






'''simple docstring'''







import torch

from transformers import AutoTokenizer






class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[int]				,				lowercase__    :	Any					)  ->       Dict:
           __A		   =      AutoTokenizer.from_pretrained(lowercase__					)






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Tuple					)  ->       Tuple:

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

                 __A		   =      torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"					)					)
                 __A		   =      torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"					)					)

           elif isinstance(lowercase__				,				lowercase__					) and all(isinstance(_x				,				lowercase__					) for _x in x					):
                 __A		   =      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    :	Optional[int]				,				lowercase__    :	Optional[int]				,				lowercase__    :	List[Any]				,				lowercase__    :	Optional[int]				,				lowercase__    :	Dict				,				lowercase__    :	Tuple=0.70					)  ->       Tuple:
           __A		   =      []
           for idx in range(len(W_query["input_ids"]					)					):
                 __A		   =      __A		   =      range(p_start.shape[1]					)

                 __A		   =      []
                 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__					)
                 __A		   =      []
                 for k in range(len(lowercase__					)					):
                       if output[k][2] * output[k][3] >= thresh:
                             __A   ,				__A		   =      output[k][0], output[k][1]
                             __A		   =      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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A      :       str      =	{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A      :       str      =	[
           'UMT5EncoderModel',
           'UMT5ForConditionalGeneration',
           'UMT5ForQuestionAnswering',
           'UMT5Model',
           'UMT5PreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

       __A      :       Optional[int]      =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


