



'''simple docstring'''






import torch

from transformers import AutoTokenizer



class 							lowercase (						A__       ):


 """simple docstring"""


 def __init__(							self						,a_							)	->     List[str]:
        _UpperCAmelCase						:		List[str]      =				AutoTokenizer.from_pretrained(_lowerCAmelCase							)







 def _snake_case				(							self						,a_							)	->     List[str]:

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

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

        elif isinstance(_lowerCAmelCase						,_lowerCAmelCase							) and all(isinstance(_x						,_lowerCAmelCase							) for _x in x							):
               _UpperCAmelCase						:		Union[str, Any]      =				self.tokenizer(
                   _lowerCAmelCase						,padding="""max_length"""						,max_length=384						,truncation=_lowerCAmelCase						,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 _snake_case				(							self						,a_						,a_						,a_						,a_						,a_=0.70							)	->     Optional[int]:
        _UpperCAmelCase						:		Optional[int]      =				[]
        for idx in range(len(W_query["""input_ids"""]							)							):
               _UpperCAmelCase						:		int      =				range(p_start.shape[1]							)

               _UpperCAmelCase						:		Tuple      =				[]
               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 a_							: (tup[2] * tup[3])						,reverse=_lowerCAmelCase							)
               _UpperCAmelCase						:		Any      =				[]
               for k in range(len(_lowerCAmelCase							)							):
                      if output[k][2] * output[k][3] >= thresh:
                             _UpperCAmelCase  ,_UpperCAmelCase						:		Any      =				output[k][0], output[k][1]
                             _UpperCAmelCase						:		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(_lowerCAmelCase							)

        return final_outputs








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_							:				List[Any]						=			{
    """configuration_deta""": ["""DETA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetaConfig"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				Optional[int]						=			["""DetaImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							:				Optional[Any]						=			[
       """DETA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DetaForObjectDetection""",
       """DetaModel""",
       """DetaPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_deta import DetaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deta import (
          DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DetaForObjectDetection,
          DetaModel,
          DetaPreTrainedModel,
      )

else:
   import sys

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



