


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": 1_6384,
}


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  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		,						_lowercase      :					Dict=None		,						_lowercase      :					List[Any]=None		,						_lowercase      :					str=None		,						_lowercase      :					Optional[Any]="replace"		,						_lowercase      :					Any="<s>"		,						_lowercase      :					Optional[int]="</s>"		,						_lowercase      :					Dict="</s>"		,						_lowercase      :					str="<s>"		,						_lowercase      :					str="<unk>"		,						_lowercase      :					int="<pad>"		,						_lowercase      :					List[str]="<mask>"		,						_lowercase      :					Optional[int]=False		,						_lowercase      :					Union[str, Any]=True		,						**_lowercase      :					Union[str, Any]		,						):




         """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		,						)

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

         UpperCAmelCase__										=     add_prefix_space

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

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

                if changes_to_apply:
                       UpperCAmelCase__										=     getattr(_lowercase		,						state.pop("type" ) )
                       UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Dict ):




         """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 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					Dict ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					int		,						*_lowercase      :					Dict		,						**_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						*_lowercase      :					Optional[int]		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[str] = None ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					List[str]		,						_lowercase      :					Tuple		,						_lowercase      :					Union[str, Any]=None ):




         """simple docstring"""







         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      :					Optional[Any]		,						_lowercase      :					List[int]		,						_lowercase      :					Optional[List[int]] = None ):




         """simple docstring"""







         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      :					Tuple		,						_lowercase      :					Union[Dict[str, EncodedInput], BatchEncoding]		,						_lowercase      :					Optional[int] = None		,						_lowercase      :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,						_lowercase      :					Optional[int] = None		,						_lowercase      :					Optional[bool] = None		,						):




         """simple docstring"""







         UpperCAmelCase__										=     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:
                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(_lowercase )

                if needs_to_be_padded:
                       UpperCAmelCase__										=     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`
                              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 .imports import is_tqdm_available


if is_tqdm_available():
    from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def __UpperCAmelCase       (		__A = True ,						*__A ,						**__A			)						->					Any:



       '''simple docstring'''





       if not is_tqdm_available():
              raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."			)
       UpperCAmelCase__										=     False
       if main_process_only:
              UpperCAmelCase__										=     PartialState().local_process_index == 0
       return _tqdm(*__A ,						**__A ,						disable=__A			)





