

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


lowercase_       		=  logging.get_logger(__name__)


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

lowercase_       		=  {
    "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",
    },
}

lowercase_       		=  {
    "allenai/led-base-16384": 16384,
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							VOCAB_FILES_NAMES
    lowerCamelCase			=							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase			=							LEDTokenizer
    lowerCamelCase			=							['input_ids', 'attention_mask']


    def __init__(      self :       List[Any],lowercase_ :       Dict=None,lowercase_ :       Optional[int]=None,lowercase_ :       Any=None,lowercase_ :       Optional[int]="replace",lowercase_ :       List[Any]="<s>",lowercase_ :       int="</s>",lowercase_ :       List[str]="</s>",lowercase_ :       str="<s>",lowercase_ :       str="<unk>",lowercase_ :       Dict="<pad>",lowercase_ :       str="<mask>",lowercase_ :       List[Any]=False,lowercase_ :       Tuple=True,**lowercase_ :       Optional[Any],)->    str:





        '''simple docstring'''







        super().__init__(
            lowercase_,lowercase_,tokenizer_file=lowercase_,errors=lowercase_,bos_token=lowercase_,eos_token=lowercase_,sep_token=lowercase_,cls_token=lowercase_,unk_token=lowercase_,pad_token=lowercase_,mask_token=lowercase_,add_prefix_space=lowercase_,trim_offsets=lowercase_,**lowercase_,)

        A__          =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('add_prefix_space',lowercase_		) != add_prefix_space:
            A__          =   getattr(lowercase_,pre_tok_state.pop('type'		)		)
            A__          =   add_prefix_space
            A__          =   pre_tok_class(**lowercase_		)

        A__          =   add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        A__          =   'post_processor'
        A__          =   getattr(self.backend_tokenizer,lowercase_,lowercase_		)
        if tokenizer_component_instance:
            A__          =   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:
                A__          =   tuple(state['sep']		)
            if "cls" in state:
                A__          =   tuple(state['cls']		)

            A__          =   False

            if state.get('add_prefix_space',lowercase_		) != add_prefix_space:
                A__          =   add_prefix_space
                A__          =   True

            if state.get('trim_offsets',lowercase_		) != trim_offsets:
                A__          =   trim_offsets
                A__          =   True

            if changes_to_apply:
                A__          =   getattr(lowercase_,state.pop('type'		)		)
                A__          =   component_class(**lowercase_		)
                setattr(self.backend_tokenizer,lowercase_,lowercase_		)


    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def 						snake_case__     (      self :       Any		)->    str:





        '''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 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else value
        A__          =   value


    def 						snake_case__     (      self :       Union[str, Any],*lowercase_ :       Union[str, Any],**lowercase_ :       List[Any]		)->    BatchEncoding:





        '''simple docstring'''







        A__          =   kwargs.get('is_split_into_words',lowercase_		)

        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(*lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       List[Any],*lowercase_ :       int,**lowercase_ :       List[Any]		)->    BatchEncoding:





        '''simple docstring'''







        A__          =   kwargs.get('is_split_into_words',lowercase_		)

        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(*lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       str,lowercase_ :       Optional[str] = None		)->    Tuple[str]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[str],lowercase_ :       str,lowercase_ :       Any=None		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   [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 						snake_case__     (      self :       Optional[Any],lowercase_ :       List[int],lowercase_ :       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 + sep + token_ids_a + sep		) * [0]


    def 						snake_case__     (      self :       str,lowercase_ :       Union[Dict[str, EncodedInput], BatchEncoding],lowercase_ :       Optional[int] = None,lowercase_ :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD,lowercase_ :       Optional[int] = None,lowercase_ :       Optional[bool] = None,)->    dict:





        '''simple docstring'''







        A__          =   super()._pad(
            encoded_inputs=lowercase_,max_length=lowercase_,padding_strategy=lowercase_,pad_to_multiple_of=lowercase_,return_attention_mask=lowercase_,)

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

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

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

        return encoded_inputs




from typing import Dict

from .base import GenericTensor, Pipeline


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'		)
            A__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    Any:





        '''simple docstring'''







        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()


    def __call__(      self :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        return super().__call__(*lowercase_,**lowercase_		)

