

'''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 	UpperCamelCase   (       lowercase_						:    List[str]	)		->	int:




  '''simple docstring'''



  return 1.0 / (1.0 + np.exp(-_outputs	))




def 	UpperCamelCase   (       lowercase_						:    Any	)		->	List[Any]:




  '''simple docstring'''



  lowercase									=np.max(_outputs     ,					axis=-1     ,					keepdims=lowercase_	)
  lowercase									=np.exp(_outputs - maxes	)
  return shifted_exp / shifted_exp.sum(axis=-1     ,					keepdims=lowercase_	)

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'sigmoid'
 UpperCamelCase__     =       'softmax'
 UpperCamelCase__     =       'none'

@add_end_docstrings(
    __SCREAMING_SNAKE_CASE ,				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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       False
 UpperCamelCase__     =       ClassificationFunction.NONE







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

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







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

   lowercase									={}
   if hasattr(self.model.config  , '''return_all_scores'''   ) and return_all_scores is None:
     lowercase									=self.model.config.return_all_scores

   if isinstance(snake_case_  , snake_case_   ) or top_k is None:
     lowercase									=top_k
     lowercase									=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`.'''  , snake_case_  , )
     if return_all_scores:
       lowercase									=None
     else:
       lowercase									=1

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

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







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







 def 						_A(       self  , snake_case_  , **snake_case_   ):
   lowercase									=self.framework
   if isinstance(snake_case_  , snake_case_   ):
     return self.tokenizer(**snake_case_  , return_tensors=snake_case_  , **snake_case_   )
   elif isinstance(snake_case_  , snake_case_   ) and len(snake_case_   ) == 1 and isinstance(inputs[0]  , 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=snake_case_  , **snake_case_   )
   elif isinstance(snake_case_  , 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(snake_case_  , return_tensors=snake_case_  , **snake_case_   )







 def 						_A(       self  , snake_case_   ):
   return self.model(**snake_case_   )







 def 						_A(       self  , snake_case_  , snake_case_=None  , snake_case_=1  , snake_case_=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:
       lowercase									=ClassificationFunction.SIGMOID
     elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
       lowercase									=ClassificationFunction.SOFTMAX
     elif hasattr(self.model.config  , '''function_to_apply'''   ) and function_to_apply is None:
       lowercase									=self.model.config.function_to_apply
     else:
       lowercase									=ClassificationFunction.NONE

   lowercase									=model_outputs['''logits'''][0]
   lowercase									=outputs.numpy()

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__						=				{
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__						=				[
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		lowerCamelCase__						=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



