



'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__a          =		logging.get_logger(__name__)

__a          =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__a          =		{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

__a          =		{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


__a          =		{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		VOCAB_FILES_NAMES
    lowercase					=		PRETRAINED_VOCAB_FILES_MAP
    lowercase					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase					=		PRETRAINED_INIT_CONFIGURATION
    lowercase					=		RoFormerTokenizer




    def __init__(    self					:     List[Any] ,     snake_case_					:     List[str]=None ,     snake_case_					:     Dict=None ,     snake_case_					:     Any=True ,     snake_case_					:     str="[UNK]" ,     snake_case_					:     List[str]="[SEP]" ,     snake_case_					:     Optional[Any]="[PAD]" ,     snake_case_					:     Union[str, Any]="[CLS]" ,     snake_case_					:     Union[str, Any]="[MASK]" ,     snake_case_					:     List[Any]=True ,     snake_case_					:     Optional[Any]=None ,     **snake_case_					:     Tuple ,     ):
          super().__init__(
              snake_case_ ,     tokenizer_file=snake_case_ ,     do_lower_case=snake_case_ ,     unk_token=snake_case_ ,     sep_token=snake_case_ ,     pad_token=snake_case_ ,     cls_token=snake_case_ ,     mask_token=snake_case_ ,     tokenize_chinese_chars=snake_case_ ,     strip_accents=snake_case_ ,     **snake_case_ ,     )

          snake_case__  :    str    						=      json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
          if (
              pre_tok_state.get("""lowercase""" ,     snake_case_						) != do_lower_case
              or pre_tok_state.get("""strip_accents""" ,     snake_case_						) != strip_accents
          ):
                snake_case__  :    str    						=      getattr(snake_case_ ,     pre_tok_state.pop("""type"""						)						)
                snake_case__  :    Optional[int]    						=      do_lower_case
                snake_case__  :    Union[str, Any]    						=      strip_accents
                snake_case__  :    Union[str, Any]    						=      pre_tok_class(**snake_case_						)

          snake_case__  :    str    						=      do_lower_case




    def __getstate__(    self					:     int						):
          snake_case__  :    List[Any]    						=      self.__dict__.copy()
          snake_case__  :    str    						=      BertPreTokenizer()
          return state




    def __setstate__(    self					:     Dict ,     snake_case_					:     Dict						):
          snake_case__  :    List[Any]    						=      d
          snake_case__  :    Union[str, Any]    						=      self.__dict__["""_tokenizer"""].get_vocab()
          snake_case__  :    List[Any]    						=      PreTokenizer.custom(JiebaPreTokenizer(snake_case_						)						)




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Optional[Any] ,     snake_case_					:     List[str]=None						):
          snake_case__  :    str    						=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

          return output




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     List[int] ,     snake_case_					:     Optional[List[int]] = None						):
          snake_case__  :    int    						=      [self.sep_token_id]
          snake_case__  :    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						) * [0] + len(token_ids_a + sep						) * [1]




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     str ,     snake_case_					:     Optional[str] = None						):
          snake_case__  :    Union[str, Any]    						=      self._tokenizer.model.save(snake_case_ ,     name=snake_case_						)
          return tuple(snake_case_						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     List[str] ,     snake_case_					:     Tuple=None ,     snake_case_					:     List[str]=None ,     snake_case_					:     Union[str, Any]=False ,     **snake_case_					:     Tuple ,     ):
          snake_case__  :    Optional[Any]    						=      BertPreTokenizer()
          return super().save_pretrained(snake_case_ ,     snake_case_ ,     snake_case_ ,     snake_case_ ,     **snake_case_						)





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

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

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