




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													=			logging.get_logger(__name__)


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

a													=			{
    "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													=			{
    "allenai/led-base-16384": 16384,
}




class       _A				(		__lowercase     ):
		__a					    =       VOCAB_FILES_NAMES
		__a					    =       PRETRAINED_VOCAB_FILES_MAP
		__a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       LEDTokenizer
		__a					    =       ["""input_ids""", """attention_mask"""]






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE="replace"						,		_SCREAMING_SNAKE_CASE="<s>"						,		_SCREAMING_SNAKE_CASE="</s>"						,		_SCREAMING_SNAKE_CASE="</s>"						,		_SCREAMING_SNAKE_CASE="<s>"						,		_SCREAMING_SNAKE_CASE="<unk>"						,		_SCREAMING_SNAKE_CASE="<pad>"						,		_SCREAMING_SNAKE_CASE="<mask>"						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=True						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		tokenizer_file=_SCREAMING_SNAKE_CASE						,		errors=_SCREAMING_SNAKE_CASE						,		bos_token=_SCREAMING_SNAKE_CASE						,		eos_token=_SCREAMING_SNAKE_CASE						,		sep_token=_SCREAMING_SNAKE_CASE						,		cls_token=_SCREAMING_SNAKE_CASE						,		unk_token=_SCREAMING_SNAKE_CASE						,		pad_token=_SCREAMING_SNAKE_CASE						,		mask_token=_SCREAMING_SNAKE_CASE						,		add_prefix_space=_SCREAMING_SNAKE_CASE						,		trim_offsets=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)

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

								_UpperCAmelCase      =			add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								_UpperCAmelCase      =			"""post_processor"""
								_UpperCAmelCase      =			getattr(self.backend_tokenizer						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								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"""						,		_SCREAMING_SNAKE_CASE			) != add_prefix_space:
																				_UpperCAmelCase      =			add_prefix_space
																				_UpperCAmelCase      =			True

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

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






		@property
		# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
		def 				UpperCAmelCase (    self			):
								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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			AddedToken(_SCREAMING_SNAKE_CASE						,		lstrip=_SCREAMING_SNAKE_CASE						,		rstrip=_SCREAMING_SNAKE_CASE			) if isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			) else value
								_UpperCAmelCase      =			value






		def 				UpperCAmelCase (    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			kwargs.get("""is_split_into_words"""						,		_SCREAMING_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(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			kwargs.get("""is_split_into_words"""						,		_SCREAMING_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(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,		name=_SCREAMING_SNAKE_CASE			)
								return tuple(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		):
								_UpperCAmelCase      =			super()._pad(
								    encoded_inputs=_SCREAMING_SNAKE_CASE						,		max_length=_SCREAMING_SNAKE_CASE						,		padding_strategy=_SCREAMING_SNAKE_CASE						,		pad_to_multiple_of=_SCREAMING_SNAKE_CASE						,		return_attention_mask=_SCREAMING_SNAKE_CASE						,		)

								# 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(_SCREAMING_SNAKE_CASE			)

														if needs_to_be_padded:
																				_UpperCAmelCase      =			len(_SCREAMING_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`
																										_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





from typing import Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)