





"""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       __snake_case    (   __A     )		->					Any:
   return 1.0 / (1.0 + np.exp(-_outputs     ))




def       __snake_case    (   __A     )		->					Optional[int]:
   lowercase     :      Dict	=							np.max(_outputs			,axis=-1			,keepdims=_UpperCamelCase     )
   lowercase     :      Dict	=							np.exp(_outputs - maxes     )
   return shifted_exp / shifted_exp.sum(axis=-1			,keepdims=_UpperCamelCase     )
class lowerCamelCase__	(     lowerCAmelCase__					):
   __UpperCAmelCase										=       """sigmoid"""
   __UpperCAmelCase										=       """softmax"""
   __UpperCAmelCase										=       """none"""




@add_end_docstrings(
    lowerCAmelCase__	,					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 lowerCamelCase__	(     lowerCAmelCase__					):
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       ClassificationFunction.NONE



   def __init__(							self   , **snake_case						)			->     Optional[int]:







      """simple docstring"""
      super().__init__(**__a						)

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



   def  _UpperCAmelCase						(							self   , snake_case=None   , snake_case=None   , snake_case=""   , **snake_case						)			->     Dict:







      """simple docstring"""
      # Using "" as default argument because we're going to use `top_k=None` in user code to declare
      # "No top_k"
      lowercase     :      Tuple	=							tokenizer_kwargs

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

      if isinstance(__a   , __a						) or top_k is None:
         lowercase     :      Optional[Any]	=							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`."""   , __a   , )
         if return_all_scores:
            lowercase     :      Union[str, Any]	=							None
         else:
            lowercase     :      Dict	=							1

      if isinstance(__a   , __a						):
         lowercase     :      Any	=							ClassificationFunction[function_to_apply.upper()]

      if function_to_apply is not None:
         lowercase     :      Any	=							function_to_apply
      return preprocess_params, {}, postprocess_params



   def __call__(							self   , *snake_case   , **snake_case						)			->     str:







      """simple docstring"""
      lowercase     :      Optional[Any]	=							super().__call__(*__a   , **__a						)
      # TODO try and retrieve it in a nicer way from _sanitize_parameters.
      lowercase     :      Tuple	=							"""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  _UpperCAmelCase						(							self   , snake_case   , **snake_case						)			->     Any:







      """simple docstring"""
      lowercase     :      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  _UpperCAmelCase						(							self   , snake_case						)			->     int:







      """simple docstring"""
      return self.model(**__a						)





   def  _UpperCAmelCase						(							self   , snake_case   , snake_case=None   , snake_case=1   , snake_case=True						)			->     Dict:







      """simple docstring"""
      # `_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:
            lowercase     :      Optional[int]	=							ClassificationFunction.SIGMOID
         elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
            lowercase     :      Tuple	=							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     :      Optional[int]	=							ClassificationFunction.NONE

      lowercase     :      List[str]	=							model_outputs["""logits"""][0]
      lowercase     :      str	=							outputs.numpy()

      if function_to_apply == ClassificationFunction.SIGMOID:
         lowercase     :      List[str]	=							sigmoid(__a						)
      elif function_to_apply == ClassificationFunction.SOFTMAX:
         lowercase     :      Optional[int]	=							softmax(__a						)
      elif function_to_apply == ClassificationFunction.NONE:
         lowercase     :      Optional[int]	=							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     :      List[Any]	=							[
          {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(__a						)
      ]
      if not _legacy:
         dict_scores.sort(key=lambda snake_case						: x["score"]   , reverse=__a						)
         if top_k is not None:
            lowercase     :      Tuple	=							dict_scores[:top_k]
      return dict_scores






"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


A	:  Optional[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  **__a      ):
										warnings.warn(
										    "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
										    " use YolosImageProcessor instead."       ,  __a       ,  )
										super().__init__(*__a       ,  **__a      )




