





'''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 _lowerCAmelCase     (      _lowerCAmelCase    )->     Optional[int]:
 return 1.0 / (1.0 + np.exp(-_outputs    ))







def _lowerCAmelCase     (      _lowerCAmelCase    )->     List[Any]:
 __UpperCAmelCase     :   int					=   np.max(_outputs       ,    axis=-1       ,    keepdims=_lowerCAmelCase    )
 __UpperCAmelCase     :   str					=   np.exp(_outputs - maxes    )
 return shifted_exp / shifted_exp.sum(axis=-1       ,    keepdims=_lowerCAmelCase    )






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Optional[Any]						   =       """sigmoid"""
    _A :			Optional[Any]						   =       """softmax"""
    _A :			Optional[int]						   =       """none"""






@add_end_docstrings(
    UpperCAmelCase_					,			R"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """					,			)
class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			str						   =       False
    _A :			Optional[int]						   =       ClassificationFunction.NONE



    def __init__(  self     ,			**__A					):
     super().__init__(**__A					)

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



    def 					__lowerCamelCase						(  self     ,			__A=None     ,			__A=None     ,			__A=""     ,			**__A					):
     # Using "" as default argument because we're going to use `top_k=None` in user code to declare
     # "No top_k"
     __UpperCAmelCase     :   Dict					=   tokenizer_kwargs

     __UpperCAmelCase     :   str					=   {}
     if hasattr(self.model.config     ,			'return_all_scores'					) and return_all_scores is None:
      __UpperCAmelCase     :   Union[str, Any]					=   self.model.config.return_all_scores

     if isinstance(__A     ,			__A					) or top_k is None:
      __UpperCAmelCase     :   List[Any]					=   top_k
      __UpperCAmelCase     :   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`.'     ,			__A     ,			)
      if return_all_scores:
       __UpperCAmelCase     :   List[Any]					=   None
      else:
       __UpperCAmelCase     :   int					=   1

     if isinstance(__A     ,			__A					):
      __UpperCAmelCase     :   Optional[int]					=   ClassificationFunction[function_to_apply.upper()]

     if function_to_apply is not None:
      __UpperCAmelCase     :   Dict					=   function_to_apply
     return preprocess_params, {}, postprocess_params



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



    def 					__lowerCamelCase						(  self     ,			__A     ,			**__A					):
     __UpperCAmelCase     :   Optional[Any]					=   self.framework
     if isinstance(__A     ,			__A					):
      return self.tokenizer(**__A     ,			return_tensors=__A     ,			**__A					)
     elif isinstance(__A     ,			__A					) and len(__A					) == 1 and isinstance(inputs[0]     ,			__A					) 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=__A     ,			**__A					)
     elif isinstance(__A     ,			__A					):
      # 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(__A     ,			return_tensors=__A     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     return self.model(**__A					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A=None     ,			__A=1     ,			__A=True					):
     # `_legacy` is used to determine if we're running the naked pipeline and in backward
     # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
     # the more natural result containing the list.
     # Default value before `set_parameters`
     if function_to_apply is None:
      if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
       __UpperCAmelCase     :   Optional[Any]					=   ClassificationFunction.SIGMOID
      elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
       __UpperCAmelCase     :   Union[str, Any]					=   ClassificationFunction.SOFTMAX
      elif hasattr(self.model.config     ,			'function_to_apply'					) and function_to_apply is None:
       __UpperCAmelCase     :   int					=   self.model.config.function_to_apply
      else:
       __UpperCAmelCase     :   Tuple					=   ClassificationFunction.NONE

     __UpperCAmelCase     :   List[str]					=   model_outputs['logits'][0]
     __UpperCAmelCase     :   List[Any]					=   outputs.numpy()

     if function_to_apply == ClassificationFunction.SIGMOID:
      __UpperCAmelCase     :   List[str]					=   sigmoid(__A					)
     elif function_to_apply == ClassificationFunction.SOFTMAX:
      __UpperCAmelCase     :   Any					=   softmax(__A					)
     elif function_to_apply == ClassificationFunction.NONE:
      __UpperCAmelCase     :   List[str]					=   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()}

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












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A:					Tuple							=				logging.get_logger(__name__)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[Any]						   =       """timm_backbone"""



    def __init__(  self     ,			__A=None     ,			__A=3     ,			__A=True     ,			__A=True     ,			__A=None     ,			**__A     ,			):
     super().__init__(**__A					)
     __UpperCAmelCase					=   backbone
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   features_only
     __UpperCAmelCase					=   use_pretrained_backbone
     __UpperCAmelCase					=   True
     __UpperCAmelCase					=   out_indices if out_indices is not None else (-1,)





