






'''simple docstring'''

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


A:      int  				=				logging.get_logger(__name__)

A:      int  				=				{'''vocab_file''': '''sentencepiece.bpe.model'''}

A:      Optional[Any]  				=				{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

A:      Optional[int]  				=				{
    '''moussaKam/mbarthez''': 1_0_2_4,
    '''moussaKam/barthez''': 1_0_2_4,
    '''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}

A:      Dict  				=				'''▁'''






class 							__magic_name__     (	__a				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      List[str]        =  ['''input_ids''', '''attention_mask''']
	def __init__(							self  ,      _lowercase  ,      _lowercase="<s>"  ,      _lowercase="</s>"  ,      _lowercase="</s>"  ,      _lowercase="<s>"  ,      _lowercase="<unk>"  ,      _lowercase="<pad>"  ,      _lowercase="<mask>"  ,      _lowercase = None  ,      **_lowercase  ,      )						->  List[Any]:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_      :   List[str]        =			AddedToken(snake_case__  ,      lstrip=snake_case__  ,      rstrip=snake_case__       ) if isinstance(snake_case__  ,      snake_case__       ) else mask_token

							lowercase_      :   Union[str, Any]        =			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=snake_case__  ,      eos_token=snake_case__  ,      unk_token=snake_case__  ,      sep_token=snake_case__  ,      cls_token=snake_case__  ,      pad_token=snake_case__  ,      mask_token=snake_case__  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **snake_case__  ,      )

							lowercase_      :   int        =			vocab_file
							lowercase_      :   int        =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(str(snake_case__       )       )

							lowercase_      :   Dict        =			{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							lowercase_      :   List[str]        =			len(self.sp_model       ) - 1
							lowercase_      :   Tuple        =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Any:

							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_      :   str        =			[self.cls_token_id]
							lowercase_      :   Optional[int]        =			[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = False       )						->  Dict:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=snake_case__  ,      token_ids_a=snake_case__  ,      already_has_special_tokens=snake_case__       )

							if token_ids_a is None:
													return [1] + ([0] * len(snake_case__       )) + [1]
							return [1] + ([0] * len(snake_case__       )) + [1, 1] + ([0] * len(snake_case__       )) + [1]
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[str]:
							lowercase_      :   Optional[Any]        =			[self.sep_token_id]
							lowercase_      :   List[str]        =			[self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
	@property
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							return len(self.sp_model       )
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Any        =			{self.convert_ids_to_tokens(snake_case__       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  List[Any]:
							return self.sp_model.encode(snake_case__  ,      out_type=snake_case__       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[Any]:
							if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
							lowercase_      :   str        =			self.sp_model.PieceToId(snake_case__       )

							return spm_id if spm_id else self.unk_token_id
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Union[str, Any]:
							if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(snake_case__       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  int:
							lowercase_      :   Dict        =			[]
							lowercase_      :   str        =			''
							lowercase_      :   Optional[int]        =			False
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			if not prev_is_special:
																									out_string += " "
																			out_string += self.sp_model.decode(snake_case__       ) + token
																			lowercase_      :   List[str]        =			True
																			lowercase_      :   List[str]        =			[]
													else:
																			current_sub_tokens.append(snake_case__       )
																			lowercase_      :   Dict        =			False
							out_string += self.sp_model.decode(snake_case__       )
							return out_string.strip()
	def __getstate__(							self       )						->  Any:
							lowercase_      :   Dict        =			self.__dict__.copy()
							lowercase_      :   Dict        =			None
							return state
	def __setstate__(							self  ,      _lowercase       )						->  List[Any]:
							lowercase_      :   Optional[Any]        =			d

							# for backward compatibility
							if not hasattr(self  ,      'sp_model_kwargs'       ):
													lowercase_      :   Optional[Any]        =			{}

							lowercase_      :   Tuple        =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(self.vocab_file       )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Tuple:
							if not os.path.isdir(snake_case__       ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
													return
							lowercase_      :   Tuple        =			os.path.join(
							    snake_case__  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(snake_case__       ) and os.path.isfile(self.vocab_file       ):
													copyfile(self.vocab_file  ,      snake_case__       )
							elif not os.path.isfile(self.vocab_file       ):
													with open(snake_case__  ,      'wb'       ) as fi:
																			lowercase_      :   List[str]        =			self.sp_model.serialized_model_proto()
																			fi.write(snake_case__       )

							return (out_vocab_file,)








'''simple docstring'''

import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A:      Any  				=				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
						A:      List[Any]  				=				json.load(f)






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			FSMTForConditionalGeneration.from_pretrained(_lowercase       ).to(_lowercase       )
							if torch_device == "cuda":
													model.half()
							return model







	@parameterized.expand(
	    [
	        ['en-ru', 26.0],
	        ['ru-en', 22.0],
	        ['en-de', 22.0],
	        ['de-en', 29.0],
	    ]       )
	@slow
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowercase_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
