



"""simple docstring"""
import os
import re
import warnings
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_ta import TaTokenizer
else:
       UpperCAmelCase						:		int			= None


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

UpperCAmelCase						:		Any			= {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase						:		Tuple			= {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase						:		Dict			= {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   VOCAB_FILES_NAMES
    lowercase__      =   PRETRAINED_VOCAB_FILES_MAP
    lowercase__      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__      =   ["input_ids", "attention_mask"]
    lowercase__      =   TaTokenizer

    lowercase__      =   []

    def __init__(				self			:				str ,  lowerCAmelCase_			:				Any=None ,  lowerCAmelCase_			:				List[Any]=None ,  lowerCAmelCase_			:				str="</s>" ,  lowerCAmelCase_			:				List[str]="<unk>" ,  lowerCAmelCase_			:				int="<pad>" ,  lowerCAmelCase_			:				Optional[int]=1_0_0 ,  lowerCAmelCase_			:				Any=None ,  **lowerCAmelCase_			:				Any ,  ):







      """simple docstring"""







      if extra_ids > 0 and additional_special_tokens is None:
        lowercase_								= [F'''<extra_id_{i}>''' for i in range(lowerCAmelCase_)]
      elif extra_ids > 0 and additional_special_tokens is not None:
        # Check that we have the right number of extra special tokens
        lowercase_								= len(set(filter(lambda lowerCAmelCase_: bool("""extra_id_""" in str(lowerCAmelCase_)) ,  lowerCAmelCase_)))
        if extra_tokens != extra_ids:
          raise ValueError(
              F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
              """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
              """ tokens""")

      super().__init__(
          lowerCAmelCase_ ,  tokenizer_file=lowerCAmelCase_ ,  eos_token=lowerCAmelCase_ ,  unk_token=lowerCAmelCase_ ,  pad_token=lowerCAmelCase_ ,  extra_ids=lowerCAmelCase_ ,  additional_special_tokens=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )

      lowercase_								= vocab_file
      lowercase_								= False if not self.vocab_file else True
      lowercase_								= extra_ids

    @staticmethod
    def 					_UpperCAmelCase	(				lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[str]):







      """simple docstring"""







      if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
        lowercase_								= TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
        if init_max_model_length is not None and init_max_model_length != max_model_length:
          return init_max_model_length
        elif init_max_model_length is None:
          warnings.warn(
              """This tokenizer was incorrectly instantiated with a model max length of"""
              F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
              """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
              """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
              F''' {pretrained_model_name_or_path} automatically truncating your input to'''
              F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
              F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
              """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
              """ instantiate this tokenizer with `model_max_length` set to your preferred value.""" ,  lowerCAmelCase_ ,  )

      return max_model_length

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Optional[str] = None):







      """simple docstring"""







      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
      lowercase_								= 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_)
        logger.info(F'''Copy vocab file to {out_vocab_file}''')

      return (out_vocab_file,)

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				Optional[List[int]] = None):







      """simple docstring"""







      lowercase_								= token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a
      else:
        lowercase_								= token_ids_a + [self.eos_token_id]
        return self.prefix_tokens + token_ids_a + token_ids_a

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				List[int] ,  lowerCAmelCase_			:				Optional[List[int]] = None):







      """simple docstring"""







      lowercase_								= [self.eos_token_id]

      if token_ids_a is None:
        return len(token_ids_a + eos) * [0]
      return len(token_ids_a + eos + token_ids_a + eos) * [0]

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







      return list(
          set(filter(lambda lowerCAmelCase_: bool(re.search(r"""<extra_id_\d+>""" ,  lowerCAmelCase_)) is not None ,  self.additional_special_tokens)))




    def 					_UpperCAmelCase	(				self			:				Tuple):







      """simple docstring"""







      return [self.convert_tokens_to_ids(lowerCAmelCase_) for token in self.get_sentinel_tokens()]






"""simple docstring"""
from typing import TYPE_CHECKING

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


UpperCAmelCase						:		Union[str, Any]			= {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= ["LongformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LongformerForMaskedLM",
           "LongformerForMultipleChoice",
           "LongformerForQuestionAnswering",
           "LongformerForSequenceClassification",
           "LongformerForTokenClassification",
           "LongformerModel",
           "LongformerPreTrainedModel",
           "LongformerSelfAttention",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= [
           "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFLongformerForMaskedLM",
           "TFLongformerForMultipleChoice",
           "TFLongformerForQuestionAnswering",
           "TFLongformerForSequenceClassification",
           "TFLongformerForTokenClassification",
           "TFLongformerModel",
           "TFLongformerPreTrainedModel",
           "TFLongformerSelfAttention",
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

