





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       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      VOCAB_FILES_NAMES
      UpperCAmelCase__						=      PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__						=      LEDTokenizer
      UpperCAmelCase__						=      ["""input_ids""", """attention_mask"""]


      def __init__(		self      ,							a__=None      ,							a__=None      ,							a__=None      ,							a__="replace"      ,							a__="<s>"      ,							a__="</s>"      ,							a__="</s>"      ,							a__="<s>"      ,							a__="<unk>"      ,							a__="<pad>"      ,							a__="<mask>"      ,							a__=False      ,							a__=True      ,							**a__      ,							):



             """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__      ,							)

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

             _lowerCamelCase							:     Any	=		add_prefix_space

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

                    _lowerCamelCase							:     Optional[int]	=		False

                    if state.get('''add_prefix_space'''      ,							a__) != add_prefix_space:
                           _lowerCamelCase							:     str	=		add_prefix_space
                           _lowerCamelCase							:     Tuple	=		True

                    if state.get('''trim_offsets'''      ,							a__) != trim_offsets:
                           _lowerCamelCase							:     List[str]	=		trim_offsets
                           _lowerCamelCase							:     Optional[Any]	=		True

                    if changes_to_apply:
                           _lowerCamelCase							:     Dict	=		getattr(a__      ,							state.pop('''type'''))
                           _lowerCamelCase							:     Optional[int]	=		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):



             """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      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		AddedToken(a__      ,							lstrip=a__      ,							rstrip=a__) if isinstance(a__      ,							a__) else value
             _lowerCamelCase							:     Any	=		value


      def 				__snake_case			(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             _lowerCamelCase							:     str	=		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      ,							*a__      ,							**a__):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		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      ,							a__      ,							a__ = None):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		self._tokenizer.model.save(a__      ,							name=a__)
             return tuple(a__)


      def 				__snake_case			(		self      ,							a__      ,							a__=None):



             """simple docstring"""





             _lowerCamelCase							:     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 				__snake_case			(		self      ,							a__      ,							a__ = None):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		[self.sep_token_id]
             _lowerCamelCase							:     List[str]	=		[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      ,							a__      ,							a__ = None      ,							a__ = PaddingStrategy.DO_NOT_PAD      ,							a__ = None      ,							a__ = None      ,							):



             """simple docstring"""





             _lowerCamelCase							:     int	=		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:
                    _lowerCamelCase							:     Optional[int]	=		'''attention_mask''' in self.model_input_names

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

                    if needs_to_be_padded:
                           _lowerCamelCase							:     Union[str, Any]	=		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`
                                  _lowerCamelCase							:     Any	=		(
                                      encoded_inputs['''global_attention_mask'''] + [-1] * difference
                                  )
                           elif self.padding_side == "left":
                                  _lowerCamelCase							:     Dict	=		[-1] * difference + encoded_inputs[
                                      '''global_attention_mask'''
                                  ]
                           else:
                                  raise ValueError('''Invalid padding strategy:''' + str(self.padding_side))

             return encoded_inputs








import unittest

from knapsack import knapsack as k







class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		0
             _lowerCamelCase							:     int	=		[0]
             _lowerCamelCase							:     Optional[int]	=		[0]
             _lowerCamelCase							:     Dict	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)

             _lowerCamelCase							:     str	=		[60]
             _lowerCamelCase							:     List[str]	=		[10]
             _lowerCamelCase							:     List[Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		3
             _lowerCamelCase							:     List[Any]	=		[1, 2, 3]
             _lowerCamelCase							:     Dict	=		[3, 2, 1]
             _lowerCamelCase							:     Any	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							5)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		50
             _lowerCamelCase							:     Dict	=		[60, 100, 120]
             _lowerCamelCase							:     Dict	=		[10, 20, 30]
             _lowerCamelCase							:     Union[str, Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							220)


if __name__ == "__main__":
       unittest.main()

