

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_rembert import RemBertTokenizer
else:
						A__						=							None

A__						=							logging.get_logger(__name__)
A__						=							{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

A__						=							{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

A__						=							{
    "google/rembert": 256,
}

A__						=							"▁"

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Any       				=  VOCAB_FILES_NAMES
	_lowercase  :   int       				=  PRETRAINED_VOCAB_FILES_MAP
	_lowercase  :   List[str]       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase  :   List[Any]       				=  RemBertTokenizer







	def __init__(   self: int  , __UpperCamelCase: int=None  , __UpperCamelCase: Optional[Any]=None  , __UpperCamelCase: int=True  , __UpperCamelCase: Dict=True  , __UpperCamelCase: Optional[Any]=False  , __UpperCamelCase: int="[CLS]"  , __UpperCamelCase: Any="[SEP]"  , __UpperCamelCase: str="<unk>"  , __UpperCamelCase: str="[SEP]"  , __UpperCamelCase: Optional[Any]="<pad>"  , __UpperCamelCase: Optional[int]="[CLS]"  , __UpperCamelCase: List[str]="[MASK]"  , **__UpperCamelCase: Dict  , ):

						'''simple docstring'''





						__magic_name__							=    AddedToken(__UpperCamelCase  , lstrip=__UpperCamelCase  , rstrip=__UpperCamelCase      ) if isinstance(__UpperCamelCase  , __UpperCamelCase      ) else mask_token

						super().__init__(
						    __UpperCamelCase  , tokenizer_file=__UpperCamelCase  , do_lower_case=__UpperCamelCase  , remove_space=__UpperCamelCase  , keep_accents=__UpperCamelCase  , bos_token=__UpperCamelCase  , eos_token=__UpperCamelCase  , unk_token=__UpperCamelCase  , sep_token=__UpperCamelCase  , pad_token=__UpperCamelCase  , cls_token=__UpperCamelCase  , mask_token=__UpperCamelCase  , **__UpperCamelCase  , )

						__magic_name__							=    do_lower_case
						__magic_name__							=    remove_space
						__magic_name__							=    keep_accents
						__magic_name__							=    vocab_file
						__magic_name__							=    False if not self.vocab_file else True







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: List[int]  , __UpperCamelCase: Optional[List[int]] = None      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: List[int]  , __UpperCamelCase: Optional[List[int]] = None  , __UpperCamelCase: bool = False      ):

						'''simple docstring'''





						if already_has_special_tokens:
											if token_ids_a is not None:
																raise ValueError(
																    'You should not supply a second sequence if the provided sequence of '
																    'ids is already formatted with special tokens for the model.'      )
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
											return [1] + ([0] * len(__UpperCamelCase      )) + [1] + ([0] * len(__UpperCamelCase      )) + [1]
						return [1] + ([0] * len(__UpperCamelCase      )) + [1]







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: List[int]  , __UpperCamelCase: Optional[List[int]] = None      ):

						'''simple docstring'''





						__magic_name__							=    [self.sep_token_id]
						__magic_name__							=    [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      ) * [0] + len(token_ids_a + sep      ) * [1]







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: str  , __UpperCamelCase: Optional[str] = None      ):

						'''simple docstring'''





						if not os.path.isdir(__UpperCamelCase      ):
											logger.error('Vocabulary path ({}) should be a directory'.format(__UpperCamelCase      )      )
											return
						__magic_name__							=    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      ):
											copyfile(self.vocab_file  , __UpperCamelCase      )

						return (out_vocab_file,)







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




