




import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

_lowerCAmelCase						:   List[Any]		=   {
    "bart": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "bert": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "bert-base-cased-finetuned-mrpc": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "dpr": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "gpt2": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlnet": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "xlm-roberta": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "transfo-xl": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "openai-gpt": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "roberta": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "layoutlm": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    "roberta-large-mnli": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "camembert": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "flaubert": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "distilbert-base-distilled-squad": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "lxmert-visual-feature-encoder": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "ctrl": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "albert": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "t5": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "electra": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    "wav2vec2": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   Optional[Any]=False						,							_lowerCAmelCase							:   Optional[Any]=True	):




 """simple docstring"""







 if model_type not in MODEL_CLASSES:
  raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()	)}.'''	)

 UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						MODEL_CLASSES[model_type]

 # Initialise TF model
 if config_file in aws_config_map:
  UpperCAmelCase__							 =						cached_file(_lowerCAmelCase						,							_lowerCAmelCase						,							force_download=not use_cached_models	)
 UpperCAmelCase__							 =						config_class.from_json_file(_lowerCAmelCase	)
 UpperCAmelCase__							 =						True
 UpperCAmelCase__							 =						True
 print(F'''Building TensorFlow model from configuration: {config}'''	)
 UpperCAmelCase__							 =						model_class(_lowerCAmelCase	)

 # Load weights from tf checkpoint
 if pytorch_checkpoint_path in aws_config_map.keys():
  UpperCAmelCase__							 =						cached_file(
      _lowerCAmelCase						,							_lowerCAmelCase						,							force_download=not use_cached_models	)
 # Load PyTorch checkpoint in tf2 model:
 UpperCAmelCase__							 =						load_pytorch_checkpoint_in_tfa_model(_lowerCAmelCase						,							_lowerCAmelCase	)

 if compare_with_pt_model:
  UpperCAmelCase__							 =						tf_model(tf_model.dummy_inputs						,							training=_lowerCAmelCase	)  # build the network

  UpperCAmelCase__							 =						torch.load(_lowerCAmelCase						,							map_location="cpu"	)
  UpperCAmelCase__							 =						pt_model_class.from_pretrained(
      pretrained_model_name_or_path=_lowerCAmelCase						,							config=_lowerCAmelCase						,							state_dict=_lowerCAmelCase	)

  with torch.no_grad():
   UpperCAmelCase__							 =						pt_model(**pt_model.dummy_inputs	)

  UpperCAmelCase__							 =						pto[0].numpy()
  UpperCAmelCase__							 =						tfo[0].numpy()
  UpperCAmelCase__							 =						np.amax(np.abs(np_pt - np_tf	)	)
  print(F'''Max absolute difference between models outputs {diff}'''	)
  assert diff <= 2E-2, F'''Error, model absolute difference is >2e-2: {diff}'''

 # Save pytorch-model
 print(F'''Save TensorFlow model to {tf_dump_path}'''	)
 tf_model.save_weights(_lowerCAmelCase						,							save_format="h5"	)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   Tuple=None						,							_lowerCAmelCase							:   Dict=None						,							_lowerCAmelCase							:   Optional[int]=False						,							_lowerCAmelCase							:   Tuple=False						,							_lowerCAmelCase							:   List[Any]=False						,							_lowerCAmelCase							:   Dict=False						,							):




 """simple docstring"""







 if args_model_type is None:
  UpperCAmelCase__							 =						list(MODEL_CLASSES.keys()	)
 else:
  UpperCAmelCase__							 =						[args_model_type]

 for j, model_type in enumerate(_lowerCAmelCase						,							start=1	):
  print("=" * 100	)
  print(F''' Converting model type {j}/{len(_lowerCAmelCase	)}: {model_type}'''	)
  print("=" * 100	)
  if model_type not in MODEL_CLASSES:
   raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()	)}.'''	)

  UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						MODEL_CLASSES[model_type]

  if model_shortcut_names_or_path is None:
   UpperCAmelCase__							 =						list(aws_model_maps.keys()	)
  if config_shortcut_names_or_path is None:
   UpperCAmelCase__							 =						model_shortcut_names_or_path

  for i, (model_shortcut_name, config_shortcut_name) in enumerate(
      zip(_lowerCAmelCase						,							_lowerCAmelCase	)						,							start=1	):
   print("-" * 100	)
   if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
    if not only_convert_finetuned_models:
     print(F'''    Skipping finetuned checkpoint {model_shortcut_name}'''	)
     continue
    UpperCAmelCase__							 =						model_shortcut_name
   elif only_convert_finetuned_models:
    print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''	)
    continue
   print(
       F'''    Converting checkpoint {i}/{len(_lowerCAmelCase	)}: {model_shortcut_name} - model_type {model_type}'''	)
   print("-" * 100	)

   if config_shortcut_name in aws_config_map:
    UpperCAmelCase__							 =						cached_file(_lowerCAmelCase						,							_lowerCAmelCase						,							force_download=not use_cached_models	)
   else:
    UpperCAmelCase__							 =						config_shortcut_name

   if model_shortcut_name in aws_model_maps:
    UpperCAmelCase__							 =						cached_file(_lowerCAmelCase						,							_lowerCAmelCase						,							force_download=not use_cached_models	)
   else:
    UpperCAmelCase__							 =						model_shortcut_name

   if os.path.isfile(_lowerCAmelCase	):
    UpperCAmelCase__							 =						"converted_model"

   convert_pt_checkpoint_to_tf(
       model_type=_lowerCAmelCase						,							pytorch_checkpoint_path=_lowerCAmelCase						,							config_file=_lowerCAmelCase						,							tf_dump_path=os.path.join(_lowerCAmelCase						,							model_shortcut_name + "-tf_model.h5"	)						,							compare_with_pt_model=_lowerCAmelCase						,							)
   if remove_cached_files:
    os.remove(_lowerCAmelCase	)
    os.remove(_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Any		=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
  )
  parser.add_argument(
      "--model_type",
      default=None,
      type=str,
      help=(
          F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
          "convert all the models from AWS."
      ),
  )
  parser.add_argument(
      "--pytorch_checkpoint_path",
      default=None,
      type=str,
      help=(
          "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
          "If not given, will download and convert all the checkpoints from AWS."
      ),
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      help=(
          "The config json file corresponding to the pre-trained model. \n"
          "This specifies the model architecture. If not given and "
          "--pytorch_checkpoint_path is not given or is a shortcut name "
          "use the configuration associated to the shortcut name on the AWS"
      ),
  )
  parser.add_argument(
      "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
  )
  parser.add_argument(
      "--use_cached_models",
      action="store_true",
      help="Use cached models if possible instead of updating to latest checkpoint versions.",
  )
  parser.add_argument(
      "--remove_cached_files",
      action="store_true",
      help="Remove pytorch models after conversion (save memory when converting in batches).",
  )
  parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
  _lowerCAmelCase						:   Tuple		=   parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )











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						:   Tuple		=   logging.get_logger(__name__)


_lowerCAmelCase						:   Union[str, Any]		=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase						:   str		=   {
    "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						:   List[Any]		=   {
    "allenai/led-base-16384": 1_6_3_8_4,
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_        =		LEDTokenizer
      UpperCAmelCase_        =		["""input_ids""", """attention_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :int="replace" ,      lowerCamelCase   :List[Any]="<s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :int="<s>" ,      lowerCamelCase   :Optional[Any]="<unk>" ,      lowerCamelCase   :Dict="<pad>" ,      lowerCamelCase   :Tuple="<mask>" ,      lowerCamelCase   :str=False ,      lowerCamelCase   :Union[str, Any]=True ,      **lowerCamelCase   :List[str] ,      )     ->			Tuple:
       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__							 =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
       if pre_tok_state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
        UpperCAmelCase__							 =						getattr(lowerCamelCase ,      pre_tok_state.pop("type"      )      )
        UpperCAmelCase__							 =						add_prefix_space
        UpperCAmelCase__							 =						pre_tok_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						add_prefix_space

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

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

        if changes_to_apply:
         UpperCAmelCase__							 =						getattr(lowerCamelCase ,      state.pop("type"      )      )
         UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			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 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :List[str]      )     ->			Dict:
       UpperCAmelCase__							 =						AddedToken(lowerCamelCase ,      lstrip=lowerCamelCase ,      rstrip=lowerCamelCase      ) if isinstance(lowerCamelCase ,      lowerCamelCase      ) else value
       UpperCAmelCase__							 =						value
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :int      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :int ,      *lowerCamelCase   :Optional[Any] ,      **lowerCamelCase   :Optional[int]      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:
       UpperCAmelCase__							 =						self._tokenizer.model.save(lowerCamelCase ,      name=lowerCamelCase      )
       return tuple(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[Any]=None      )     ->			Dict:
       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[int] ,      lowerCamelCase   :List[int] ,      lowerCamelCase   :Optional[List[int]] = None      )     ->			List[int]:
       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   :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__							 =						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__							 =						"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(lowerCamelCase      )

        if needs_to_be_padded:
         UpperCAmelCase__							 =						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__							 =						(
              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





