





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							   =							logging.get_logger(__name__)

__A							   =							{"vocab_file": "sentencepiece.bpe.model"}

__A							   =							{
    "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							   =							{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__A							   =							"▁"



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     VOCAB_FILES_NAMES
 lowercase_						=     PRETRAINED_VOCAB_FILES_MAP
 lowercase_						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_						=     ["input_ids", "attention_mask"]

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



   '''simple docstring'''



   lowerCamelCase__: str         =AddedToken(UpperCAmelCase_			,					lstrip=UpperCAmelCase_			,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_			,					UpperCAmelCase_) else mask_token

   lowerCamelCase__: Optional[Any]         ={} if sp_model_kwargs is None else sp_model_kwargs

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

   lowerCamelCase__: Optional[int]         =vocab_file
   lowerCamelCase__: List[str]         =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(UpperCAmelCase_))

   lowerCamelCase__: Tuple         ={"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

   lowerCamelCase__: int         =len(self.sp_model) - 1
   lowerCamelCase__: Union[str, Any]         ={v: k for k, v in self.fairseq_tokens_to_ids.items()}

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  List[int]			,					UpperCAmelCase_							:  Optional[List[int]] = None)			->List[int]:



   '''simple docstring'''



   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   lowerCamelCase__: str         =[self.cls_token_id]
   lowerCamelCase__: List[str]         =[self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  List[int]			,					UpperCAmelCase_							:  Optional[List[int]] = None			,					UpperCAmelCase_							:  bool = False)			->List[int]:



   '''simple docstring'''



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

   if token_ids_a is None:
     return [1] + ([0] * len(UpperCAmelCase_)) + [1]
   return [1] + ([0] * len(UpperCAmelCase_)) + [1, 1] + ([0] * len(UpperCAmelCase_)) + [1]

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  List[int]			,					UpperCAmelCase_							:  Optional[List[int]] = None)			->List[int]:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =[self.sep_token_id]
   lowerCamelCase__: int         =[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 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->int:



   '''simple docstring'''



   return len(self.sp_model)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: List[str]         ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

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



   '''simple docstring'''



   return self.sp_model.encode(UpperCAmelCase_			,					out_type=UpperCAmelCase_)

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



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__: int         =self.sp_model.PieceToId(UpperCAmelCase_)

   return spm_id if spm_id else self.unk_token_id

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int]			,					UpperCAmelCase_							:  List[Any])			->Dict:



   '''simple docstring'''



   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  str)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: int         =[]
   lowerCamelCase__: Any         =""
   lowerCamelCase__: 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(UpperCAmelCase_) + token
       lowerCamelCase__: int         =True
       lowerCamelCase__: Optional[int]         =[]
     else:
       current_sub_tokens.append(UpperCAmelCase_)
       lowerCamelCase__: Dict         =False
   out_string += self.sp_model.decode(UpperCAmelCase_)
   return out_string.strip()

 def __getstate__(self							:  Optional[int])			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self.__dict__.copy()
   lowerCamelCase__: Union[str, Any]         =None
   return state

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



   '''simple docstring'''



   lowerCamelCase__: Dict         =d

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

   lowerCamelCase__: Any         =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)







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



   '''simple docstring'''



   if not os.path.isdir(UpperCAmelCase_):
     logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
     return
   lowerCamelCase__: List[Any]         =os.path.join(
       UpperCAmelCase_			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

   if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file			,					UpperCAmelCase_)
   elif not os.path.isfile(self.vocab_file):
     with open(UpperCAmelCase_			,					"wb") as fi:
       lowerCamelCase__: str         =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)



"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 			snake_case  (				__lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   BloomTokenizerFast
							UpperCAmelCase__      =   True
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   '''tokenizer_file'''
							UpperCAmelCase__      =   {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}

							def _lowercase       (self     ):

									"""simple docstring"""







									super().setUp()
									SCREAMING_SNAKE_CASE_    			=			BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''     )
									tokenizer.save_pretrained(self.tmpdirname     )

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map     )
									return BloomTokenizerFast.from_pretrained(self.tmpdirname							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE_    			=			['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
									SCREAMING_SNAKE_CASE_    			=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_     )['''input_ids''']
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_=6     ):

									"""simple docstring"""







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
													SCREAMING_SNAKE_CASE_    			=			self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
													# tokenizer_r.pad_token = None # Hotfixing padding = None
													# Simple input
													SCREAMING_SNAKE_CASE_    			=			'''This is a simple input'''
													SCREAMING_SNAKE_CASE_    			=			['''This is a simple input 1''', '''This is a simple input 2''']
													SCREAMING_SNAKE_CASE_    			=			('''This is a simple input''', '''This is a pair''')
													SCREAMING_SNAKE_CASE_    			=			[
													    ('''This is a simple input 1''', '''This is a simple input 2'''),
													    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
													]

													# Simple input tests
													try:
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )

															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.encode(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
															tokenizer_r.batch_encode_plus(SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_     )
													except ValueError:
															self.fail('''Bloom Tokenizer should be able to deal with padding'''     )

													SCREAMING_SNAKE_CASE_    			=			None  # Hotfixing padding = None
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Simple input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(SCREAMING_SNAKE_CASE_							,							tokenizer_r.encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''     )

													# Pair input
													self.assertRaises(
													    SCREAMING_SNAKE_CASE_							,							tokenizer_r.batch_encode_plus							,							SCREAMING_SNAKE_CASE_							,							max_length=SCREAMING_SNAKE_CASE_							,							padding='''max_length'''							,							)

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE_    			=			load_dataset('''xnli'''							,							'''all_languages'''							,							split='''test'''							,							streaming=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			next(iter(SCREAMING_SNAKE_CASE_     )     )['''premise''']  # pick up one data
									SCREAMING_SNAKE_CASE_    			=			list(sample_data.values()     )

									SCREAMING_SNAKE_CASE_    			=			list(map(tokenizer.encode							,							SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			[tokenizer.decode(SCREAMING_SNAKE_CASE_							,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_     ) for x in output_tokens]
									self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self     ):

									"""simple docstring"""







									self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map     )							,							1     )
									self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()     )[0]     )							,							1     )