
'''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          =						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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  VOCAB_FILES_NAMES
						_lowercase						: int										=  PRETRAINED_VOCAB_FILES_MAP
						_lowercase						: int										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase						: str										=  LEDTokenizer
						_lowercase						: Optional[Any]										=  ['''input_ids''', '''attention_mask''']




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

												"""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       ,  )

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

												_lowerCAmelCase    		=					add_prefix_space

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

																		_lowerCAmelCase    		=					False

																		if state.get("""add_prefix_space"""       ,  _lowercase ) != add_prefix_space:
																								_lowerCAmelCase    		=					add_prefix_space
																								_lowerCAmelCase    		=					True

																		if state.get("""trim_offsets"""       ,  _lowercase ) != trim_offsets:
																								_lowerCAmelCase    		=					trim_offsets
																								_lowerCAmelCase    		=					True

																		if changes_to_apply:
																								_lowerCAmelCase    		=					getattr(_lowercase       ,  state.pop("""type""" ) )
																								_lowerCAmelCase    		=					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    _lowercase      (   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    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					AddedToken(_lowercase       ,  lstrip=_lowercase       ,  rstrip=_lowercase ) if isinstance(_lowercase       ,  _lowercase ) else value
												_lowerCAmelCase    		=					value




						def    _lowercase      (   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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    _lowercase      (   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self._tokenizer.model.save(_lowercase       ,  name=_lowercase )
												return tuple(_lowercase )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=None ):

												"""simple docstring"""



												_lowerCAmelCase    		=					[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    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None ):

												"""simple docstring"""



												_lowerCAmelCase    		=					[self.sep_token_id]
												_lowerCAmelCase    		=					[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    _lowercase      (   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = PaddingStrategy.DO_NOT_PAD       ,  _lowercase = None       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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:
																		_lowerCAmelCase    		=					"""attention_mask""" in self.model_input_names

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

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

												return encoded_inputs


'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												warnings.warn(
												    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use DonutImageProcessor instead."""       ,  _lowercase       ,  )
												super().__init__(*_lowercase       ,  **_lowercase )
