



"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_				= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_				= {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE_				= {
    """allenai/led-base-16384""": 1_63_84,
}




class 			snake_case_						(						__a				):
       __lowerCAmelCase    					=					VOCAB_FILES_NAMES
       __lowerCAmelCase    					=					PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase    					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase    					=					LEDTokenizer
       __lowerCAmelCase    					=					["input_ids", "attention_mask"]






       def __init__(   self			,     a_=None			,     a_=None			,     a_=None			,     a_="replace"			,     a_="<s>"			,     a_="</s>"			,     a_="</s>"			,     a_="<s>"			,     a_="<unk>"			,     a_="<pad>"			,     a_="<mask>"			,     a_=False			,     a_=True			,     **a_			,     ):
         super().__init__(
             A__			,     A__			,     tokenizer_file=A__			,     errors=A__			,     bos_token=A__			,     eos_token=A__			,     sep_token=A__			,     cls_token=A__			,     unk_token=A__			,     pad_token=A__			,     mask_token=A__			,     add_prefix_space=A__			,     trim_offsets=A__			,     **A__			,     )

         a_     :      Optional[Any] 						=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
         if pre_tok_state.get("add_prefix_space"			,     A__      ) != add_prefix_space:
           a_     :      Optional[int] 						=     getattr(A__			,     pre_tok_state.pop("type"      )      )
           a_     :      List[Any] 						=     add_prefix_space
           a_     :      List[Any] 						=     pre_tok_class(**A__      )

         a_     :      Tuple 						=     add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         a_     :      List[str] 						=     "post_processor"
         a_     :      str 						=     getattr(self.backend_tokenizer			,     A__			,     A__      )
         if tokenizer_component_instance:
           a_     :      Optional[int] 						=     json.loads(tokenizer_component_instance.__getstate__()      )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             a_     :      int 						=     tuple(state["sep"]      )
           if "cls" in state:
             a_     :      int 						=     tuple(state["cls"]      )

           a_     :      Dict 						=     False

           if state.get("add_prefix_space"			,     A__      ) != add_prefix_space:
             a_     :      Optional[int] 						=     add_prefix_space
             a_     :      List[str] 						=     True

           if state.get("trim_offsets"			,     A__      ) != trim_offsets:
             a_     :      Any 						=     trim_offsets
             a_     :      Optional[Any] 						=     True

           if changes_to_apply:
             a_     :      Dict 						=     getattr(A__			,     state.pop("type"      )      )
             a_     :      List[str] 						=     component_class(**A__      )
             setattr(self.backend_tokenizer			,     A__			,     A__      )






       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def snake_case_  (   self      ):
         if self._mask_token is None:
           if self.verbose:
             logger.error("Using mask_token, but it is not set yet."      )
           return None
         return str(self._mask_token      )






       @mask_token.setter
       def snake_case_  (   self			,     a_      ):
         a_     :      str 						=     AddedToken(A__			,     lstrip=A__			,     rstrip=A__      ) if isinstance(A__			,     A__      ) else value
         a_     :      Dict 						=     value






       def snake_case_  (   self			,     *a_			,     **a_      ):
         a_     :      Tuple 						=     kwargs.get("is_split_into_words"			,     A__      )

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."      )

         return super()._batch_encode_plus(*A__			,     **A__      )






       def snake_case_  (   self			,     *a_			,     **a_      ):
         a_     :      List[str] 						=     kwargs.get("is_split_into_words"			,     A__      )

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."      )

         return super()._encode_plus(*A__			,     **A__      )






       def snake_case_  (   self			,     a_			,     a_ = None      ):
         a_     :      Optional[Any] 						=     self._tokenizer.model.save(A__			,     name=A__      )
         return tuple(A__      )






       def snake_case_  (   self			,     a_			,     a_=None      ):
         a_     :      Union[str, Any] 						=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
           return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def snake_case_  (   self			,     a_			,     a_ = None      ):
         a_     :      Optional[Any] 						=     [self.sep_token_id]
         a_     :      Tuple 						=     [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 + sep + token_ids_a + sep      ) * [0]






       def snake_case_  (   self			,     a_			,     a_ = None			,     a_ = PaddingStrategy.DO_NOT_PAD			,     a_ = None			,     a_ = None			,     ):
         a_     :      Union[str, Any] 						=     super()._pad(
             encoded_inputs=A__			,     max_length=A__			,     padding_strategy=A__			,     pad_to_multiple_of=A__			,     return_attention_mask=A__			,     )

         # Load from model defaults
         if return_attention_mask is None:
           a_     :      str 						=     "attention_mask" in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
           a_     :      List[Any] 						=     encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           a_     :      int 						=     len(encoded_inputs["global_attention_mask"]      ) != len(A__      )

           if needs_to_be_padded:
             a_     :      Optional[Any] 						=     len(A__      ) - len(encoded_inputs["global_attention_mask"]      )

             if self.padding_side == "right":
               # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
               a_     :      Union[str, Any] 						=     (
                   encoded_inputs["global_attention_mask"] + [-1] * difference
               )
             elif self.padding_side == "left":
               a_     :      Union[str, Any] 						=     [-1] * difference + encoded_inputs[
                   "global_attention_mask"
               ]
             else:
               raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

         return encoded_inputs




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"realm"






							def __init__(   self			,     a_=3_0_5_2_2			,     a_=7_6_8			,     a_=1_2_8			,     a_=1_2			,     a_=1_2			,     a_=8			,     a_=3_0_7_2			,     a_="gelu_new"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=2			,     a_=0.02			,     a_=1e-12			,     a_=2_5_6			,     a_=1_0			,     a_=1e-3			,     a_=5			,     a_=3_2_0			,     a_=1_3_3_5_3_7_1_8			,     a_=5_0_0_0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

									# Common config
									a_     :      Optional[int] 						=     vocab_size
									a_     :      List[Any] 						=     max_position_embeddings
									a_     :      Optional[Any] 						=     hidden_size
									a_     :      Optional[Any] 						=     retriever_proj_size
									a_     :      List[str] 						=     num_hidden_layers
									a_     :      List[Any] 						=     num_attention_heads
									a_     :      Tuple 						=     num_candidates
									a_     :      str 						=     intermediate_size
									a_     :      Optional[int] 						=     hidden_act
									a_     :      List[str] 						=     hidden_dropout_prob
									a_     :      List[str] 						=     attention_probs_dropout_prob
									a_     :      Tuple 						=     initializer_range
									a_     :      Tuple 						=     type_vocab_size
									a_     :      str 						=     layer_norm_eps

									# Reader config
									a_     :      str 						=     span_hidden_size
									a_     :      Union[str, Any] 						=     max_span_width
									a_     :      Tuple 						=     reader_layer_norm_eps
									a_     :      List[Any] 						=     reader_beam_size
									a_     :      str 						=     reader_seq_len

									# Retrieval config
									a_     :      str 						=     num_block_records
									a_     :      int 						=     searcher_beam_size