
"""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


lowercase  : List[Any]						   =							logging.get_logger(__name__)


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

lowercase  : Dict						   =							{
    """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  : Any						   =							{
    """allenai/led-base-16384""": 16384,
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Any=    VOCAB_FILES_NAMES
    _a		:						str=    PRETRAINED_VOCAB_FILES_MAP
    _a		:						Union[str, Any]=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						Optional[int]=    LEDTokenizer
    _a		:						Union[str, Any]=    ["input_ids", "attention_mask"]
    def __init__(  self		,snake_case=None		,snake_case=None		,snake_case=None		,snake_case="replace"		,snake_case="<s>"		,snake_case="</s>"		,snake_case="</s>"		,snake_case="<s>"		,snake_case="<unk>"		,snake_case="<pad>"		,snake_case="<mask>"		,snake_case=False		,snake_case=True		,**snake_case		,):



           '''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		,)

           lowercase							:					List[Any]	     =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
           if pre_tok_state.get("""add_prefix_space"""		,snake_case				) != add_prefix_space:
                  lowercase							:					Dict	     =				getattr(snake_case		,pre_tok_state.pop("""type"""				)				)
                  lowercase							:					Tuple	     =				add_prefix_space
                  lowercase							:					Tuple	     =				pre_tok_class(**snake_case				)

           lowercase							:					Tuple	     =				add_prefix_space

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

                  lowercase							:					List[Any]	     =				False

                  if state.get("""add_prefix_space"""		,snake_case				) != add_prefix_space:
                         lowercase							:					Any	     =				add_prefix_space
                         lowercase							:					Tuple	     =				True

                  if state.get("""trim_offsets"""		,snake_case				) != trim_offsets:
                         lowercase							:					Union[str, Any]	     =				trim_offsets
                         lowercase							:					List[Any]	     =				True

                  if changes_to_apply:
                         lowercase							:					Union[str, Any]	     =				getattr(snake_case		,state.pop("""type"""				)				)
                         lowercase							:					str	     =				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 				_SCREAMING_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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else value
           lowercase							:					List[str]	     =				value
    def 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				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 				_SCREAMING_SNAKE_CASE	(  self		,*snake_case		,**snake_case				):



           '''simple docstring'''







           lowercase							:					Union[str, 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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self._tokenizer.model.save(snake_case		,name=snake_case				)
           return tuple(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case=None				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				[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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				[self.sep_token_id]
           lowercase							:					Dict	     =				[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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = PaddingStrategy.DO_NOT_PAD		,snake_case = None		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:					Any	     =				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:
                  lowercase							:					Optional[int]	     =				"""attention_mask""" in self.model_input_names

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

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

           return encoded_inputs


from __future__ import annotations

import numpy as np







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   tuple[np.ndarray, np.ndarray]:
       lowercase     ,      lowercase							:					Dict	     =				np.shape(SCREAMING_SNAKE_CASE__    )
       if rows != columns:
              lowercase							:					str	     =				(
                  """'table' has to be of square shaped array but got a """
                  f"{rows}x{columns} array:\n{table}"
              )
              raise ValueError(SCREAMING_SNAKE_CASE__    )

       lowercase							:					Any	     =				np.zeros((rows, columns)    )
       lowercase							:					int	     =				np.zeros((rows, columns)    )
       for i in range(SCREAMING_SNAKE_CASE__    ):
              for j in range(SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Optional[int]	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     if upper[j][j] == 0:
                            raise ArithmeticError("""No LU decomposition exists"""    )
                     lowercase							:					str	     =				(table[i][j] - total) / upper[j][j]
              lowercase							:					Optional[Any]	     =				1
              for j in range(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ):
                     lowercase							:					Any	     =				sum(lower[i][k] * upper[k][j] for k in range(SCREAMING_SNAKE_CASE__    )    )
                     lowercase							:					Tuple	     =				table[i][j] - total
       return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
