


'''simple docstring'''



import torch

from transformers import AutoTokenizer



class       a							(						_a			):


     """simple docstring"""






     def __init__(							self	:						Union[str, Any]				,					snake_case	:						Optional[Any]     )		->				str:
          __UpperCAmelCase      :					int    				=     AutoTokenizer.from_pretrained(snake_case     )

     def 					lowerCamelCase__					(							self	:						List[Any]				,					snake_case	:						Any     )		->				Tuple:

          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     )		->				List[Any]:
          __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				,  __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__)