



'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			=					logging.get_logger(__name__)

a_			=					{
    """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  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """realm"""




 def __init__(						self	,				__UpperCAmelCase=30522	,				__UpperCAmelCase=768	,				__UpperCAmelCase=128	,				__UpperCAmelCase=12	,				__UpperCAmelCase=12	,				__UpperCAmelCase=8	,				__UpperCAmelCase=3072	,				__UpperCAmelCase="gelu_new"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=1E-1_2	,				__UpperCAmelCase=256	,				__UpperCAmelCase=10	,				__UpperCAmelCase=1E-3	,				__UpperCAmelCase=5	,				__UpperCAmelCase=320	,				__UpperCAmelCase=13353718	,				__UpperCAmelCase=5000	,				__UpperCAmelCase=1	,				__UpperCAmelCase=0	,				__UpperCAmelCase=2	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(pad_token_id=__UpperCAmelCase	,				bos_token_id=__UpperCAmelCase	,				eos_token_id=__UpperCAmelCase	,				**__UpperCAmelCase				)

    # Common config
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    retriever_proj_size
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    num_candidates
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    layer_norm_eps

    # Reader config
    __lowerCamelCase													=    span_hidden_size
    __lowerCamelCase													=    max_span_width
    __lowerCamelCase													=    reader_layer_norm_eps
    __lowerCamelCase													=    reader_beam_size
    __lowerCamelCase													=    reader_seq_len

    # Retrieval config
    __lowerCamelCase													=    num_block_records
    __lowerCamelCase													=    searcher_beam_size











import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				padding=self.padding	,				max_length=self.max_length	,				pad_to_multiple_of=self.pad_to_multiple_of	,				return_tensors='''pt''' if labels is None else None	,				)

    if labels is None:
       return batch

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






