


import torch

from transformers import AutoTokenizer





class 			lowerCAmelCase__(     _a    ):






    '''simple docstring'''





    def __init__(						self					: Union[str, Any]							, __snake_case					: Optional[Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   int							= AutoTokenizer.from_pretrained(__snake_case						)





    def        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: Any						):





       '''simple docstring'''

       if isinstance(__snake_case							, __snake_case						) and all(isinstance(_x							, __snake_case						) for _x in x						):
          UpperCAmelCase_				:   Union[str, Any]							= None
          for l in x:
             UpperCAmelCase_				:   Tuple							= self.tokenizer(
                 __snake_case							, padding='''max_length'''							, max_length=384							, truncation=__snake_case							, return_tensors='''pt'''							, )
             UpperCAmelCase_				:   Any							= torch.tensor([len(__snake_case						)]						)
             if d is not None:
                for k in d.keys():
                   UpperCAmelCase_				:   Optional[Any]							= torch.cat((d[k], t[k])							, 0						)
             else:
                UpperCAmelCase_				:   int							= t

          UpperCAmelCase_				:   str							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''						)						)
          UpperCAmelCase_				:   List[Any]							= torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''						)						)

       elif isinstance(__snake_case							, __snake_case						) and all(isinstance(_x							, __snake_case						) for _x in x						):
          UpperCAmelCase_				:   Optional[int]							= self.tokenizer(
              __snake_case							, padding='''max_length'''							, max_length=384							, truncation=__snake_case							, 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        _lowerCamelCase			(						self					: List[Any]							, __snake_case					: Optional[int]							, __snake_case					: Union[str, Any]							, __snake_case					: List[str]							, __snake_case					: List[str]							, __snake_case					: Dict=0.70						):





       '''simple docstring'''
       UpperCAmelCase_				:   Dict							= []
       for idx in range(len(W_query['''input_ids''']						)						):
          UpperCAmelCase_				:   Dict							= range(p_start.shape[1]						)

          UpperCAmelCase_				:   Union[str, Any]							= []
          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 __snake_case						: (tup[2] * tup[3])							, reverse=__snake_case						)
          UpperCAmelCase_				:   Dict							= []
          for k in range(len(__snake_case						)						):
             if output[k][2] * output[k][3] >= thresh:
                UpperCAmelCase_				:   Union[str, Any]							= output[k][0], output[k][1]
                UpperCAmelCase_				:   Any							= 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(__snake_case						)

       return final_outputs



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

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