

'''simple docstring'''
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


lowerCamelCase :      str					 =     logging.get_logger(__name__)

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

lowerCamelCase :      Optional[int]					 =     {
    "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"
        ),
    },
}

lowerCamelCase :      Dict					 =     {
    "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,
}

lowerCamelCase :      int					 =     {
    "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__    (       A__  ):
  A__		 =				VOCAB_FILES_NAMES
  A__		 =				PRETRAINED_VOCAB_FILES_MAP
  A__		 =				PRETRAINED_INIT_CONFIGURATION
  A__		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__		 =				RealmTokenizer





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





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE							    =do_lower_case





  def 						A  (		self      :	int							,					_a      :	Union[str, Any]							,					**_a      :	List[Any] )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =PaddingStrategy.MAX_LENGTH

       _SCREAMING_SNAKE_CASE							    =text
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('text_pair'							,					_a )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('return_tensors'							,					_a )

       _SCREAMING_SNAKE_CASE							    ={
           'input_ids': [],
           'attention_mask': [],
           'token_type_ids': [],
       }

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

            _SCREAMING_SNAKE_CASE							    =super().__call__(_a							,					_a							,					return_tensors=_a							,					**_a )

            _SCREAMING_SNAKE_CASE							    =encoded_candidates.get('input_ids' )
            _SCREAMING_SNAKE_CASE							    =encoded_candidates.get('attention_mask' )
            _SCREAMING_SNAKE_CASE							    =encoded_candidates.get('token_type_ids' )

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

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

       return BatchEncoding(_a							,					tensor_type=_a )





  def 						A  (		self      :	int							,					_a      :	Optional[Any]							,					_a      :	Optional[int]=None )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[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 						A  (		self      :	str							,					_a      :	List[int]							,					_a      :	Optional[List[int]] = None )	->    List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[self.sep_token_id]
       _SCREAMING_SNAKE_CASE							    =[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 						A  (		self      :	Dict							,					_a      :	str							,					_a      :	Optional[str] = None )	->    Tuple[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self._tokenizer.model.save(_a							,					name=_a )
       return tuple(_a )



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
