





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_lowerCAmelCase 				=     """bert-base-cased"""
_lowerCAmelCase 				=     """google/pegasus-xsum"""
_lowerCAmelCase 				=     [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_lowerCAmelCase 				=     ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_lowerCAmelCase 				=     """patrickvonplaten/t5-tiny-random"""
_lowerCAmelCase 				=     """sshleifer/bart-tiny-random"""
_lowerCAmelCase 				=     """sshleifer/tiny-mbart"""
_lowerCAmelCase 				=     """sshleifer/tiny-marian-en-de"""



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	"""\n""".join(_lowerCAmelCase     )
   Path(_lowerCAmelCase     ).open("""w"""     ).writelines(_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(_lowerCAmelCase  ,f"""{split}.source"""     )  ,_lowerCAmelCase     )
      _dump_articles(os.path.join(_lowerCAmelCase  ,f"""{split}.target"""     )  ,_lowerCAmelCase     )
   return tmp_dir







class    _UpperCAmelCase							(		_lowerCamelCase     ):
      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,       )
      @slow
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[Any]			      =	AutoTokenizer.from_pretrained(a__       )
         A_     :				str			      =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         A_     :				int			      =	max(len(tokenizer.encode(a__       )       ) for a in ARTICLES       )
         A_     :				List[Any]			      =	max(len(tokenizer.encode(a__       )       ) for a in SUMMARIES       )
         A_     :				str			      =	4
         A_     :				List[str]			      =	8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         A_      ,				A_     :				Optional[int]			      =	"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
         A_     :				Dict			      =	SeqaSeqDataset(
             a__      ,       data_dir=a__      ,       type_path="""train"""      ,       max_source_length=a__      ,       max_target_length=a__      ,       src_lang=a__      ,       tgt_lang=a__      ,       )
         A_     :				int			      =	DataLoader(a__      ,       batch_size=2      ,       collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
            assert isinstance(a__      ,       a__       )
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
               continue
            # check language codes in correct place
            A_     :				Optional[int]			      =	shift_tokens_right(batch["""labels"""]      ,       tokenizer.pad_token_id       )
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch
      @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Optional[int]			      =	AutoTokenizer.from_pretrained(a__       )
         A_     :				Dict			      =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         A_     :				int			      =	max(len(tokenizer.encode(a__       )       ) for a in ARTICLES       )
         A_     :				Union[str, Any]			      =	max(len(tokenizer.encode(a__       )       ) for a in SUMMARIES       )
         A_     :				Union[str, Any]			      =	4
         A_     :				Union[str, Any]			      =	LegacySeqaSeqDataset(
             a__      ,       data_dir=a__      ,       type_path="""train"""      ,       max_source_length=20      ,       max_target_length=a__      ,       )
         A_     :				Union[str, Any]			      =	DataLoader(a__      ,       batch_size=2      ,       collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""       )

         A_     :				str			      =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         A_     :				List[Any]			      =	tmp_dir.joinpath("""train.source"""       ).open().readlines()
         A_     :				Dict			      =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(a__      ,       a__      ,       128      ,       a__       )
         A_     :				Optional[Any]			      =	{x.name for x in tmp_dir.iterdir()}
         A_     :				int			      =	{x.name for x in save_dir.iterdir()}
         A_     :				Dict			      =	save_dir.joinpath("""train.source"""       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(a__       ) < len(a__       )
         assert len(a__       ) == 1
         assert len(packed_examples[0]       ) == sum(len(a__       ) for x in orig_examples       )
         assert orig_paths == new_paths
      @pytest.mark.skipif(not FAIRSEQ_AVAILABLE      ,       reason="""This test requires fairseq"""       )
      def 		_lowerCamelCase      (		self       ):
         if not FAIRSEQ_AVAILABLE:
            return
         A_      ,				A_      ,				A_     :				List[Any]			      =	self._get_dataset(max_len=64       )
         A_     :				Tuple			      =	64
         A_     :				Optional[Any]			      =	ds.make_dynamic_sampler(a__      ,       required_batch_size_multiple=a__       )
         A_     :				Tuple			      =	[len(a__       ) for x in batch_sampler]
         assert len(set(a__       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(a__       ) == len(a__       )  # no dropped or added examples
         A_     :				List[str]			      =	DataLoader(a__      ,       batch_sampler=a__      ,       collate_fn=ds.collate_fn      ,       num_workers=2       )
         A_     :				str			      =	[]
         A_     :				int			      =	[]
         for batch in data_loader:
            A_     :				Tuple			      =	batch["""input_ids"""].shape
            A_     :				Any			      =	src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            A_     :				List[Any]			      =	np.product(batch["""input_ids"""].shape       )
            num_src_per_batch.append(a__       )
            if num_src_tokens > (max_tokens * 1.1):
               failures.append(a__       )
         assert num_src_per_batch[0] == max(a__       )
         if failures:
            raise AssertionError(F"""too many tokens in {len(a__       )} batches"""       )
      def 		_lowerCamelCase      (		self       ):
         A_      ,				A_      ,				A_     :				Union[str, Any]			      =	self._get_dataset(max_len=512       )
         A_     :				List[Any]			      =	2
         A_     :				Optional[Any]			      =	ds.make_sortish_sampler(a__      ,       shuffle=a__       )

         A_     :				List[Any]			      =	DataLoader(a__      ,       batch_size=a__      ,       collate_fn=ds.collate_fn      ,       num_workers=2       )
         A_     :				List[str]			      =	DataLoader(a__      ,       batch_size=a__      ,       collate_fn=ds.collate_fn      ,       num_workers=2      ,       sampler=a__       )

         A_     :				Optional[int]			      =	tokenizer.pad_token_id

         def count_pad_tokens(a__      ,       a__="input_ids"       ):
            return [batch[k].eq(a__       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(a__      ,       k="""labels"""       )       ) < sum(count_pad_tokens(a__      ,       k="""labels"""       )       )
         assert sum(count_pad_tokens(a__       )       ) < sum(count_pad_tokens(a__       )       )
         assert len(a__       ) == len(a__       )
      def 		_lowerCamelCase      (		self      ,       a__=1000      ,       a__=128       ):
         if os.getenv("""USE_REAL_DATA"""      ,       a__       ):
            A_     :				int			      =	"""examples/seq2seq/wmt_en_ro"""
            A_     :				Dict			      =	max_len * 2 * 64
            if not Path(a__       ).joinpath("""train.len"""       ).exists():
               save_len_file(a__      ,       a__       )
         else:
            A_     :				Optional[int]			      =	"""examples/seq2seq/test_data/wmt_en_ro"""
            A_     :				str			      =	max_len * 4
            save_len_file(a__      ,       a__       )

         A_     :				List[str]			      =	AutoTokenizer.from_pretrained(a__       )
         A_     :				Any			      =	SeqaSeqDataset(
             a__      ,       data_dir=a__      ,       type_path="""train"""      ,       max_source_length=a__      ,       max_target_length=a__      ,       n_obs=a__      ,       )
         return ds, max_tokens, tokenizer
      def 		_lowerCamelCase      (		self       ):
         A_      ,				A_      ,				A_     :				Optional[int]			      =	self._get_dataset()
         A_     :				List[str]			      =	set(DistributedSortishSampler(a__      ,       256      ,       num_replicas=2      ,       rank=0      ,       add_extra_examples=a__       )       )
         A_     :				str			      =	set(DistributedSortishSampler(a__      ,       256      ,       num_replicas=2      ,       rank=1      ,       add_extra_examples=a__       )       )
         assert idsa.intersection(a__       ) == set()






      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				int			      =	AutoTokenizer.from_pretrained(a__      ,       use_fast=a__       )
         if tok_name == MBART_TINY:
            A_     :				Union[str, Any]			      =	SeqaSeqDataset(
                a__      ,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )      ,       type_path="""train"""      ,       max_source_length=4      ,       max_target_length=8      ,       src_lang="""EN"""      ,       tgt_lang="""FR"""      ,       )
            A_     :				Any			      =	train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
            A_     :				List[str]			      =	SeqaSeqDataset(
                a__      ,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )      ,       type_path="""train"""      ,       max_source_length=4      ,       max_target_length=8      ,       )
            A_     :				Tuple			      =	train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(a__       ) == 1 if tok_name == BART_TINY else len(a__       ) == 0













import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     """▁"""

_lowerCAmelCase 				=     {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_lowerCAmelCase 				=     {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

_lowerCAmelCase 				=     {
    """facebook/m2m100_418M""": 1_024,
}

# fmt: off
_lowerCAmelCase 				=     {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							VOCAB_FILES_NAMES
      a						       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a						       =							PRETRAINED_VOCAB_FILES_MAP
      a						       =							['''input_ids''', '''attention_mask''']

      a						       =							[]
      a						       =							[]
      def __init__(		self      ,       a__      ,       a__      ,       a__=None      ,       a__=None      ,       a__="<s>"      ,       a__="</s>"      ,       a__="</s>"      ,       a__="<pad>"      ,       a__="<unk>"      ,       a__="m2m100"      ,       a__ = None      ,       a__=8      ,       **a__      ,       ):
         A_     :				Any			      =	{} if sp_model_kwargs is None else sp_model_kwargs

         A_     :				Dict			      =	language_codes
         A_     :				Union[str, Any]			      =	FAIRSEQ_LANGUAGE_CODES[language_codes]
         A_     :				int			      =	{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

         A_     :				Union[str, Any]			      =	kwargs.get("""additional_special_tokens"""      ,       []       )
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(a__       )
             for lang_code in fairseq_language_code
             if self.get_lang_token(a__       ) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=a__      ,       tgt_lang=a__      ,       bos_token=a__      ,       eos_token=a__      ,       sep_token=a__      ,       unk_token=a__      ,       pad_token=a__      ,       language_codes=a__      ,       sp_model_kwargs=self.sp_model_kwargs      ,       num_madeup_words=a__      ,       **a__      ,       )

         A_     :				List[str]			      =	vocab_file
         A_     :				Dict			      =	load_json(a__       )
         A_     :				str			      =	{v: k for k, v in self.encoder.items()}
         A_     :				Optional[Any]			      =	spm_file
         A_     :				Any			      =	load_spm(a__      ,       self.sp_model_kwargs       )

         A_     :				Union[str, Any]			      =	len(self.encoder       )

         A_     :				Dict			      =	{
             self.get_lang_token(a__       ): self.encoder_size + i for i, lang_code in enumerate(a__       )
         }
         A_     :				Optional[Any]			      =	{lang_code: self.encoder_size + i for i, lang_code in enumerate(a__       )}
         A_     :				Any			      =	{v: k for k, v in self.lang_token_to_id.items()}

         A_     :				Tuple			      =	src_lang if src_lang is not None else """en"""
         A_     :				str			      =	tgt_lang
         A_     :				Optional[int]			      =	self.get_lang_id(self._src_lang       )
         self.set_src_lang_special_tokens(self._src_lang       )

         A_     :				Optional[int]			      =	num_madeup_words
      @property
      def 		_lowerCamelCase      (		self       ):
         return len(self.encoder       ) + len(self.lang_token_to_id       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self._src_lang
      @src_lang.setter
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Any			      =	new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.sp_model.encode(a__      ,       out_type=a__       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
         return self.encoder.get(a__      ,       self.encoder[self.unk_token]       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
         return self.decoder.get(a__      ,       self.unk_token       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[str]			      =	[]
         A_     :				List[str]			      =	""""""
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += self.sp_model.decode(a__       ) + token
               A_     :				int			      =	[]
            else:
               current_sub_tokens.append(a__       )
         out_string += self.sp_model.decode(a__       )
         return out_string.strip()
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None      ,       a__ = False       ):

         if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=a__      ,       token_ids_a=a__      ,       already_has_special_tokens=a__       )

         A_     :				List[Any]			      =	[1] * len(self.prefix_tokens       )
         A_     :				Dict			      =	[1] * len(self.suffix_tokens       )
         if token_ids_a is None:
            return prefix_ones + ([0] * len(a__       )) + suffix_ones
         return prefix_ones + ([0] * len(a__       )) + ([0] * len(a__       )) + suffix_ones
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[str]			      =	{self.convert_ids_to_tokens(a__       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab
      def __getstate__(		self       ):
         A_     :				List[str]			      =	self.__dict__.copy()
         A_     :				str			      =	None
         return state
      def __setstate__(		self      ,       a__       ):
         A_     :				Any			      =	d

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

         A_     :				int			      =	load_spm(self.spm_file      ,       self.sp_model_kwargs       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = None       ):
         A_     :				Optional[Any]			      =	Path(a__       )
         if not save_dir.is_dir():
            raise OSError(F"""{save_directory} should be a directory"""       )
         A_     :				List[Any]			      =	save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
         )
         A_     :				Tuple			      =	save_dir / (
             (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
         )

         save_json(self.encoder      ,       a__       )

         if os.path.abspath(self.spm_file       ) != os.path.abspath(a__       ) and os.path.isfile(self.spm_file       ):
            copyfile(self.spm_file      ,       a__       )
         elif not os.path.isfile(self.spm_file       ):
            with open(a__      ,       """wb"""       ) as fi:
               A_     :				Tuple			      =	self.sp_model.serialized_model_proto()
               fi.write(a__       )

         return (str(a__       ), str(a__       ))
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__ = "en"      ,       a__ = None      ,       a__ = "ro"      ,       **a__      ,       ):
         A_     :				Optional[int]			      =	src_lang
         A_     :				List[Any]			      =	tgt_lang
         self.set_src_lang_special_tokens(self.src_lang       )
         return super().prepare_seqaseq_batch(a__      ,       a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       **a__       ):
         if src_lang is None or tgt_lang is None:
            raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
         A_     :				List[str]			      =	src_lang
         A_     :				Union[str, Any]			      =	self(a__      ,       add_special_tokens=a__      ,       **a__       )
         A_     :				Any			      =	self.get_lang_id(a__       )
         A_     :				List[Any]			      =	tgt_lang_id
         return inputs
      def 		_lowerCamelCase      (		self       ):
         self.set_src_lang_special_tokens(self.src_lang       )
      def 		_lowerCamelCase      (		self       ):
         self.set_tgt_lang_special_tokens(self.tgt_lang       )
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Tuple			      =	self.get_lang_token(a__       )
         A_     :				Optional[Any]			      =	self.lang_token_to_id[lang_token]
         A_     :				Optional[Any]			      =	[self.cur_lang_id]
         A_     :				Union[str, Any]			      =	[self.eos_token_id]
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				str			      =	self.get_lang_token(a__       )
         A_     :				Optional[int]			      =	self.lang_token_to_id[lang_token]
         A_     :				str			      =	[self.cur_lang_id]
         A_     :				Optional[int]			      =	[self.eos_token_id]
      def 		_lowerCamelCase      (		self      ,       a__       ):
         return self.lang_code_to_token[lang]






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Union[str, Any]			      =	self.get_lang_token(a__       )
         return self.lang_token_to_id[lang_token]






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	sentencepiece.SentencePieceProcessor(**_lowerCAmelCase     )
   spm.Load(str(_lowerCAmelCase     )     )
   return spm



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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







def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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






