"""simple docstring"""




from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= {
    'nielsr/canine-s': 2_0_4_8,
}

# Unicode defines 1,114,112 total “codepoints”
UpperCamelCase__    							= 1_1_1_4_1_1_2

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
UpperCamelCase__    							= 0

UpperCamelCase__    							= 0Xe0_00
UpperCamelCase__    							= 0Xe0_01
UpperCamelCase__    							= 0Xe0_02
UpperCamelCase__    							= 0Xe0_03
UpperCamelCase__    							= 0Xe0_04

# Maps special codepoints to human-readable names.
UpperCamelCase__    							= {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: '[CLS]',
    SEP: '[SEP]',
    BOS: '[BOS]',
    MASK: '[MASK]',
    PAD: '[PAD]',
    RESERVED: '[RESERVED]',
}

# Maps special codepoint human-readable names to their codepoint values.
UpperCamelCase__    							= {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Optional[Any]              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


   def __init__(self					:						List[str]	,  __UpperCAmelCase					:						int=chr(__UpperCAmelCase		)	,  __UpperCAmelCase					:						int=chr(__UpperCAmelCase		)	,  __UpperCAmelCase					:						Dict=chr(__UpperCAmelCase		)	,  __UpperCAmelCase					:						int=chr(__UpperCAmelCase		)	,  __UpperCAmelCase					:						Optional[int]=chr(__UpperCAmelCase		)	,  __UpperCAmelCase					:						Any=chr(__UpperCAmelCase		)	,  __UpperCAmelCase					:						Tuple=False	,  __UpperCAmelCase					:						Optional[Any]=2_0_4_8	,  **__UpperCAmelCase					:						Union[str, Any]	,  )						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else bos_token
         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else eos_token
         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else sep_token
         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else cls_token
         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else pad_token

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

         super().__init__(
             bos_token=__UpperCAmelCase	,  eos_token=__UpperCAmelCase	,  sep_token=__UpperCAmelCase	,  cls_token=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  add_prefix_space=__UpperCAmelCase	,  model_max_length=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         # Creates a mapping for looking up the IDs of special symbols.
         UpperCAmelCase__								=  {}
         for codepoint, name in SPECIAL_CODEPOINTS.items():
               UpperCAmelCase__								=  codepoint

         # Creates a mapping for looking up the string forms of special symbol IDs.
         UpperCAmelCase__								=  {
             codepoint: name for name, codepoint in self._special_codepoints.items()
         }

         UpperCAmelCase__								=  UNICODE_VOCAB_SIZE
         UpperCAmelCase__								=  len(self._special_codepoints		)


   @property
   def       lowercase_				(self					:						Dict		)						->    int:
         """simple docstring"""



         return self._unicode_vocab_size


   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						str		)						->    List[str]:
         """simple docstring"""



         return list(__UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						str		)						->    int:
         """simple docstring"""



         try:
               return ord(__UpperCAmelCase		)
         except TypeError:
               raise ValueError(f"""invalid token: '{token}'"""		)


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						int		)						->    str:
         """simple docstring"""



         try:
               if index in SPECIAL_CODEPOINTS:
                     return SPECIAL_CODEPOINTS[index]
               return chr(__UpperCAmelCase		)
         except TypeError:
               raise ValueError(f"""invalid id: {index}"""		)


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Tuple		)						->    Dict:
         """simple docstring"""



         return "".join(__UpperCAmelCase		)


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						List[int]	,  __UpperCAmelCase					:						Optional[List[int]] = None		)						->    List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [self.cls_token_id]

         UpperCAmelCase__								=  cls + token_ids_a + sep
         if token_ids_a is not None:
               result += token_ids_a + sep
         return result


   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						List[int]	,  __UpperCAmelCase					:						Optional[List[int]] = None	,  __UpperCAmelCase					:						bool = False		)						->    List[int]:
         """simple docstring"""



         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase	,  token_ids_a=__UpperCAmelCase	,  already_has_special_tokens=__UpperCAmelCase		)

         UpperCAmelCase__								=  [1] + ([0] * len(__UpperCAmelCase		)) + [1]
         if token_ids_a is not None:
               result += ([0] * len(__UpperCAmelCase		)) + [1]
         return result


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						List[int]	,  __UpperCAmelCase					:						Optional[List[int]] = None		)						->    List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [self.cls_token_id]

         UpperCAmelCase__								=  len(cls + token_ids_a + sep		) * [0]
         if token_ids_a is not None:
               result += len(token_ids_a + sep		) * [1]
         return result





   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    Any:
         """simple docstring"""



         return ()




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



