


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__snake_case									=		logging.get_logger(__name__)

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

__snake_case									=		{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__snake_case									=		{
    "yjernite/retribert-base-uncased": 5_1_2,
}


__snake_case									=		{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class      __lowerCamelCase       (_lowerCamelCase		):
       _lowercase											=			VOCAB_FILES_NAMES
       _lowercase											=			PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=			PRETRAINED_INIT_CONFIGURATION
       _lowercase											=			RetriBertTokenizer
       _lowercase											=			["""input_ids""", """attention_mask"""]







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






            '''simple docstring'''




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

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

            __UpperCamelCase      =       do_lower_case







       def 						snake_case_     (       self:      List[str],A_:      List[str],A_:      str=None ):






            '''simple docstring'''




            __UpperCamelCase      =       [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 						snake_case_     (       self:      Tuple,A_:      Dict,A_:      Union[str, Any] = None ):






            '''simple docstring'''




            __UpperCamelCase      =       [self.sep_token_id]
            __UpperCamelCase      =       [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 						snake_case_     (       self:      List[Any],A_:      str,A_:      Optional[Any] = None ):






            '''simple docstring'''




            __UpperCamelCase      =       self._tokenizer.model.save(lowerCAmelCase_,name=lowerCAmelCase_ )
            return tuple(lowerCAmelCase_ )




'''simple docstring'''







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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

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

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

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

