





import torch

from transformers import AutoTokenizer





class   lowercase__     (				__A	):







  def __init__(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[int]			 =		AutoTokenizer.from_pretrained(_lowercase    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):

       if isinstance(_lowercase     ,    _lowercase    ) and all(isinstance(_x     ,    _lowercase    ) for _x in x    ):
            lowerCAmelCase_       :							List[str]			 =		None
            for l in x:
                 lowerCAmelCase_       :							Dict			 =		self.tokenizer(
                     _lowercase     ,    padding="""max_length"""     ,    max_length=384     ,    truncation=_lowercase     ,    return_tensors="""pt"""     ,    )
                 lowerCAmelCase_       :							int			 =		torch.tensor([len(_lowercase    )]    )
                 if d is not None:
                      for k in d.keys():
                           lowerCAmelCase_       :							Dict			 =		torch.cat((d[k], t[k])     ,    0    )
                 else:
                      lowerCAmelCase_       :							List[str]			 =		t

            lowerCAmelCase_       :							List[Any]			 =		torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""    )    )
            lowerCAmelCase_       :							int			 =		torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""    )    )

       elif isinstance(_lowercase     ,    _lowercase    ) and all(isinstance(_x     ,    _lowercase    ) for _x in x    ):
            lowerCAmelCase_       :							Optional[int]			 =		self.tokenizer(
                _lowercase     ,    padding="""max_length"""     ,    max_length=384     ,    truncation=_lowercase     ,    return_tensors="""pt"""     ,    )

       else:
            raise Exception(
                """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
                """ strings` for supports are supported."""    )

       return d







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase=0.70    ):
       lowerCAmelCase_       :							Dict			 =		[]
       for idx in range(len(W_query["""input_ids"""]    )    ):
            lowerCAmelCase_       :							Optional[int]			 =		range(p_start.shape[1]    )

            lowerCAmelCase_       :							Dict			 =		[]
            for start_id in start_indexes:
                 for end_id in end_indexes:
                      if start_id < end_id:
                           output.append(
                               (
                                   start_id,
                                   end_id,
                                   p_start[idx][start_id].item(),
                                   p_end[idx][end_id].item(),
                               )    )

            output.sort(key=lambda _lowercase    : (tup[2] * tup[3])     ,    reverse=_lowercase    )
            lowerCAmelCase_       :							Tuple			 =		[]
            for k in range(len(_lowercase    )    ):
                 if output[k][2] * output[k][3] >= thresh:
                      lowerCAmelCase_ ,	lowerCAmelCase_       :							Optional[int]			 =		output[k][0], output[k][1]
                      lowerCAmelCase_       :							str			 =		self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]    )
                      temp.append((decoded, output[k][2] * output[k][3])    )

            final_outputs.append(_lowercase    )

       return final_outputs







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_       : Union[str, Any]							=    logging.get_logger(__name__)

UpperCAmelCase_       : int							=    {
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}





class   lowercase__     (				__A	):
  __UpperCamelCase						  =	"""electra"""







  def __init__(						self     ,    _lowercase=30_522     ,    _lowercase=128     ,    _lowercase=256     ,    _lowercase=12     ,    _lowercase=4     ,    _lowercase=1_024     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0.1     ,    _lowercase=512     ,    _lowercase=2     ,    _lowercase=0.02     ,    _lowercase=1e-12     ,    _lowercase="first"     ,    _lowercase=True     ,    _lowercase="gelu"     ,    _lowercase=0.1     ,    _lowercase=0     ,    _lowercase="absolute"     ,    _lowercase=True     ,    _lowercase=None     ,    **_lowercase     ,    ):
       super().__init__(pad_token_id=_lowercase     ,    **_lowercase    )

       lowerCAmelCase_       :							Dict			 =		vocab_size
       lowerCAmelCase_       :							Tuple			 =		embedding_size
       lowerCAmelCase_       :							Any			 =		hidden_size
       lowerCAmelCase_       :							Optional[Any]			 =		num_hidden_layers
       lowerCAmelCase_       :							Union[str, Any]			 =		num_attention_heads
       lowerCAmelCase_       :							List[str]			 =		intermediate_size
       lowerCAmelCase_       :							Union[str, Any]			 =		hidden_act
       lowerCAmelCase_       :							Any			 =		hidden_dropout_prob
       lowerCAmelCase_       :							Optional[Any]			 =		attention_probs_dropout_prob
       lowerCAmelCase_       :							int			 =		max_position_embeddings
       lowerCAmelCase_       :							Optional[int]			 =		type_vocab_size
       lowerCAmelCase_       :							Dict			 =		initializer_range
       lowerCAmelCase_       :							Any			 =		layer_norm_eps

       lowerCAmelCase_       :							str			 =		summary_type
       lowerCAmelCase_       :							Optional[int]			 =		summary_use_proj
       lowerCAmelCase_       :							Optional[int]			 =		summary_activation
       lowerCAmelCase_       :							int			 =		summary_last_dropout
       lowerCAmelCase_       :							str			 =		position_embedding_type
       lowerCAmelCase_       :							Optional[int]			 =		use_cache
       lowerCAmelCase_       :							Tuple			 =		classifier_dropout





class   lowercase__     (				__A	):







  @property
  def 						UpperCAmelCase__		(						self    ):
       if self.task == "multiple-choice":
            lowerCAmelCase_       :							Optional[int]			 =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_       :							Any			 =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]    )
