




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  _lowerCAmelCase						:   str		=   None


_lowerCAmelCase						:   List[Any]		=   logging.get_logger(__name__)


_lowerCAmelCase						:   Union[str, Any]		=   "▁"

_lowerCAmelCase						:   Optional[Any]		=   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase						:   Union[str, Any]		=   {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

_lowerCAmelCase						:   int		=   {
    "google/pegasus-xsum": 5_1_2,
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_        =		PegasusTokenizer
      UpperCAmelCase_        =		["""input_ids""", """attention_mask"""]
      def __init__(			self   :int ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :Dict="<pad>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :List[str]="<unk>" ,      lowerCamelCase   :List[str]="<mask_2>" ,      lowerCamelCase   :Optional[int]="<mask_1>" ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :Any=103 ,      **lowerCamelCase   :Optional[int] ,      )     ->			Dict:
       UpperCAmelCase__							 =						offset

       if additional_special_tokens is not None:
        if not isinstance(lowerCamelCase ,      lowerCamelCase      ):
         raise TypeError(
             f'''additional_special_tokens should be of type {type(lowerCamelCase      )}, but is'''
             f''' {type(lowerCamelCase      )}'''      )

        UpperCAmelCase__							 =						(
            ([mask_token_sent] + additional_special_tokens)
            if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
            else additional_special_tokens
        )
        # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
        additional_special_tokens_extended += [
            f'''<unk_{i}>''' for i in range(len(lowerCamelCase      ) ,      self.offset - 1      )
        ]

        if len(set(lowerCamelCase      )      ) != len(lowerCamelCase      ):
         raise ValueError(
             "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
             f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''      )
        UpperCAmelCase__							 =						additional_special_tokens_extended
       else:
        UpperCAmelCase__							 =						[mask_token_sent] if mask_token_sent is not None else []
        additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 ,      self.offset      )]

       super().__init__(
           lowerCamelCase ,      tokenizer_file=lowerCamelCase ,      pad_token=lowerCamelCase ,      eos_token=lowerCamelCase ,      unk_token=lowerCamelCase ,      mask_token=lowerCamelCase ,      mask_token_sent=lowerCamelCase ,      offset=lowerCamelCase ,      additional_special_tokens=lowerCamelCase ,      **lowerCamelCase ,      )
       UpperCAmelCase__							 =						vocab_file
       UpperCAmelCase__							 =						False if not self.vocab_file else True
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :int      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						set(self.all_special_ids      )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

       if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
        raise ValueError(
            "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
            f''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

       return [1 if x in all_special_ids else 0 for x in seq]
      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :List ,      lowerCamelCase   :Optional[List] = None ,      lowerCamelCase   :bool = False      )     ->			List[int]:
       if already_has_special_tokens:
        return self._special_token_mask(lowerCamelCase      )
       elif token_ids_a is None:
        return self._special_token_mask(lowerCamelCase      ) + [1]
       else:
        return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Tuple=None      )     ->			List[int]:
       if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]







      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:
       if not self.can_save_slow_tokenizer:
        raise ValueError(
            "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
            "tokenizer."      )

       if not os.path.isdir(lowerCamelCase      ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
        return
       UpperCAmelCase__							 =						os.path.join(
           lowerCamelCase ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowerCamelCase      ):
        copyfile(self.vocab_file ,      lowerCamelCase      )

       return (out_vocab_file,)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase						:   Union[str, Any]		=   {
    "configuration_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig", "BertOnnxConfig"],
    "tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   str		=   ["BertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BertForMaskedLM",
      "BertForMultipleChoice",
      "BertForNextSentencePrediction",
      "BertForPreTraining",
      "BertForQuestionAnswering",
      "BertForSequenceClassification",
      "BertForTokenClassification",
      "BertLayer",
      "BertLMHeadModel",
      "BertModel",
      "BertPreTrainedModel",
      "load_tf_weights_in_bert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Dict		=   [
      "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFBertEmbeddings",
      "TFBertForMaskedLM",
      "TFBertForMultipleChoice",
      "TFBertForNextSentencePrediction",
      "TFBertForPreTraining",
      "TFBertForQuestionAnswering",
      "TFBertForSequenceClassification",
      "TFBertForTokenClassification",
      "TFBertLMHeadModel",
      "TFBertMainLayer",
      "TFBertModel",
      "TFBertPreTrainedModel",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   str		=   ["TFBertTokenizer"]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[Any]		=   [
      "FlaxBertForCausalLM",
      "FlaxBertForMaskedLM",
      "FlaxBertForMultipleChoice",
      "FlaxBertForNextSentencePrediction",
      "FlaxBertForPreTraining",
      "FlaxBertForQuestionAnswering",
      "FlaxBertForSequenceClassification",
      "FlaxBertForTokenClassification",
      "FlaxBertModel",
      "FlaxBertPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





