

import torch

from transformers import AutoTokenizer






class    __A				(							lowerCAmelCase ):






    def __init__(	self			:    int    ,				UpperCAmelCase_			:    Dict    ):
        lowerCAmelCase				:			List[Any]    			=      AutoTokenizer.from_pretrained(UpperCAmelCase_    )






    def 	lowercase__ (	self			:    Dict    ,				UpperCAmelCase_			:    List[Any]    ):

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

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

        elif isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ) and all(isinstance(_x    ,				UpperCAmelCase_    ) for _x in x    ):
            lowerCAmelCase				:			int    			=      self.tokenizer(
                UpperCAmelCase_    ,				padding='max_length'    ,				max_length=384    ,				truncation=UpperCAmelCase_    ,				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 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    str=0.70    ):
        lowerCAmelCase				:			str    			=      []
        for idx in range(len(W_query['input_ids']    )    ):
            lowerCAmelCase				:			Union[str, Any]    			=      range(p_start.shape[1]    )

            lowerCAmelCase				:			str    			=      []
            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 UpperCAmelCase_    : (tup[2] * tup[3])    ,				reverse=UpperCAmelCase_    )
            lowerCAmelCase				:			Optional[Any]    			=      []
            for k in range(len(UpperCAmelCase_    )    ):
                if output[k][2] * output[k][3] >= thresh:
                    lowerCAmelCase			,  lowerCAmelCase				:			int    			=      output[k][0], output[k][1]
                    lowerCAmelCase				:			List[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(UpperCAmelCase_    )

        return final_outputs








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


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


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A   :     Tuple      =			[
     '''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   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





