



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

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


if is_sentencepiece_available():
       from .tokenization_big_bird import BigBirdTokenizer
else:
       a__   :					int 						=      None

a__   :					int 						=      logging.get_logger(__name__)
a__   :					Any 						=      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a__   :					Union[str, Any] 						=      {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

a__   :					Optional[Any] 						=      {
    "google/bigbird-roberta-base": 40_96,
    "google/bigbird-roberta-large": 40_96,
    "google/bigbird-base-trivia-itc": 40_96,
}


a__   :					List[str] 						=      "▁"



class    UpperCAmelCase__(				__lowercase						):

   '''simple docstring'''




   A     :	str												=     VOCAB_FILES_NAMES
   A     :	List[Any]												=     PRETRAINED_VOCAB_FILES_MAP
   A     :	Tuple												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A     :	List[str]												=     BigBirdTokenizer
   A     :	Any												=     ["input_ids", "attention_mask"]
   A     :	List[int]												=     []


   def __init__(    self	:      List[str]      ,					lowerCAmelCase	:      Tuple=None      ,					lowerCAmelCase	:      str=None      ,					lowerCAmelCase	:      Union[str, Any]="<unk>"      ,					lowerCAmelCase	:      List[Any]="<s>"      ,					lowerCAmelCase	:      List[Any]="</s>"      ,					lowerCAmelCase	:      Union[str, Any]="<pad>"      ,					lowerCAmelCase	:      Optional[int]="[SEP]"      ,					lowerCAmelCase	:      List[str]="[MASK]"      ,					lowerCAmelCase	:      Optional[Any]="[CLS]"      ,					**lowerCAmelCase	:      Tuple      ,					)						->					List[str]:




         """simple docstring"""

         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else bos_token
         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else eos_token
         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else unk_token
         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else pad_token
         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else cls_token
         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else sep_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowercase__  =	AddedToken(lowerCAmelCase      ,					lstrip=lowerCAmelCase      ,					rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase      ,					lowerCAmelCase) else mask_token

         super().__init__(
             lowerCAmelCase      ,					tokenizer_file=lowerCAmelCase      ,					bos_token=lowerCAmelCase      ,					eos_token=lowerCAmelCase      ,					unk_token=lowerCAmelCase      ,					sep_token=lowerCAmelCase      ,					pad_token=lowerCAmelCase      ,					cls_token=lowerCAmelCase      ,					mask_token=lowerCAmelCase      ,					**lowerCAmelCase      ,					)

         lowercase__  =	vocab_file
         lowercase__  =	False if not self.vocab_file else True


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str] = None)						->					List[int]:




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[self.cls_token_id]
         if token_ids_a is None:
               return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep


   def       UpperCAmelCase		(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      int = None      ,					lowerCAmelCase	:      List[Any] = False)						->					List[int]:




         """simple docstring"""

         if already_has_special_tokens:
               if token_ids_a is not None:
                     raise ValueError(
                         'You should not supply a second sequence if the provided sequence of '
                         'ids is already formatted with special tokens for the model.')
               return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is None:
               return [1] + ([0] * len(lowerCAmelCase)) + [1]
         return [1] + ([0] * len(lowerCAmelCase)) + [1] + ([0] * len(lowerCAmelCase)) + [1]


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      List[Any] = None)						->					List[int]:




         """simple docstring"""

         lowercase__  =	[self.sep_token_id]
         lowercase__  =	[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       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      int = None)						->					Tuple[str]:




         """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)

         return (out_vocab_file,)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__						=				{
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__						=				[
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		lowerCamelCase__						=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



