

'''simple docstring'''
import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def  _A		(     snake_case					)      ->     List[str]:
   return 1.0 / (1.0 + np.exp(-_outputs					))







def  _A		(     snake_case					)      ->     Optional[int]:
   _lowercase      :				Optional[Any]      					=       np.max(_outputs		,							axis=-1		,							keepdims=snake_case					)
   _lowercase      :				Any      					=       np.exp(_outputs - maxes					)
   return shifted_exp / shifted_exp.sum(axis=-1		,							keepdims=snake_case					)


class      a__  (							lowerCAmelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Dict     	=							"""sigmoid"""
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							"""softmax"""
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							"""none"""




@add_end_docstrings(
    lowerCAmelCase_  ,				r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    '  ,				)
class      a__  (							lowerCAmelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Optional[Any]     	=							False
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							ClassificationFunction.NONE


   def __init__(			self      ,    **_UpperCamelCase  ):





      """simple docstring"""





      super().__init__(**__SCREAMING_SNAKE_CASE  )

      self.check_model_type(
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
          if self.framework == "tf"
          else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase=None      ,    _UpperCamelCase=""      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       tokenizer_kwargs

      _lowercase      :				Optional[int]      					=       {}
      if hasattr(self.model.config      ,    "return_all_scores"  ) and return_all_scores is None:
         _lowercase      :				Any      					=       self.model.config.return_all_scores

      if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE  ) or top_k is None:
         _lowercase      :				Tuple      					=       top_k
         _lowercase      :				Tuple      					=       False
      elif return_all_scores is not None:
         warnings.warn(
             "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
             " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."      ,    __SCREAMING_SNAKE_CASE      ,    )
         if return_all_scores:
            _lowercase      :				str      					=       None
         else:
            _lowercase      :				Dict      					=       1

      if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE  ):
         _lowercase      :				Optional[int]      					=       ClassificationFunction[function_to_apply.upper()]

      if function_to_apply is not None:
         _lowercase      :				Optional[int]      					=       function_to_apply
      return preprocess_params, {}, postprocess_params


   def __call__(			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       super().__call__(*__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE  )
      # TODO try and retrieve it in a nicer way from _sanitize_parameters.
      _lowercase      :				Any      					=       "top_k" not in kwargs
      if isinstance(args[0]      ,    __SCREAMING_SNAKE_CASE  ) and _legacy:
         # This pipeline is odd, and return a list when single item is run
         return [result]
      else:
         return result


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.framework
      if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE  ):
         return self.tokenizer(**__SCREAMING_SNAKE_CASE      ,    return_tensors=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE  )
      elif isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE  ) and len(__SCREAMING_SNAKE_CASE  ) == 1 and isinstance(inputs[0]      ,    __SCREAMING_SNAKE_CASE  ) and len(inputs[0]  ) == 2:
         # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
         return self.tokenizer(
             text=inputs[0][0]      ,    text_pair=inputs[0][1]      ,    return_tensors=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE  )
      elif isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE  ):
         # This is likely an invalid usage of the pipeline attempting to pass text pairs.
         raise ValueError(
             "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
             " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."  )
      return self.tokenizer(__SCREAMING_SNAKE_CASE      ,    return_tensors=__SCREAMING_SNAKE_CASE      ,    **__SCREAMING_SNAKE_CASE  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return self.model(**__SCREAMING_SNAKE_CASE  )






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase=1      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      if function_to_apply is None:
         if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
            _lowercase      :				str      					=       ClassificationFunction.SIGMOID
         elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
            _lowercase      :				str      					=       ClassificationFunction.SOFTMAX
         elif hasattr(self.model.config      ,    "function_to_apply"  ) and function_to_apply is None:
            _lowercase      :				Any      					=       self.model.config.function_to_apply
         else:
            _lowercase      :				Any      					=       ClassificationFunction.NONE

      _lowercase      :				Dict      					=       model_outputs["logits"][0]
      _lowercase      :				Union[str, Any]      					=       outputs.numpy()

      if function_to_apply == ClassificationFunction.SIGMOID:
         _lowercase      :				str      					=       sigmoid(__SCREAMING_SNAKE_CASE  )
      elif function_to_apply == ClassificationFunction.SOFTMAX:
         _lowercase      :				str      					=       softmax(__SCREAMING_SNAKE_CASE  )
      elif function_to_apply == ClassificationFunction.NONE:
         _lowercase      :				Union[str, Any]      					=       outputs
      else:
         raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''  )

      if top_k == 1 and _legacy:
         return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

      _lowercase      :				Optional[int]      					=       [
          {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(__SCREAMING_SNAKE_CASE  )
      ]
      if not _legacy:
         dict_scores.sort(key=lambda _UpperCamelCase  : x["score"]      ,    reverse=__SCREAMING_SNAKE_CASE  )
         if top_k is not None:
            _lowercase      :				List[Any]      					=       dict_scores[:top_k]
      return dict_scores









'''simple docstring'''
import logging

from transformers import PretrainedConfig


_snake_case         =  logging.getLogger(__name__)


_snake_case         =  {
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'bertabs'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=512      ,    _UpperCamelCase=6      ,    _UpperCamelCase=512      ,    _UpperCamelCase=8      ,    _UpperCamelCase=512      ,    _UpperCamelCase=0.2      ,    _UpperCamelCase=6      ,    _UpperCamelCase=768      ,    _UpperCamelCase=8      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=0.2      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )

      _lowercase      :				List[str]      					=       vocab_size
      _lowercase      :				Tuple      					=       max_pos

      _lowercase      :				int      					=       enc_layers
      _lowercase      :				str      					=       enc_hidden_size
      _lowercase      :				Optional[Any]      					=       enc_heads
      _lowercase      :				Union[str, Any]      					=       enc_ff_size
      _lowercase      :				Tuple      					=       enc_dropout

      _lowercase      :				Dict      					=       dec_layers
      _lowercase      :				List[str]      					=       dec_hidden_size
      _lowercase      :				List[str]      					=       dec_heads
      _lowercase      :				str      					=       dec_ff_size
      _lowercase      :				str      					=       dec_dropout






