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_xlnet import XLNetTokenizer
else:
      UpperCamelCase__    							= None


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__    							= {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

UpperCamelCase__    							= {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

UpperCamelCase__    							= '▁'

# Segments (not really needed)
UpperCamelCase__    							= 0
UpperCamelCase__    							= 1
UpperCamelCase__    							= 2
UpperCamelCase__    							= 3
UpperCamelCase__    							= 4





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			int              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			Optional[int]              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			List[str]              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Union[str, Any]              =      'left'
   __UpperCAmelCase			:			str              =      XLNetTokenizer


   def __init__(self					:						Dict	,  __UpperCAmelCase					:						List[str]=None	,  __UpperCAmelCase					:						Optional[int]=None	,  __UpperCAmelCase					:						Optional[Any]=False	,  __UpperCAmelCase					:						str=True	,  __UpperCAmelCase					:						str=False	,  __UpperCAmelCase					:						Union[str, Any]="<s>"	,  __UpperCAmelCase					:						List[Any]="</s>"	,  __UpperCAmelCase					:						Optional[Any]="<unk>"	,  __UpperCAmelCase					:						Tuple="<sep>"	,  __UpperCAmelCase					:						str="<pad>"	,  __UpperCAmelCase					:						Optional[Any]="<cls>"	,  __UpperCAmelCase					:						Tuple="<mask>"	,  __UpperCAmelCase					:						Tuple=["<eop>", "<eod>"]	,  **__UpperCAmelCase					:						Optional[int]	,  )						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase	,  lstrip=__UpperCAmelCase	,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else mask_token

         super().__init__(
             vocab_file=__UpperCAmelCase	,  tokenizer_file=__UpperCAmelCase	,  do_lower_case=__UpperCAmelCase	,  remove_space=__UpperCAmelCase	,  keep_accents=__UpperCAmelCase	,  bos_token=__UpperCAmelCase	,  eos_token=__UpperCAmelCase	,  unk_token=__UpperCAmelCase	,  sep_token=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  cls_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  additional_special_tokens=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         UpperCAmelCase__								=  3
         UpperCAmelCase__								=  do_lower_case
         UpperCAmelCase__								=  remove_space
         UpperCAmelCase__								=  keep_accents
         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  False if not self.vocab_file else True


   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]
         if token_ids_a is None:
               return token_ids_a + sep + cls
         return token_ids_a + sep + token_ids_a + sep + cls


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



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [2]

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





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						Optional[str] = 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(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

         return (out_vocab_file,)




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,
      )



