






from ..utils import DummyObject, requires_backends



class 							SCREAMING_SNAKE_CASE__						(    metaclass=UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["flax", "transformers"]



					def __init__( self					:  List[Any]							,		*__A					:  str							,		**__A					:  Optional[int]	):
						requires_backends(self							,		["flax", "transformers"]	)



					@classmethod
					def 	_lowercase					( cls					:  List[Any]							,		*__A					:  List[Any]							,		**__A					:  Optional[int]	):
						requires_backends(cls							,		["flax", "transformers"]	)






					@classmethod
					def 	_lowercase					( cls					:  List[Any]							,		*__A					:  int							,		**__A					:  str	):
						requires_backends(cls							,		["flax", "transformers"]	)



class 							SCREAMING_SNAKE_CASE__						(    metaclass=UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["flax", "transformers"]



					def __init__( self					:  List[Any]							,		*__A					:  Optional[int]							,		**__A					:  Dict	):
						requires_backends(self							,		["flax", "transformers"]	)



					@classmethod
					def 	_lowercase					( cls					:  str							,		*__A					:  Union[str, Any]							,		**__A					:  Optional[int]	):
						requires_backends(cls							,		["flax", "transformers"]	)






					@classmethod
					def 	_lowercase					( cls					:  int							,		*__A					:  Tuple							,		**__A					:  List[str]	):
						requires_backends(cls							,		["flax", "transformers"]	)



class 							SCREAMING_SNAKE_CASE__						(    metaclass=UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["flax", "transformers"]



					def __init__( self					:  int							,		*__A					:  Any							,		**__A					:  List[Any]	):
						requires_backends(self							,		["flax", "transformers"]	)



					@classmethod
					def 	_lowercase					( cls					:  Optional[int]							,		*__A					:  str							,		**__A					:  List[Any]	):
						requires_backends(cls							,		["flax", "transformers"]	)






					@classmethod
					def 	_lowercase					( cls					:  List[str]							,		*__A					:  Any							,		**__A					:  int	):
						requires_backends(cls							,		["flax", "transformers"]	)



class 							SCREAMING_SNAKE_CASE__						(    metaclass=UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["flax", "transformers"]



					def __init__( self					:  List[str]							,		*__A					:  str							,		**__A					:  Any	):
						requires_backends(self							,		["flax", "transformers"]	)



					@classmethod
					def 	_lowercase					( cls					:  int							,		*__A					:  Dict							,		**__A					:  str	):
						requires_backends(cls							,		["flax", "transformers"]	)






					@classmethod
					def 	_lowercase					( cls					:  Dict							,		*__A					:  Union[str, Any]							,		**__A					:  Any	):
						requires_backends(cls							,		["flax", "transformers"]	)











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 :      int										=				logging.get_logger(__name__)


__lowerCamelCase :      List[str]										=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase :      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""",
    },
}

__lowerCamelCase :      Optional[int]										=				{
    """allenai/led-base-16384""": 1_6384,
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






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



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

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

						snake_case__			:				List[Any]       =  add_prefix_space

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

							snake_case__			:				Union[str, Any]       =  False

							if state.get("add_prefix_space"							,		__A	) != add_prefix_space:
								snake_case__			:				Optional[Any]       =  add_prefix_space
								snake_case__			:				List[str]       =  True

							if state.get("trim_offsets"							,		__A	) != trim_offsets:
								snake_case__			:				str       =  trim_offsets
								snake_case__			:				Tuple       =  True

							if changes_to_apply:
								snake_case__			:				Any       =  getattr(__A							,		state.pop("type"	)	)
								snake_case__			:				Optional[Any]       =  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 	_lowercase					( self					:  Union[str, Any]	):
						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					:  str							,		__A					:  int	):
						snake_case__			:				str       =  AddedToken(__A							,		lstrip=__A							,		rstrip=__A	) if isinstance(__A							,		__A	) else value
						snake_case__			:				Optional[int]       =  value



					def 	_lowercase					( self					:  List[str]							,		*__A					:  Union[str, Any]							,		**__A					:  int	):
						snake_case__			:				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()._batch_encode_plus(*__A							,		**__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		*__A					:  Tuple							,		**__A					:  Union[str, Any]	):
						snake_case__			:				List[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()._encode_plus(*__A							,		**__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  str							,		__A					:  Optional[str] = None	):
						snake_case__			:				str       =  self._tokenizer.model.save(__A							,		name=__A	)
						return tuple(__A	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  int							,		__A					:  str=None	):
						snake_case__			:				int       =  [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					:  Any							,		__A					:  List[int]							,		__A					:  Optional[List[int]] = None	):
						snake_case__			:				Optional[Any]       =  [self.sep_token_id]
						snake_case__			:				Any       =  [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					:  str							,		__A					:  Union[Dict[str, EncodedInput], BatchEncoding]							,		__A					:  Optional[int] = None							,		__A					:  PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		):
						snake_case__			:				Dict       =  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:
							snake_case__			:				Optional[Any]       =  "attention_mask" in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
							snake_case__			:				List[Any]       =  encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							snake_case__			:				Union[str, Any]       =  len(encoded_inputs["global_attention_mask"]	) != len(__A	)

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

						return encoded_inputs



