=self.sp_model_kwargs							,		target_vocab_file=UpperCAmelCase__							,		separate_vocabs=UpperCAmelCase__							,		**UpperCAmelCase__							,		)
								assert Path(UpperCAmelCase__  ).exists(), f'''cannot find spm source {source_spm}'''

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

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

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

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

								self._setup_normalizer()



				def 	lowerCAmelCase__		(    self  ):
								try:
												from sacremoses import MosesPunctNormalizer

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



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								return self.punc_normalizer(UpperCAmelCase__  ) if x else ""



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								return self.current_encoder.get(UpperCAmelCase__							,		self.current_encoder[self.unk_token]  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					self.language_code_re.match(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					[match.group(0  )] if match else []
								return code, self.language_code_re.sub(""							,		UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					self.remove_language_code(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					self.current_spm.encode(UpperCAmelCase__							,		out_type=UpperCAmelCase__  )
								return code + pieces



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								return self.decoder.get(UpperCAmelCase__							,		self.unk_token  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		**UpperCAmelCase__  ):
								return super().batch_decode(UpperCAmelCase__							,		**UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		**UpperCAmelCase__  ):
								return super().decode(UpperCAmelCase__							,		**UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								SCREAMING_SNAKE_CASE__  				=					[]
								SCREAMING_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(UpperCAmelCase__  ) + token + " "
																SCREAMING_SNAKE_CASE__  				=					[]
												else:
																current_sub_tokens.append(UpperCAmelCase__  )
								out_string += sp_model.decode_pieces(UpperCAmelCase__  )
								return out_string.strip()



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__=None  ):
								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  ):
								SCREAMING_SNAKE_CASE__  				=					self.spm_source
								SCREAMING_SNAKE_CASE__  				=					self.encoder



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.spm_target
								if self.separate_vocabs:
												SCREAMING_SNAKE_CASE__  				=					self.target_encoder



				@property
				def 	lowerCAmelCase__		(    self  ):
								return len(self.encoder  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__ = None  ):
								if not os.path.isdir(UpperCAmelCase__  ):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
								SCREAMING_SNAKE_CASE__  				=					[]

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

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

								return tuple(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self  ):
								return self.get_src_vocab()



				def 	lowerCAmelCase__		(    self  ):
								return dict(self.encoder							,		**self.added_tokens_encoder  )



				def 	lowerCAmelCase__		(    self  ):
								return dict(self.target_encoder							,		**self.added_tokens_decoder  )



				def __getstate__(    self  ):
								SCREAMING_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							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					d

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

								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					(load_spm(UpperCAmelCase__							,		self.sp_model_kwargs  ) for f in self.spm_files)
								SCREAMING_SNAKE_CASE__  				=					self.spm_source
								self._setup_normalizer()



				def 	lowerCAmelCase__		(    self							,		*UpperCAmelCase__							,		**UpperCAmelCase__  ):
								return 1



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_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							,		UpperCAmelCase__							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = False  ):
								if already_has_special_tokens:
												return self._special_token_mask(UpperCAmelCase__  )
								elif token_ids_a is None:
												return self._special_token_mask(UpperCAmelCase__  ) + [1]
								else:
												return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]


def __lowercase  (   lowerCamelCase_	:   str    ,       lowerCamelCase_	:   Dict[str, Any]	):
				SCREAMING_SNAKE_CASE__  				=					sentencepiece.SentencePieceProcessor(**lowerCamelCase_	)
				spm.Load(lowerCamelCase_	)
				return spm


def __lowercase  (   lowerCamelCase_	:   Optional[Any]    ,       lowerCamelCase_	:   str	):
				with open(lowerCamelCase_    ,       "w"	) as f:
								json.dump(lowerCamelCase_    ,       lowerCamelCase_    ,       indent=2	)


def __lowercase  (   lowerCamelCase_	:   str	):
				with open(lowerCamelCase_    ,       "r"	) as f:
								return json.load(lowerCamelCase_	)




"""simple docstring"""






import torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

								if isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					None
												for l in x:
																SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
																    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)
																SCREAMING_SNAKE_CASE__  				=					torch.tensor([len(UpperCAmelCase__  )]  )
																if d is not None:
																				for k in d.keys():
																								SCREAMING_SNAKE_CASE__  				=					torch.cat((d[k], t[k])							,		0  )
																else:
																				SCREAMING_SNAKE_CASE__  				=					t

												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"  )  )
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"  )  )

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		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							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

												SCREAMING_SNAKE_CASE__  				=					[]
												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 UpperCAmelCase__  : (tup[2] * tup[3])							,		reverse=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					[]
												for k in range(len(UpperCAmelCase__  )  ):
																if output[k][2] * output[k][3] >= thresh:
																				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					output[k][0], output[k][1]
																				SCREAMING_SNAKE_CASE__  				=					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(UpperCAmelCase__  )

								return final_outputs
