import torch

from transformers import AutoTokenizer




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 def __init__(      self							:		Optional[int]							,      lowerCamelCase							:		Any	)			->				str:
        _UpperCamelCase  :							List[str]	    =	AutoTokenizer.from_pretrained(lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		List[str]	)			->				str:

        if isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							Any	    =	None
               for l in x:
                      _UpperCamelCase  :							List[str]	    =	self.tokenizer(
                          lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      truncation=lowerCamelCase							,      return_tensors='pt'							,      )
                      _UpperCamelCase  :							Optional[Any]	    =	torch.tensor([len(lowerCamelCase	)]	)
                      if d is not None:
                             for k in d.keys():
                                    _UpperCamelCase  :							Dict	    =	torch.cat((d[k], t[k])							,      0	)
                      else:
                             _UpperCamelCase  :							Optional[int]	    =	t

               _UpperCamelCase  :							Optional[int]	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'	)	)
               _UpperCamelCase  :							Dict	    =	torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'	)	)

        elif isinstance(lowerCamelCase							,      lowerCamelCase	) and all(isinstance(_x							,      lowerCamelCase	) for _x in x	):
               _UpperCamelCase  :							int	    =	self.tokenizer(
                   lowerCamelCase							,      padding='max_length'							,      max_length=3_8_4							,      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							:		List[Any]							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[str]=0.7_0	)			->				Optional[int]:
        _UpperCamelCase  :							Union[str, Any]	    =	[]
        for idx in range(len(W_query['input_ids']	)	):
               _UpperCamelCase  :							List[Any]	    =	range(p_start.shape[1]	)

               _UpperCamelCase  :							List[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 lowerCamelCase	: (tup[2] * tup[3])							,      reverse=lowerCamelCase	)
               _UpperCamelCase  :							Dict	    =	[]
               for k in range(len(lowerCamelCase	)	):
                      if output[k][2] * output[k][3] >= thresh:
                             _UpperCamelCase    ,   _UpperCamelCase  :							int	    =	output[k][0], output[k][1]
                             _UpperCamelCase  :							Optional[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(lowerCamelCase	)

        return final_outputs










import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_UpperCAmelCase										=					logging.get_logger(__name__)



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


              warnings.warn(
                  "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                  " Please use ConvNextImageProcessor instead."       , _SCREAMING_SNAKE_CASE       , )
              super().__init__(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

