
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


a					:	str							=		logging.get_logger(__name__)


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

a					:	Union[str, 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''',
    },
}

a					:	Dict							=		{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}




class 		a_						( __a	):
    a      :				int     =				VOCAB_FILES_NAMES
    a      :				Optional[Any]     =				PRETRAINED_VOCAB_FILES_MAP
    a      :				Optional[Any]     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a      :				List[str]     =				LEDTokenizer
    a      :				List[Any]     =				['input_ids', 'attention_mask']







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






        '''simple docstring'''






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

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

        _UpperCAmelCase          =   add_prefix_space

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

            _UpperCAmelCase          =   False

            if state.get("""add_prefix_space"""     ,					A__     ) != add_prefix_space:
                _UpperCAmelCase          =   add_prefix_space
                _UpperCAmelCase          =   True

            if state.get("""trim_offsets"""     ,					A__     ) != trim_offsets:
                _UpperCAmelCase          =   trim_offsets
                _UpperCAmelCase          =   True

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







    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def      _snake_case					(			self			:       str     )		->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			:       Dict     ,					__UpperCamelCase			:       Tuple     )		->List[Any]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       List[str]     ,					*__UpperCamelCase			:       Union[str, Any]     ,					**__UpperCamelCase			:       int     )		->BatchEncoding:






        '''simple docstring'''






        _UpperCAmelCase          =   kwargs.get("""is_split_into_words"""     ,					A__     )

        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(*A__     ,					**A__     )







    def      _snake_case					(			self			:       Any     ,					*__UpperCamelCase			:       Union[str, Any]     ,					**__UpperCamelCase			:       Dict     )		->BatchEncoding:






        '''simple docstring'''






        _UpperCAmelCase          =   kwargs.get("""is_split_into_words"""     ,					A__     )

        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(*A__     ,					**A__     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[str] = None     )		->Tuple[str]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[int]=None     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   [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			:       List[Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Dict = None     )		->List[int]:






        '''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 + sep + token_ids_a + sep     ) * [0]







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Optional[Any] = None     ,					__UpperCamelCase			:       Union[str, Any] = PaddingStrategy.DO_NOT_PAD     ,					__UpperCamelCase			:       List[Any] = None     ,					__UpperCamelCase			:       str = None     ,					)		->dict:






        '''simple docstring'''






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

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

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

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

        return encoded_inputs

"""simple docstring"""



from collections.abc import Callable

import numpy as np


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  np.array:





    """simple docstring"""

    _UpperCAmelCase          =   int(np.ceil((x_end - xa) / step_size      )      )
    _UpperCAmelCase          =   np.zeros((n + 1,)      )
    _UpperCAmelCase          =   ya
    _UpperCAmelCase          =   xa

    for k in range(_A      ):
        _UpperCAmelCase          =   y[k] + step_size * ode_func(_A					,      y[k]      )
        _UpperCAmelCase          =   y[k] + (
            (step_size / 2) * (ode_func(_A					,      y[k]      ) + ode_func(x + step_size					,      _A      ))
        )
        x += step_size

    return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()