



"""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_  (			_lowercase	):







						'''simple docstring'''


						return 1.0 / (1.0 + np.exp(-_outputs	))
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			np.max(_outputs,					axis=-1,					keepdims=_lowercase	)
						snake_case_ :Optional[Any]						=			np.exp(_outputs - maxes	)
						return shifted_exp / shifted_exp.sum(axis=-1,					keepdims=_lowercase	)
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					_A		:			List[str]						=     """sigmoid"""
					_A		:			Union[str, Any]						=     """softmax"""
					_A		:			Tuple						=     """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				):






					'''simple docstring'''





					_A		:			Optional[int]						=     False
					_A		:			Any						=     ClassificationFunction.NONE






					def __init__(       self:							Tuple						,     **snake_case:							Any						)   ->	List[Any]:
											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    lowerCAmelCase_				(       self:							str						,     snake_case:							str=None						,     snake_case:							Any=None						,     snake_case:							Union[str, Any]=""						,     **snake_case:							List[str]						)   ->	Union[str, Any]:
											# Using "" as default argument because we're going to use `top_k=None` in user code to declare
											# "No top_k"
											snake_case_ :List[Any]						=			tokenizer_kwargs

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

											if isinstance(snake_case						,     snake_case						) or top_k is None:
																	snake_case_ :Optional[int]						=			top_k
																	snake_case_ :Optional[Any]						=			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:
																							snake_case_ :str						=			None
																	else:
																							snake_case_ :Tuple						=			1

											if isinstance(snake_case						,     snake_case						):
																	snake_case_ :List[Any]						=			ClassificationFunction[function_to_apply.upper()]

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






					def __call__(       self:							int						,     *snake_case:							List[str]						,     **snake_case:							List[Any]						)   ->	Union[str, Any]:
											snake_case_ :str						=			super().__call__(*snake_case						,     **snake_case						)
											# TODO try and retrieve it in a nicer way from _sanitize_parameters.
											snake_case_ :Any						=			"""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    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							str						,     **snake_case:							int						)   ->	Dict[str, GenericTensor]:
											snake_case_ :int						=			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    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							List[str]						)   ->	List[str]:
											return self.model(**snake_case						)






					def    lowerCAmelCase_				(       self:							Tuple						,     snake_case:							List[str]						,     snake_case:							Optional[int]=None						,     snake_case:							List[Any]=1						,     snake_case:							str=True						)   ->	int:
											# `_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:
																							snake_case_ :Optional[Any]						=			ClassificationFunction.SIGMOID
																	elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																							snake_case_ :Union[str, Any]						=			ClassificationFunction.SOFTMAX
																	elif hasattr(self.model.config						,     """function_to_apply"""						) and function_to_apply is None:
																							snake_case_ :int						=			self.model.config.function_to_apply
																	else:
																							snake_case_ :Tuple						=			ClassificationFunction.NONE

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

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

											snake_case_ :Union[str, Any]						=			[
											    {"""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:
																							snake_case_ :int						=			dict_scores[:top_k]
											return dict_scores




"""simple docstring"""



from typing import Dict

from .base import GenericTensor, Pipeline







class _lowerCAmelCase				(      snake_case_	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       if tokenize_kwargs is None:
           snake_case    :      Optional[Any]      					=    {}

       if truncation is not None:
           if "truncation" in tokenize_kwargs:
               raise ValueError(
                   "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"						)
           snake_case    :      List[str]      					=    truncation

       snake_case    :      Union[str, Any]      					=    tokenize_kwargs

       snake_case    :      List[Any]      					=    {}
       if return_tensors is not None:
           snake_case    :      Tuple      					=    return_tensors

       return preprocess_params, {}, postprocess_params







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	**UpperCamelCase__						)	->		Dict[str, GenericTensor]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.framework
       snake_case    :      str      					=    self.tokenizer(UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						)
       return model_inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      int      					=    self.model(**UpperCamelCase__						)
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=False						)	->		Union[str, Any]:

       '''simple docstring'''




       if return_tensors:
           return model_outputs[0]
       if self.framework == "pt":
           return model_outputs[0].tolist()
       elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







   def __call__(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       return super().__call__(*UpperCamelCase__						,	**UpperCamelCase__						)





