




'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_lowerCamelCase			:	Union[str, Any]							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Dict							 =     {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase			:	Tuple							 =     {
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

_lowerCamelCase			:	Tuple							 =     {"""mobilebert-uncased""": 512}


_lowerCamelCase			:	int							 =     {}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   PRETRAINED_INIT_CONFIGURATION
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   MobileBertTokenizer


    def __init__(    self   :			int      ,				UpperCAmelCase__   :			Any=None      ,				UpperCAmelCase__   :			Union[str, Any]=None      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Dict="[UNK]"      ,				UpperCAmelCase__   :			Optional[int]="[SEP]"      ,				UpperCAmelCase__   :			Tuple="[PAD]"      ,				UpperCAmelCase__   :			Dict="[CLS]"      ,				UpperCAmelCase__   :			int="[MASK]"      ,				UpperCAmelCase__   :			Optional[int]=True      ,				UpperCAmelCase__   :			int=None      ,				**UpperCAmelCase__   :			Union[str, Any]      ,				)		->str:





           '''simple docstring'''

           super().__init__(
               UpperCAmelCase__      ,				tokenizer_file=UpperCAmelCase__      ,				do_lower_case=UpperCAmelCase__      ,				unk_token=UpperCAmelCase__      ,				sep_token=UpperCAmelCase__      ,				pad_token=UpperCAmelCase__      ,				cls_token=UpperCAmelCase__      ,				mask_token=UpperCAmelCase__      ,				tokenize_chinese_chars=UpperCAmelCase__      ,				strip_accents=UpperCAmelCase__      ,				**UpperCAmelCase__      ,				)

           A__   				=					json.loads(self.backend_tokenizer.normalizer.__getstate__())
           if (
               normalizer_state.get('''lowercase'''      ,				UpperCAmelCase__) != do_lower_case
               or normalizer_state.get('''strip_accents'''      ,				UpperCAmelCase__) != strip_accents
               or normalizer_state.get('''handle_chinese_chars'''      ,				UpperCAmelCase__) != tokenize_chinese_chars
           ):
                  A__   				=					getattr(UpperCAmelCase__      ,				normalizer_state.pop('''type'''))
                  A__   				=					do_lower_case
                  A__   				=					strip_accents
                  A__   				=					tokenize_chinese_chars
                  A__   				=					normalizer_class(**UpperCAmelCase__)

           A__   				=					do_lower_case


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union[str, Any]      ,				UpperCAmelCase__   :			Tuple=None)		->Dict:





           '''simple docstring'''

           A__   				=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                  output += token_ids_a + [self.sep_token_id]

           return output


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           A__   				=					[self.sep_token_id]
           A__   				=					[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)		->Tuple[str]:





           '''simple docstring'''

           A__   				=					self._tokenizer.model.save(UpperCAmelCase__      ,				name=UpperCAmelCase__)
           return tuple(UpperCAmelCase__)








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  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

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

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

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

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


