






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


UpperCAmelCase_ :						Union[str, Any]      =      logging.get_logger(__name__)


UpperCAmelCase_ :						Dict      =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_ :						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''',
    },
}

UpperCAmelCase_ :						Any      =      {
    '''allenai/led-base-16384''': 1_63_84,
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     int     	=  VOCAB_FILES_NAMES
	snake_case__							:     Optional[Any]     	=  PRETRAINED_VOCAB_FILES_MAP
	snake_case__							:     Optional[int]     	=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__							:     int     	=  LEDTokenizer
	snake_case__							:     str     	=  ["""input_ids""", """attention_mask"""]





	def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Dict=None ,  __lowerCamelCase				:					Any=None ,  __lowerCamelCase				:					int=None ,  __lowerCamelCase				:					Any="replace" ,  __lowerCamelCase				:					Union[str, Any]="<s>" ,  __lowerCamelCase				:					str="</s>" ,  __lowerCamelCase				:					Tuple="</s>" ,  __lowerCamelCase				:					List[Any]="<s>" ,  __lowerCamelCase				:					Optional[int]="<unk>" ,  __lowerCamelCase				:					Any="<pad>" ,  __lowerCamelCase				:					Tuple="<mask>" ,  __lowerCamelCase				:					List[str]=False ,  __lowerCamelCase				:					List[Any]=True ,  **__lowerCamelCase				:					str ,  ):
		super().__init__(
		    __lowerCamelCase ,  __lowerCamelCase ,  tokenizer_file=__lowerCamelCase ,  errors=__lowerCamelCase ,  bos_token=__lowerCamelCase ,  eos_token=__lowerCamelCase ,  sep_token=__lowerCamelCase ,  cls_token=__lowerCamelCase ,  unk_token=__lowerCamelCase ,  pad_token=__lowerCamelCase ,  mask_token=__lowerCamelCase ,  add_prefix_space=__lowerCamelCase ,  trim_offsets=__lowerCamelCase ,  **__lowerCamelCase ,  )

		UpperCamelCase     :Dict          =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
		if pre_tok_state.get("""add_prefix_space""" ,  __lowerCamelCase	) != add_prefix_space:
			UpperCamelCase     :Union[str, Any]          =	getattr(__lowerCamelCase ,  pre_tok_state.pop("""type"""	)	)
			UpperCamelCase     :Optional[Any]          =	add_prefix_space
			UpperCamelCase     :Tuple          =	pre_tok_class(**__lowerCamelCase	)

		UpperCamelCase     :Optional[Any]          =	add_prefix_space

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

			UpperCamelCase     :Optional[Any]          =	False

			if state.get("""add_prefix_space""" ,  __lowerCamelCase	) != add_prefix_space:
				UpperCamelCase     :Optional[Any]          =	add_prefix_space
				UpperCamelCase     :Union[str, Any]          =	True

			if state.get("""trim_offsets""" ,  __lowerCamelCase	) != trim_offsets:
				UpperCamelCase     :Tuple          =	trim_offsets
				UpperCamelCase     :Tuple          =	True

			if changes_to_apply:
				UpperCamelCase     :Tuple          =	getattr(__lowerCamelCase ,  state.pop("""type"""	)	)
				UpperCamelCase     :int          =	component_class(**__lowerCamelCase	)
				setattr(self.backend_tokenizer ,  __lowerCamelCase ,  __lowerCamelCase	)





	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def 						_A (		self				:					Dict	):
		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 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :Dict          =	AddedToken(__lowerCamelCase ,  lstrip=__lowerCamelCase ,  rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase ,  __lowerCamelCase	) else value
		UpperCamelCase     :List[Any]          =	value





	def 						_A (		self				:					Union[str, Any] ,  *__lowerCamelCase				:					List[Any] ,  **__lowerCamelCase				:					Optional[int]	):
		UpperCamelCase     :Optional[int]          =	kwargs.get("""is_split_into_words""" ,  __lowerCamelCase	)

		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(*__lowerCamelCase ,  **__lowerCamelCase	)





	def 						_A (		self				:					Tuple ,  *__lowerCamelCase				:					Union[str, Any] ,  **__lowerCamelCase				:					str	):
		UpperCamelCase     :Tuple          =	kwargs.get("""is_split_into_words""" ,  __lowerCamelCase	)

		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(*__lowerCamelCase ,  **__lowerCamelCase	)





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[str] = None	):
		UpperCamelCase     :List[str]          =	self._tokenizer.model.save(__lowerCamelCase ,  name=__lowerCamelCase	)
		return tuple(__lowerCamelCase	)





	def 						_A (		self				:					int ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Optional[Any]=None	):
		UpperCamelCase     :List[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 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					List[int] ,  __lowerCamelCase				:					Optional[List[int]] = None	):
		UpperCamelCase     :Dict          =	[self.sep_token_id]
		UpperCamelCase     :List[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 						_A (		self				:					Tuple ,  __lowerCamelCase				:					Union[Dict[str, EncodedInput], BatchEncoding] ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,  __lowerCamelCase				:					Optional[int] = None ,  __lowerCamelCase				:					Optional[bool] = None ,  ):
		UpperCamelCase     :List[Any]          =	super()._pad(
		    encoded_inputs=__lowerCamelCase ,  max_length=__lowerCamelCase ,  padding_strategy=__lowerCamelCase ,  pad_to_multiple_of=__lowerCamelCase ,  return_attention_mask=__lowerCamelCase ,  )

		# Load from model defaults
		if return_attention_mask is None:
			UpperCamelCase     :List[Any]          =	"""attention_mask""" in self.model_input_names

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

			if needs_to_be_padded:
				UpperCamelCase     :Optional[int]          =	len(__lowerCamelCase	) - 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     :Optional[Any]          =	(
					    encoded_inputs["""global_attention_mask"""] + [-1] * difference
					)
				elif self.padding_side == "left":
					UpperCamelCase     :Union[str, Any]          =	[-1] * difference + encoded_inputs[
					    """global_attention_mask"""
					]
				else:
					raise ValueError("""Invalid padding strategy:""" + str(self.padding_side	)	)

		return encoded_inputs








from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowercase  							=					{"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}


if TYPE_CHECKING:
	from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





