




import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


_lowerCAmelCase  :      List[str]						=      logging.get_logger(__name__)
class 	_UpperCamelCase					:
      def __init__(	self      :Union[str, Any]       ,					lowerCamelCase      :str       ,					lowerCamelCase      :Optional[int]					)   ->	Dict:
       UpperCAmelCase__							       =  question_encoder
       UpperCAmelCase__							       =  generator
       UpperCAmelCase__							       =  self.question_encoder
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :List[str]					)   ->	Union[str, Any]:
       if os.path.isfile(_lowerCAmelCase					):
        raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''					)
       os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase					)
       UpperCAmelCase__							       =  os.path.join(_lowerCAmelCase       ,					"question_encoder_tokenizer"					)
       UpperCAmelCase__							       =  os.path.join(_lowerCAmelCase       ,					"generator_tokenizer"					)
       self.question_encoder.save_pretrained(_lowerCAmelCase					)
       self.generator.save_pretrained(_lowerCAmelCase					)
      @classmethod
      def 		UpperCAmelCase_  (	cls      :Union[str, Any]       ,					lowerCamelCase      :Tuple       ,					**lowerCamelCase      :Optional[int]					)   ->	Dict:
       # dynamically import AutoTokenizer
       from ..auto.tokenization_auto import AutoTokenizer

       UpperCAmelCase__							       =  kwargs.pop("config"       ,					_lowerCAmelCase					)

       if config is None:
        UpperCAmelCase__							       =  RagConfig.from_pretrained(_lowerCAmelCase					)

       UpperCAmelCase__							       =  AutoTokenizer.from_pretrained(
           _lowerCAmelCase       ,					config=config.question_encoder       ,					subfolder="question_encoder_tokenizer"					)
       UpperCAmelCase__							       =  AutoTokenizer.from_pretrained(
           _lowerCAmelCase       ,					config=config.generator       ,					subfolder="generator_tokenizer"					)
       return cls(question_encoder=_lowerCAmelCase       ,					generator=_lowerCAmelCase					)
      def __call__(	self      :int       ,					*lowerCamelCase      :List[Any]       ,					**lowerCamelCase      :Union[str, Any]					)   ->	Dict:
       return self.current_tokenizer(*_lowerCAmelCase       ,					**_lowerCAmelCase					)
      def 		UpperCAmelCase_  (	self      :Any       ,					*lowerCamelCase      :str       ,					**lowerCamelCase      :Dict					)   ->	Optional[int]:
       return self.generator.batch_decode(*_lowerCAmelCase       ,					**_lowerCAmelCase					)
      def 		UpperCAmelCase_  (	self      :str       ,					*lowerCamelCase      :List[str]       ,					**lowerCamelCase      :Optional[int]					)   ->	Any:
       return self.generator.decode(*_lowerCAmelCase       ,					**_lowerCAmelCase					)
      def 		UpperCAmelCase_  (	self      :int					)   ->	List[Any]:
       UpperCAmelCase__							       =  self.question_encoder
      def 		UpperCAmelCase_  (	self      :str					)   ->	List[Any]:
       UpperCAmelCase__							       =  self.generator







      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :Optional[List[str]] = None       ,					lowerCamelCase      :Optional[int] = None       ,					lowerCamelCase      :Optional[int] = None       ,					lowerCamelCase      :str = "longest"       ,					lowerCamelCase      :str = None       ,					lowerCamelCase      :bool = True       ,					**lowerCamelCase      :Tuple       ,					)   ->	Optional[int]:
       warnings.warn(
           "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
           "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
           "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
           "details"       ,					_lowerCAmelCase       ,					)
       if max_length is None:
        UpperCAmelCase__							       =  self.current_tokenizer.model_max_length
       UpperCAmelCase__							       =  self(
           _lowerCAmelCase       ,					add_special_tokens=_lowerCAmelCase       ,					return_tensors=_lowerCAmelCase       ,					max_length=_lowerCAmelCase       ,					padding=_lowerCAmelCase       ,					truncation=_lowerCAmelCase       ,					**_lowerCAmelCase       ,					)
       if tgt_texts is None:
        return model_inputs
       # Process tgt_texts
       if max_target_length is None:
        UpperCAmelCase__							       =  self.current_tokenizer.model_max_length
       UpperCAmelCase__							       =  self(
           text_target=_lowerCAmelCase       ,					add_special_tokens=_lowerCAmelCase       ,					return_tensors=_lowerCAmelCase       ,					padding=_lowerCAmelCase       ,					max_length=_lowerCAmelCase       ,					truncation=_lowerCAmelCase       ,					**_lowerCAmelCase       ,					)
       UpperCAmelCase__							       =  labels["input_ids"]
       return model_inputs











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


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

lowerCamelCase__ :   Dict					=							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__ :   Tuple					=							{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__ :   int					=							{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


lowerCamelCase__ :   str					=							{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_INIT_CONFIGURATION
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    SqueezeBertTokenizer



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

       SCREAMING_SNAKE_CASE_							=			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
       ):
             SCREAMING_SNAKE_CASE_							=			getattr(_lowerCAmelCase				,				normalizer_state.pop('type'						)						)
             SCREAMING_SNAKE_CASE_							=			do_lower_case
             SCREAMING_SNAKE_CASE_							=			strip_accents
             SCREAMING_SNAKE_CASE_							=			tokenize_chinese_chars
             SCREAMING_SNAKE_CASE_							=			normalizer_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			do_lower_case



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[int]=None						):
       SCREAMING_SNAKE_CASE_							=			[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       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.sep_token_id]
       SCREAMING_SNAKE_CASE_							=			[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       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       SCREAMING_SNAKE_CASE_							=			self._tokenizer.model.save(_lowerCAmelCase				,				name=_lowerCAmelCase						)
       return tuple(_lowerCAmelCase						)