






"""simple docstring"""



import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


_lowerCAmelCase	:	str				=      get_logger(__name__)


_lowerCAmelCase	:	int				=      r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""




class 			lowerCAmelCase__			:
   @add_start_docstrings(snake_case__							)
   def __call__(		self      :	int							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							):


        '''simple docstring'''
        raise NotImplementedError(
            f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)




class 			lowerCAmelCase__			:
   @add_start_docstrings(snake_case__							)
   def __call__(		self      :	str							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							):


        '''simple docstring'''
        raise NotImplementedError(
            f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'							)




class 			lowerCAmelCase__			(    __magic_name__   ):
   @add_start_docstrings(snake_case__							)
   def __call__(		self      :	Tuple							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							,			**snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        for processor in self:
             UpperCAmelCase__						:    str							=							inspect.signature(processor.__call__							).parameters
             if len(snake_case__							) > 3:
                  if not all(arg in kwargs for arg in list(function_args.keys()							)[2:]							):
                       raise ValueError(
                           f'Make sure that all the required parameters: {list(function_args.keys()							)} for '
                           f'{processor.__class__} are passed to the logits processor.'							)
                  UpperCAmelCase__						:    int							=							processor(snake_case__							,			snake_case__							,			snake_case__							,			**snake_case__							)
             else:
                  UpperCAmelCase__						:    Tuple							=							processor(snake_case__							,			snake_case__							,			snake_case__							)
        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	Any							,			snake_case__      :	float							):


        '''simple docstring'''
        if not isinstance(snake_case__							,			snake_case__							) or not (temperature > 0):
             raise ValueError(f'`temperature` has to be a strictly positive float, but is {temperature}'							)

        UpperCAmelCase__						:    Optional[int]							=							temperature




   def __call__(		self      :	Union[str, Any]							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							scores / self.temperature
        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	float							,			snake_case__      :	float = -float("Inf"							)							,			snake_case__      :	int = 1							):


        '''simple docstring'''
        if not isinstance(snake_case__							,			snake_case__							) or (top_p < 0 or top_p > 1.0):
             raise ValueError(f'`top_p` has to be a float > 0 and < 1, but is {top_p}'							)
        if not isinstance(snake_case__							,			snake_case__							) or (min_tokens_to_keep < 1):
             raise ValueError(f'`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'							)

        UpperCAmelCase__						:    Any							=							top_p
        UpperCAmelCase__						:    Dict							=							filter_value
        UpperCAmelCase__						:    Optional[Any]							=							min_tokens_to_keep




   def __call__(		self      :	List[Any]							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							lax.top_k(snake_case__							,			scores.shape[-1]							)

        UpperCAmelCase__						:    Dict							=							jnp.full_like(snake_case__							,			self.filter_value							)
        UpperCAmelCase__						:    int							=							jax.nn.softmax(snake_case__							,			axis=-1							).cumsum(axis=-1							)
        UpperCAmelCase__						:    Optional[int]							=							cumulative_probs < self.top_p

        # include the token that is higher than top_p as well
        UpperCAmelCase__						:    int							=							jnp.roll(snake_case__							,			1							)
        score_mask |= score_mask.at[:, 0].set(snake_case__							)

        # min tokens to keep
        UpperCAmelCase__						:    Optional[int]							=							score_mask.at[:, : self.min_tokens_to_keep].set(snake_case__							)

        UpperCAmelCase__						:    List[str]							=							jnp.where(snake_case__							,			snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Any							=							jax.lax.sort_key_val(snake_case__							,			snake_case__							)[-1]

        return next_scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	float = -float("Inf"							)							,			snake_case__      :	int = 1							):


        '''simple docstring'''
        if not isinstance(snake_case__							,			snake_case__							) or top_k <= 0:
             raise ValueError(f'`top_k` has to be a strictly positive integer, but is {top_k}'							)

        UpperCAmelCase__						:    Optional[int]							=							max(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							filter_value




   def __call__(		self      :	Optional[Any]							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    int							=							scores.shape
        UpperCAmelCase__						:    Optional[Any]							=							jnp.full(batch_size * vocab_size							,			self.filter_value							)

        UpperCAmelCase__						:    Dict							=							min(self.top_k							,			scores.shape[-1]							)  # Safety check
        UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							lax.top_k(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							jnp.broadcast_to((jnp.arange(snake_case__							) * vocab_size)[:, None]							,			(batch_size, topk)							).flatten()
        UpperCAmelCase__						:    Dict							=							topk_scores.flatten()
        UpperCAmelCase__						:    Any							=							topk_indices.flatten() + shift

        UpperCAmelCase__						:    Dict							=							next_scores_flat.at[topk_indices_flat].set(snake_case__							)
        UpperCAmelCase__						:    Union[str, Any]							=							next_scores_flat.reshape(snake_case__							,			snake_case__							)
        return next_scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							bos_token_id




   def __call__(		self      :	Optional[int]							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							jnp.full(scores.shape							,			-float("inf"							)							)

        UpperCAmelCase__						:    Optional[int]							=							1 - jnp.bool_(cur_len - 1							)

        UpperCAmelCase__						:    str							=							jnp.where(snake_case__							,			new_scores.at[:, self.bos_token_id].set(0							)							,			snake_case__							)

        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							max_length
        UpperCAmelCase__						:    Any							=							eos_token_id




   def __call__(		self      :	Dict							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							jnp.full(scores.shape							,			-float("inf"							)							)

        UpperCAmelCase__						:    Optional[int]							=							1 - jnp.bool_(cur_len - self.max_length + 1							)

        UpperCAmelCase__						:    Union[str, Any]							=							jnp.where(snake_case__							,			new_scores.at[:, self.eos_token_id].set(0							)							,			snake_case__							)

        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	Any							,			snake_case__      :	int							,			snake_case__      :	int							):


        '''simple docstring'''
        if not isinstance(snake_case__							,			snake_case__							) or min_length < 0:
             raise ValueError(f'`min_length` has to be a positive integer, but is {min_length}'							)

        if not isinstance(snake_case__							,			snake_case__							) or eos_token_id < 0:
             raise ValueError(f'`eos_token_id` has to be a positive integer, but is {eos_token_id}'							)

        UpperCAmelCase__						:    Optional[int]							=							min_length
        UpperCAmelCase__						:    Union[str, Any]							=							eos_token_id




   def __call__(		self      :	Tuple							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        # create boolean flag to decide if min length penalty should be applied
        UpperCAmelCase__						:    Optional[Any]							=							1 - jnp.clip(cur_len - self.min_length							,			0							,			1							)

        UpperCAmelCase__						:    List[str]							=							jnp.where(snake_case__							,			scores.at[:, self.eos_token_id].set(-float("inf"							)							)							,			snake_case__							)

        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	Union[str, Any]							,			snake_case__      :	Optional[int]							,			snake_case__      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							list(snake_case__							)
        UpperCAmelCase__						:    Dict							=							begin_index




   def __call__(		self      :	str							,			snake_case__      :	str							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							1 - jnp.bool_(cur_len - self.begin_index							)

        UpperCAmelCase__						:    str							=							jnp.where(snake_case__							,			scores.at[:, self.begin_suppress_tokens].set(-float("inf"							)							)							,			snake_case__							)

        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	Union[str, Any]							,			snake_case__      :	list							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							list(snake_case__							)




   def __call__(		self      :	Union[str, Any]							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							scores.at[..., self.suppress_tokens].set(-float("inf"							)							)

        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	int							,			snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							dict(snake_case__							)
        # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
        # index of the array corresponds to the index of the token to be forced, for XLA compatibility.
        # Indexes without forced tokens will have a negative value.
        UpperCAmelCase__						:    Tuple							=							jnp.ones((max(force_token_map.keys()							) + 1)							,			dtype=jnp.intaa							) * -1
        for index, token in force_token_map.items():
             if token is not None:
                  UpperCAmelCase__						:    Any							=							force_token_array.at[index].set(snake_case__							)
        UpperCAmelCase__						:    str							=							jnp.intaa(snake_case__							)




   def __call__(		self      :	Union[str, Any]							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	jnp.ndarray							,			snake_case__      :	int							):


        '''simple docstring'''
        def _force_token(snake_case__      :	str							):
             UpperCAmelCase__						:    Any							=							scores.shape[0]
             UpperCAmelCase__						:    Union[str, Any]							=							self.force_token_array[generation_idx]

             UpperCAmelCase__						:    List[str]							=							jnp.ones_like(snake_case__							,			dtype=scores.dtype							) * -float("inf"							)
             UpperCAmelCase__						:    Optional[int]							=							jnp.zeros((batch_size, 1)							,			dtype=scores.dtype							)
             UpperCAmelCase__						:    Tuple							=							lax.dynamic_update_slice(snake_case__							,			snake_case__							,			(0, current_token)							)
             return new_scores

        UpperCAmelCase__						:    Optional[int]							=							lax.cond(
            cur_len >= self.force_token_array.shape[0]							,			lambda: scores							,			lambda: lax.cond(
                self.force_token_array[cur_len] >= 0							,			lambda: _force_token(snake_case__							)							,			lambda: scores							,			)							,			)
        return scores




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	List[Any]							,			snake_case__      :	Tuple							,			snake_case__      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							generate_config.eos_token_id
        UpperCAmelCase__						:    Union[str, Any]							=							generate_config.no_timestamps_token_id
        UpperCAmelCase__						:    str							=							generate_config.no_timestamps_token_id + 1

        UpperCAmelCase__						:    Dict							=							decoder_input_length + 1

        if generate_config.is_multilingual:
             # room for language token and task token
             self.begin_index += 2
        if hasattr(snake_case__							,			"max_initial_timestamp_index"							):
             UpperCAmelCase__						:    List[str]							=							generate_config.max_initial_timestamp_index
        else:
             UpperCAmelCase__						:    Optional[int]							=							model_config.vocab_size
        if self.max_initial_timestamp_index is None:
             UpperCAmelCase__						:    Dict							=							model_config.vocab_size




   def __call__(		self      :	List[str]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        # suppress <|notimestamps|> which is handled by without_timestamps
        UpperCAmelCase__						:    Tuple							=							scores.at[:, self.no_timestamps_token_id].set(-float("inf"							)							)

        def handle_pairs(snake_case__      :	List[Any]							,			snake_case__      :	List[Any]							):
             UpperCAmelCase__						:    int							=							jnp.where((cur_len - self.begin_index) >= 1							,			snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    Optional[int]							=							jnp.where(
                 input_ids_k[cur_len - 1] >= self.timestamp_begin							,			True and last_was_timestamp							,			snake_case__							,			)

             UpperCAmelCase__						:    Optional[Any]							=							jnp.where((cur_len - self.begin_index) < 2							,			snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    Optional[Any]							=							jnp.where(
                 input_ids_k[cur_len - 2] >= self.timestamp_begin							,			snake_case__							,			snake_case__							,			)

             return jnp.where(
                 snake_case__							,			jnp.where(
                     penultimate_was_timestamp > 0							,			scores_k.at[self.timestamp_begin :].set(-float("inf"							)							)							,			scores_k.at[: self.eos_token_id].set(-float("inf"							)							)							,			)							,			snake_case__							,			)

        UpperCAmelCase__						:    List[str]							=							jax.vmap(snake_case__							)(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    Optional[Any]							=							jnp.where(cur_len == self.begin_index							,			snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    List[str]							=							jnp.where(
            self.max_initial_timestamp_index is not None							,			True and apply_max_initial_timestamp							,			snake_case__							,			)

        UpperCAmelCase__						:    Optional[Any]							=							self.timestamp_begin + self.max_initial_timestamp_index

        UpperCAmelCase__						:    Any							=							jnp.where(
            snake_case__							,			scores.at[:, last_allowed + 1 :].set(-float("inf"							)							)							,			snake_case__							,			)

        # if sum of probability over timestamps is above any other token, sample timestamp
        UpperCAmelCase__						:    Any							=							jax.nn.log_softmax(snake_case__							,			axis=-1							)

        def handle_cumulative_probs(snake_case__      :	List[Any]							,			snake_case__      :	List[str]							):
             UpperCAmelCase__						:    List[Any]							=							jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]							,			axis=-1							)
             UpperCAmelCase__						:    Any							=							jnp.max(logprobs_k[: self.timestamp_begin]							)
             return jnp.where(
                 timestamp_logprob > max_text_token_logprob							,			scores_k.at[: self.timestamp_begin].set(-float("inf"							)							)							,			snake_case__							,			)

        UpperCAmelCase__						:    str							=							jax.vmap(snake_case__							)(snake_case__							,			snake_case__							)

        return scores










"""simple docstring"""



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	:	str				=      logging.get_logger(__name__)


_lowerCAmelCase	:	int				=      {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase	:	List[Any]				=      {
    """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	:	List[str]				=      {
    """allenai/led-base-16384""": 16_384,
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    =VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE_    =PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE_    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE_    =LEDTokenizer
   SCREAMING_SNAKE_CASE_    =['''input_ids''', '''attention_mask''']
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	str=None							,			snake_case__      :	List[Any]=None							,			snake_case__      :	Dict=None							,			snake_case__      :	List[str]="replace"							,			snake_case__      :	Optional[int]="<s>"							,			snake_case__      :	List[str]="</s>"							,			snake_case__      :	Union[str, Any]="</s>"							,			snake_case__      :	Dict="<s>"							,			snake_case__      :	Tuple="<unk>"							,			snake_case__      :	Any="<pad>"							,			snake_case__      :	Dict="<mask>"							,			snake_case__      :	int=False							,			snake_case__      :	Optional[int]=True							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(
            snake_case__							,			snake_case__							,			tokenizer_file=snake_case__							,			errors=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			sep_token=snake_case__							,			cls_token=snake_case__							,			unk_token=snake_case__							,			pad_token=snake_case__							,			mask_token=snake_case__							,			add_prefix_space=snake_case__							,			trim_offsets=snake_case__							,			**snake_case__							,			)

        UpperCAmelCase__						:    str							=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
             UpperCAmelCase__						:    Dict							=							getattr(snake_case__							,			pre_tok_state.pop("type"							)							)
             UpperCAmelCase__						:    str							=							add_prefix_space
             UpperCAmelCase__						:    Any							=							pre_tok_class(**snake_case__							)

        UpperCAmelCase__						:    Dict							=							add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        UpperCAmelCase__						:    List[str]							=							"post_processor"
        UpperCAmelCase__						:    List[Any]							=							getattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
        if tokenizer_component_instance:
             UpperCAmelCase__						:    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:
                  UpperCAmelCase__						:    Optional[Any]							=							tuple(state["sep"]							)
             if "cls" in state:
                  UpperCAmelCase__						:    Any							=							tuple(state["cls"]							)

             UpperCAmelCase__						:    Any							=							False

             if state.get("add_prefix_space"							,			snake_case__							) != add_prefix_space:
                  UpperCAmelCase__						:    Union[str, Any]							=							add_prefix_space
                  UpperCAmelCase__						:    List[Any]							=							True

             if state.get("trim_offsets"							,			snake_case__							) != trim_offsets:
                  UpperCAmelCase__						:    Optional[int]							=							trim_offsets
                  UpperCAmelCase__						:    List[Any]							=							True

             if changes_to_apply:
                  UpperCAmelCase__						:    List[str]							=							getattr(snake_case__							,			state.pop("type"							)							)
                  UpperCAmelCase__						:    Optional[int]							=							component_class(**snake_case__							)
                  setattr(self.backend_tokenizer							,			snake_case__							,			snake_case__							)
   @property
   # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        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       __a							(		self      :	Any							,			snake_case__      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__							) if isinstance(snake_case__							,			snake_case__							) else value
        UpperCAmelCase__						:    Dict							=							value
   def       __a							(		self      :	str							,			*snake_case__      :	Any							,			**snake_case__      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							kwargs.get("is_split_into_words"							,			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(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	List[str]							,			*snake_case__      :	Union[str, Any]							,			**snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							kwargs.get("is_split_into_words"							,			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(*snake_case__							,			**snake_case__							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	Optional[str] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self._tokenizer.model.save(snake_case__							,			name=snake_case__							)
        return tuple(snake_case__							)
   def       __a							(		self      :	str							,			snake_case__      :	List[Any]							,			snake_case__      :	str=None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[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       __a							(		self      :	str							,			snake_case__      :	List[int]							,			snake_case__      :	Optional[List[int]] = None							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							[self.sep_token_id]
        UpperCAmelCase__						:    Optional[Any]							=							[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       __a							(		self      :	Any							,			snake_case__      :	Union[Dict[str, EncodedInput], BatchEncoding]							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[bool] = None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							super()._pad(
            encoded_inputs=snake_case__							,			max_length=snake_case__							,			padding_strategy=snake_case__							,			pad_to_multiple_of=snake_case__							,			return_attention_mask=snake_case__							,			)

        # Load from model defaults
        if return_attention_mask is None:
             UpperCAmelCase__						:    Optional[int]							=							"attention_mask" in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
             UpperCAmelCase__						:    List[Any]							=							encoded_inputs[self.model_input_names[0]]
             # `global_attention_mask` need to have the same length as other (sequential) inputs.
             UpperCAmelCase__						:    Any							=							len(encoded_inputs["global_attention_mask"]							) != len(snake_case__							)

             if needs_to_be_padded:
                  UpperCAmelCase__						:    List[str]							=							len(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`
                       UpperCAmelCase__						:    Dict							=							(
                           encoded_inputs["global_attention_mask"] + [-1] * difference
                       )
                  elif self.padding_side == "left":
                       UpperCAmelCase__						:    Dict							=							[-1] * difference + encoded_inputs[
                           "global_attention_mask"
                       ]
                  else:
                       raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

        return encoded_inputs


