




"""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


lowerCamelCase_       			=   logging.get_logger(__name__)


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

lowerCamelCase_       			=   {
    '''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_       			=   {
    '''allenai/led-base-16384''': 1_6384,
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       VOCAB_FILES_NAMES
	__magic_name__			   =       PRETRAINED_VOCAB_FILES_MAP
	__magic_name__			   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__			   =       LEDTokenizer
	__magic_name__			   =       ['''input_ids''', '''attention_mask''']





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

				UpperCAmelCase_  :				str      =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space"			,			__A		) != add_prefix_space:
							UpperCAmelCase_  :				Dict      =      getattr(__A			,			pre_tok_state.pop("type"		)		)
							UpperCAmelCase_  :				str      =      add_prefix_space
							UpperCAmelCase_  :				Tuple      =      pre_tok_class(**__A		)

				UpperCAmelCase_  :				Optional[int]      =      add_prefix_space

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

							UpperCAmelCase_  :				Union[str, Any]      =      False

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

							if state.get("trim_offsets"			,			__A		) != trim_offsets:
										UpperCAmelCase_  :				Dict      =      trim_offsets
										UpperCAmelCase_  :				List[Any]      =      True

							if changes_to_apply:
										UpperCAmelCase_  :				Tuple      =      getattr(__A			,			state.pop("type"		)		)
										UpperCAmelCase_  :				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   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				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      :				List[Any]			,			lowerCAmelCase_      :				Optional[int]		)		->    str:
				UpperCAmelCase_  :				List[str]      =      AddedToken(__A			,			lstrip=__A			,			rstrip=__A		) if isinstance(__A			,			__A		) else value
				UpperCAmelCase_  :				Any      =      value





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			*lowerCAmelCase_      :				str			,			**lowerCAmelCase_      :				str		)		->    BatchEncoding:
				UpperCAmelCase_  :				int      =      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   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			*lowerCAmelCase_      :				int			,			**lowerCAmelCase_      :				int		)		->    BatchEncoding:
				UpperCAmelCase_  :				int      =      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   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[str] = None		)		->    Tuple[str]:
				UpperCAmelCase_  :				List[str]      =      self._tokenizer.model.save(__A			,			name=__A		)
				return tuple(__A		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Union[str, Any]=None		)		->    int:
				UpperCAmelCase_  :				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      :				int			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None		)		->    List[int]:
				UpperCAmelCase_  :				Union[str, Any]      =      [self.sep_token_id]
				UpperCAmelCase_  :				str      =      [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      :				List[str]			,			lowerCAmelCase_      :				Union[Dict[str, EncodedInput], BatchEncoding]			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				PaddingStrategy = PaddingStrategy.DO_NOT_PAD			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				Optional[bool] = None			,			)		->    dict:
				UpperCAmelCase_  :				Union[str, Any]      =      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:
							UpperCAmelCase_  :				Dict      =      "attention_mask" in self.model_input_names

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

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

				return encoded_inputs











"""simple docstring"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class     UpperCamelCase_ :
	__magic_name__			   =       None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[Any]:
				UpperCAmelCase_  :				Tuple      =      self.feature_extraction_class(**self.feat_extract_dict		)
				UpperCAmelCase_  :				Union[str, Any]      =      json.loads(feat_extract.to_json_string()		)
				for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				Dict      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				List[Any]      =      os.path.join(lowerCAmelCase_			,			"feat_extract.json"		)
							feat_extract_first.to_json_file(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_json_file(lowerCAmelCase_		)

				self.assertEqual(feat_extract_second.to_dict()			,			feat_extract_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Dict:
				UpperCAmelCase_  :				Optional[Any]      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				Optional[Any]      =      feat_extract_first.save_pretrained(lowerCAmelCase_		)[0]
							check_json_file_has_correct_format(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_pretrained(lowerCAmelCase_		)

				self.assertEqual(feat_extract_second.to_dict()			,			feat_extract_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.feature_extraction_class()
				self.assertIsNotNone(lowerCAmelCase_		)



