


import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCamelCase						=		logging.get_logger(__name__)


lowerCamelCase						=		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase						=		{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase						=		{
    '''allenai/led-base-16384''': 1_6384,
}





class  _a							(    _lowercase):
	_a  :  Union[str, Any]				   =							VOCAB_FILES_NAMES
	_a  :  Tuple				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  Any				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  List[Any]				   =							LEDTokenizer
	_a  :  int				   =							['''input_ids''', '''attention_mask''']




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, _SCREAMING_SNAKE_CASE						:		str=None		, _SCREAMING_SNAKE_CASE						:		Optional[int]=None		, _SCREAMING_SNAKE_CASE						:		int="replace"		, _SCREAMING_SNAKE_CASE						:		List[Any]="<s>"		, _SCREAMING_SNAKE_CASE						:		List[str]="</s>"		, _SCREAMING_SNAKE_CASE						:		int="</s>"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="<s>"		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="<unk>"		, _SCREAMING_SNAKE_CASE						:		int="<pad>"		, _SCREAMING_SNAKE_CASE						:		List[Any]="<mask>"		, _SCREAMING_SNAKE_CASE						:		str=False		, _SCREAMING_SNAKE_CASE						:		List[Any]=True		, **_SCREAMING_SNAKE_CASE						:		Dict		, )->	Tuple:
			super().__init__(
			    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, tokenizer_file=_SCREAMING_SNAKE_CASE		, errors=_SCREAMING_SNAKE_CASE		, bos_token=_SCREAMING_SNAKE_CASE		, eos_token=_SCREAMING_SNAKE_CASE		, sep_token=_SCREAMING_SNAKE_CASE		, cls_token=_SCREAMING_SNAKE_CASE		, unk_token=_SCREAMING_SNAKE_CASE		, pad_token=_SCREAMING_SNAKE_CASE		, mask_token=_SCREAMING_SNAKE_CASE		, add_prefix_space=_SCREAMING_SNAKE_CASE		, trim_offsets=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Any   					= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
			if pre_tok_state.get('''add_prefix_space'''		, _SCREAMING_SNAKE_CASE		) != add_prefix_space:
					lowerCAmelCase__       :      List[Any]   					= getattr(_SCREAMING_SNAKE_CASE		, pre_tok_state.pop('''type'''		)		)
					lowerCAmelCase__       :      int   					= add_prefix_space
					lowerCAmelCase__       :      Tuple   					= pre_tok_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Any   					= add_prefix_space

			# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
			lowerCAmelCase__       :      str   					= '''post_processor'''
			lowerCAmelCase__       :      str   					= getattr(self.backend_tokenizer		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			if tokenizer_component_instance:
					lowerCAmelCase__       :      Optional[int]   					= json.loads(tokenizer_component_instance.__getstate__()		)

					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
					if "sep" in state:
							lowerCAmelCase__       :      Union[str, Any]   					= tuple(state['''sep''']		)
					if "cls" in state:
							lowerCAmelCase__       :      int   					= tuple(state['''cls''']		)

					lowerCAmelCase__       :      Tuple   					= False

					if state.get('''add_prefix_space'''		, _SCREAMING_SNAKE_CASE		) != add_prefix_space:
							lowerCAmelCase__       :      List[str]   					= add_prefix_space
							lowerCAmelCase__       :      Any   					= True

					if state.get('''trim_offsets'''		, _SCREAMING_SNAKE_CASE		) != trim_offsets:
							lowerCAmelCase__       :      Optional[int]   					= trim_offsets
							lowerCAmelCase__       :      List[Any]   					= True

					if changes_to_apply:
							lowerCAmelCase__       :      Optional[Any]   					= getattr(_SCREAMING_SNAKE_CASE		, state.pop('''type'''		)		)
							lowerCAmelCase__       :      Optional[int]   					= component_class(**_SCREAMING_SNAKE_CASE		)
							setattr(self.backend_tokenizer		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def 							UpperCAmelCase__( self						:		Tuple		)->	str:
			if self._mask_token is None:
					if self.verbose:
							logger.error('''Using mask_token, but it is not set yet.'''		)
					return None
			return str(self._mask_token		)




	@mask_token.setter
	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[str]		)->	Tuple:
			lowerCAmelCase__       :      Dict   					= AddedToken(_SCREAMING_SNAKE_CASE		, lstrip=_SCREAMING_SNAKE_CASE		, rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		) else value
			lowerCAmelCase__       :      Optional[int]   					= value




	def 							UpperCAmelCase__( self						:		int		, *_SCREAMING_SNAKE_CASE						:		Any		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	BatchEncoding:
			lowerCAmelCase__       :      List[str]   					= kwargs.get('''is_split_into_words'''		, _SCREAMING_SNAKE_CASE		)

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    '''to use it with pretokenized inputs.'''		)

			return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, *_SCREAMING_SNAKE_CASE						:		Dict		, **_SCREAMING_SNAKE_CASE						:		int		)->	BatchEncoding:
			lowerCAmelCase__       :      int   					= kwargs.get('''is_split_into_words'''		, _SCREAMING_SNAKE_CASE		)

			if is_split_into_words and not self.add_prefix_space:
					raise ValueError(
					    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
					    '''to use it with pretokenized inputs.'''		)

			return super()._encode_plus(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		)->	Tuple[str]:
			lowerCAmelCase__       :      Any   					= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE		, name=_SCREAMING_SNAKE_CASE		)
			return tuple(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]=None		)->	Dict:
			lowerCAmelCase__       :      Union[str, Any]   					= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
			if token_ids_a is None:
					return output

			return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None		)->	List[int]:
			lowerCAmelCase__       :      Tuple   					= [self.sep_token_id]
			lowerCAmelCase__       :      Optional[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]




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Union[Dict[str, EncodedInput], BatchEncoding]		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, _SCREAMING_SNAKE_CASE						:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, _SCREAMING_SNAKE_CASE						:		Optional[bool] = None		, )->	dict:
			lowerCAmelCase__       :      Tuple   					= super()._pad(
			    encoded_inputs=_SCREAMING_SNAKE_CASE		, max_length=_SCREAMING_SNAKE_CASE		, padding_strategy=_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=_SCREAMING_SNAKE_CASE		, return_attention_mask=_SCREAMING_SNAKE_CASE		, )

			# Load from model defaults
			if return_attention_mask is None:
					lowerCAmelCase__       :      int   					= '''attention_mask''' in self.model_input_names

			if return_attention_mask and "global_attention_mask" in encoded_inputs:
					lowerCAmelCase__       :      Tuple   					= encoded_inputs[self.model_input_names[0]]
					# `global_attention_mask` need to have the same length as other (sequential) inputs.
					lowerCAmelCase__       :      Dict   					= len(encoded_inputs['''global_attention_mask''']		) != len(_SCREAMING_SNAKE_CASE		)

					if needs_to_be_padded:
							lowerCAmelCase__       :      Optional[Any]   					= len(_SCREAMING_SNAKE_CASE		) - len(encoded_inputs['''global_attention_mask''']		)

							if self.padding_side == "right":
									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
									lowerCAmelCase__       :      List[str]   					= (
									    encoded_inputs['''global_attention_mask'''] + [-1] * difference
									)
							elif self.padding_side == "left":
									lowerCAmelCase__       :      List[str]   					= [-1] * difference + encoded_inputs[
									    '''global_attention_mask'''
									]
							else:
									raise ValueError('''Invalid padding strategy:''' + str(self.padding_side		)		)

			return encoded_inputs








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}





class  _a							(    _lowercase):
	_a  :  List[Any]				   =							'''dpr'''




	def __init__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[str]=3_0522		, _SCREAMING_SNAKE_CASE						:		Optional[int]=768		, _SCREAMING_SNAKE_CASE						:		List[str]=12		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=3072		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=512		, _SCREAMING_SNAKE_CASE						:		int=2		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02		, _SCREAMING_SNAKE_CASE						:		Tuple=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0		, _SCREAMING_SNAKE_CASE						:		List[str]="absolute"		, _SCREAMING_SNAKE_CASE						:		int = 0		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		, )->	Optional[int]:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      Tuple   					= hidden_size
			lowerCAmelCase__       :      Union[str, Any]   					= num_hidden_layers
			lowerCAmelCase__       :      List[Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[int]   					= hidden_act
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Optional[Any]   					= hidden_dropout_prob
			lowerCAmelCase__       :      int   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      List[Any]   					= type_vocab_size
			lowerCAmelCase__       :      Optional[int]   					= initializer_range
			lowerCAmelCase__       :      List[str]   					= layer_norm_eps
			lowerCAmelCase__       :      Dict   					= projection_dim
			lowerCAmelCase__       :      int   					= position_embedding_type




