




'''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							:						List[Any]								=  logging.get_logger(__name__)

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

_lowerCamelCase							:						Any								=  {
    '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							:						List[Any]								=  {'Helsinki-NLP/opus-mt-en-de': 512}
_lowerCamelCase							:						Any								=  {}





class      lowerCamelCase_							(       UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__    = VOCAB_FILES_NAMES
					lowercase__    = PRETRAINED_VOCAB_FILES_MAP
					lowercase__    = PRETRAINED_INIT_CONFIGURATION
					lowercase__    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__    = ["""input_ids""", """attention_mask"""]
					lowercase__    = 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,  ):
							_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}'''

							_snake_case	       =       separate_vocabs
							_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:
									_snake_case	       =       load_json(__UpperCamelCase )
									_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(__UpperCamelCase,  self.sp_model_kwargs )
							_snake_case	       =       load_spm(__UpperCamelCase,  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 				__SCREAMING_SNAKE_CASE	(    self ):
							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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.punc_normalizer(__UpperCamelCase ) if x else ""






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.current_encoder.get(__UpperCamelCase,  self.current_encoder[self.unk_token] )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case	       =       self.language_code_re.match(__UpperCamelCase )
							_snake_case	       =       [match.group(0 )] if match else []
							return code, self.language_code_re.sub("",  __UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_snake_case       ,    _snake_case	       =       self.remove_language_code(__UpperCamelCase )
							_snake_case	       =       self.current_spm.encode(__UpperCamelCase,  out_type=__UpperCamelCase )
							return code + pieces






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							return self.decoder.get(__UpperCamelCase,  self.unk_token )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  **__UpperCamelCase ):
							return super().batch_decode(__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  **__UpperCamelCase ):
							return super().decode(__UpperCamelCase,  **__UpperCamelCase )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_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(__UpperCamelCase ) + token + " "
											_snake_case	       =       []
									else:
											current_sub_tokens.append(__UpperCamelCase )
							out_string += sp_model.decode_pieces(__UpperCamelCase )
							return out_string.strip()






					def 				__SCREAMING_SNAKE_CASE	(    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 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.spm_source
							_snake_case	       =       self.encoder






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.spm_target
							if self.separate_vocabs:
									_snake_case	       =       self.target_encoder






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






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

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

							return tuple(__UpperCamelCase )






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






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






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






					def __getstate__(    self ):
							_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 ):
							_snake_case	       =       d

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

							_snake_case       ,    _snake_case	       =       (load_spm(__UpperCamelCase,  self.sp_model_kwargs ) for f in self.spm_files)
							_snake_case	       =       self.spm_source
							self._setup_normalizer()






					def 				__SCREAMING_SNAKE_CASE	(    self,  *__UpperCamelCase,  **__UpperCamelCase ):
							return 1






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase ):
							_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 				__SCREAMING_SNAKE_CASE	(    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		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case	       =       sentencepiece.SentencePieceProcessor(**lowercase_  )
		spm.Load(lowercase_  )
		return spm






def 		_lowercase		(	lowercase_						,							lowercase_  ):



		'''simple docstring'''







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






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







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











'''simple docstring'''



import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


UpperCAmelCase_				:     Optional[int]     			=						'''block_records.npy'''


UpperCAmelCase_				:     Any     			=						logging.get_logger(__name__)







def 	_UpperCamelCase       (_lowerCamelCase							:   str      ,		_lowerCamelCase							:   int		)->      np.ndarray:




     '''simple docstring'''





     import tensorflow.compat.va as tf

     __snake_case			    =     tf.data.TFRecordDataset(_lowerCamelCase      ,		buffer_size=5_12 * 10_24 * 10_24		)
     __snake_case			    =     blocks_dataset.batch(_lowerCamelCase      ,		drop_remainder=_lowerCamelCase		)
     __snake_case			    =     next(blocks_dataset.take(1		).as_numpy_iterator()		)

     return np_record





class 			lowerCAmelCase      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=2			,     __SCREAMING_SNAKE_CASE=1000			,     __SCREAMING_SNAKE_CASE=100			,     __SCREAMING_SNAKE_CASE=10_0000			,     )					->     Optional[int]:



            '''simple docstring'''




            from scann.scann_ops.py.scann_ops_pybind import builder as Builder

            __snake_case			    =     Builder(db=__SCREAMING_SNAKE_CASE			,     num_neighbors=__SCREAMING_SNAKE_CASE			,     distance_measure='''dot_product'''     )
            __snake_case			    =     builder.tree(
                num_leaves=__SCREAMING_SNAKE_CASE			,     num_leaves_to_search=__SCREAMING_SNAKE_CASE			,     training_sample_size=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     builder.score_ah(dimensions_per_block=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     builder.build()




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     self.searcher.search_batched(question_projection.detach().cpu()     )
            return retrieved_block_ids.astype('''int64'''     )





class 			lowerCAmelCase      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[Any]:



            '''simple docstring'''




            super().__init__()
            __snake_case			    =     block_records
            __snake_case			    =     tokenizer


       def __call__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE="pt"     )					->     Union[str, Any]:



            '''simple docstring'''




            __snake_case			    =     np.take(self.block_records			,     indices=__SCREAMING_SNAKE_CASE			,     axis=0     )

            __snake_case			    =     self.tokenizer.decode(question_input_ids[0]			,     skip_special_tokens=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     []
            __snake_case			    =     []
            for retrieved_block in retrieved_blocks:
                 text.append(__SCREAMING_SNAKE_CASE     )
                 text_pair.append(retrieved_block.decode()     )

            __snake_case			    =     self.tokenizer(
                __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     padding=__SCREAMING_SNAKE_CASE			,     truncation=__SCREAMING_SNAKE_CASE			,     return_special_tokens_mask=__SCREAMING_SNAKE_CASE			,     max_length=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     concat_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE     )

            if answer_ids is not None:
                 return self.block_has_answer(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ) + (concat_inputs_tensors,)
            else:
                 return (None, None, None, concat_inputs_tensors)


       @classmethod
       def 					lowerCAmelCase					(      cls			,     __SCREAMING_SNAKE_CASE			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )					->     Optional[int]:



            '''simple docstring'''




            if os.path.isdir(__SCREAMING_SNAKE_CASE     ):
                 __snake_case			    =     os.path.join(__SCREAMING_SNAKE_CASE			,     _REALM_BLOCK_RECORDS_FILENAME     )
            else:
                 __snake_case			    =     hf_hub_download(
                     repo_id=__SCREAMING_SNAKE_CASE			,     filename=_REALM_BLOCK_RECORDS_FILENAME			,     **__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     np.load(__SCREAMING_SNAKE_CASE			,     allow_pickle=__SCREAMING_SNAKE_CASE     )

            __snake_case			    =     AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE     )

            return cls(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




            np.save(os.path.join(__SCREAMING_SNAKE_CASE			,     _REALM_BLOCK_RECORDS_FILENAME     )			,     self.block_records     )
            # save tokenizer
            self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE     )




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



            '''simple docstring'''




            __snake_case			    =     []
            __snake_case			    =     []
            __snake_case			    =     []
            __snake_case			    =     0

            for input_id in concat_inputs.input_ids:
                 __snake_case			    =     input_id.tolist()
                 # Check answers between two [SEP] tokens
                 __snake_case			    =     input_id_list.index(self.tokenizer.sep_token_id     )
                 __snake_case			    =     first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id     )

                 start_pos.append([]     )
                 end_pos.append([]     )
                 for answer in answer_ids:
                      for idx in range(first_sep_idx + 1			,     __SCREAMING_SNAKE_CASE     ):
                           if answer[0] == input_id_list[idx]:
                                if input_id_list[idx : idx + len(__SCREAMING_SNAKE_CASE     )] == answer:
                                     start_pos[-1].append(__SCREAMING_SNAKE_CASE     )
                                     end_pos[-1].append(idx + len(__SCREAMING_SNAKE_CASE     ) - 1     )

                 if len(start_pos[-1]     ) == 0:
                      has_answers.append(__SCREAMING_SNAKE_CASE     )
                 else:
                      has_answers.append(__SCREAMING_SNAKE_CASE     )
                      if len(start_pos[-1]     ) > max_answers:
                           __snake_case			    =     len(start_pos[-1]     )

        # Pad -1 to max_answers
            for start_pos_, end_pos_ in zip(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     ):
                 if len(start_pos_     ) < max_answers:
                      __snake_case			    =     [-1] * (max_answers - len(start_pos_     ))
                      start_pos_ += padded
                      end_pos_ += padded
            return has_answers, start_pos, end_pos






