import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__    							= {
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__    							= {
    'google/realm-cc-news-pretrained-embedder': 5_1_2,
    'google/realm-cc-news-pretrained-encoder': 5_1_2,
    'google/realm-cc-news-pretrained-scorer': 5_1_2,
    'google/realm-cc-news-pretrained-openqa': 5_1_2,
    'google/realm-orqa-nq-openqa': 5_1_2,
    'google/realm-orqa-nq-reader': 5_1_2,
    'google/realm-orqa-wq-openqa': 5_1_2,
    'google/realm-orqa-wq-reader': 5_1_2,
}

UpperCamelCase__    							= {
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Tuple              =      VOCAB_FILES_NAMES
   __UpperCAmelCase			:			Any              =      PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:			List[Any]              =      PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase			:			Tuple              =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:			Tuple              =      RealmTokenizer


   def __init__(self					:						Optional[Any]	,  __UpperCAmelCase					:						int=None	,  __UpperCAmelCase					:						List[Any]=None	,  __UpperCAmelCase					:						int=True	,  __UpperCAmelCase					:						Tuple="[UNK]"	,  __UpperCAmelCase					:						Dict="[SEP]"	,  __UpperCAmelCase					:						Tuple="[PAD]"	,  __UpperCAmelCase					:						Optional[Any]="[CLS]"	,  __UpperCAmelCase					:						Any="[MASK]"	,  __UpperCAmelCase					:						Any=True	,  __UpperCAmelCase					:						str=None	,  **__UpperCAmelCase					:						Union[str, Any]	,  )						->    Dict:
         """simple docstring"""



         super().__init__(
             __UpperCAmelCase	,  tokenizer_file=__UpperCAmelCase	,  do_lower_case=__UpperCAmelCase	,  unk_token=__UpperCAmelCase	,  sep_token=__UpperCAmelCase	,  pad_token=__UpperCAmelCase	,  cls_token=__UpperCAmelCase	,  mask_token=__UpperCAmelCase	,  tokenize_chinese_chars=__UpperCAmelCase	,  strip_accents=__UpperCAmelCase	,  **__UpperCAmelCase	,  )

         UpperCAmelCase__								=  json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
         if (
             normalizer_state.get("lowercase"	,  __UpperCAmelCase		) != do_lower_case
             or normalizer_state.get("strip_accents"	,  __UpperCAmelCase		) != strip_accents
             or normalizer_state.get("handle_chinese_chars"	,  __UpperCAmelCase		) != tokenize_chinese_chars
         ):
               UpperCAmelCase__								=  getattr(__UpperCAmelCase	,  normalizer_state.pop("type"		)		)
               UpperCAmelCase__								=  do_lower_case
               UpperCAmelCase__								=  strip_accents
               UpperCAmelCase__								=  tokenize_chinese_chars
               UpperCAmelCase__								=  normalizer_class(**__UpperCAmelCase		)

         UpperCAmelCase__								=  do_lower_case


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



         UpperCAmelCase__								=  PaddingStrategy.MAX_LENGTH

         UpperCAmelCase__								=  text
         UpperCAmelCase__								=  kwargs.pop("text_pair"	,  __UpperCAmelCase		)
         UpperCAmelCase__								=  kwargs.pop("return_tensors"	,  __UpperCAmelCase		)

         UpperCAmelCase__								=  {
             "input_ids": [],
             "attention_mask": [],
             "token_type_ids": [],
         }

         for idx, candidate_text in enumerate(__UpperCAmelCase		):
               if batch_text_pair is not None:
                     UpperCAmelCase__								=  batch_text_pair[idx]
               else:
                     UpperCAmelCase__								=  None

               UpperCAmelCase__								=  super().__call__(__UpperCAmelCase	,  __UpperCAmelCase	,  return_tensors=__UpperCAmelCase	,  **__UpperCAmelCase		)

               UpperCAmelCase__								=  encoded_candidates.get("input_ids"		)
               UpperCAmelCase__								=  encoded_candidates.get("attention_mask"		)
               UpperCAmelCase__								=  encoded_candidates.get("token_type_ids"		)

               if encoded_input_ids is not None:
                     output_data["input_ids"].append(__UpperCAmelCase		)
               if encoded_attention_mask is not None:
                     output_data["attention_mask"].append(__UpperCAmelCase		)
               if encoded_token_type_ids is not None:
                     output_data["token_type_ids"].append(__UpperCAmelCase		)

         UpperCAmelCase__								=  {key: item for key, item in output_data.items() if len(__UpperCAmelCase		) != 0}

         return BatchEncoding(__UpperCAmelCase	,  tensor_type=__UpperCAmelCase		)


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



         UpperCAmelCase__								=  [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       lowercase_				(self					:						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 len(cls + token_ids_a + sep		) * [0]
         return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]





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



         UpperCAmelCase__								=  self._tokenizer.model.save(__UpperCAmelCase	,  name=__UpperCAmelCase		)
         return tuple(__UpperCAmelCase		)




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,
      )



