






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  (       snake_case__     :   Union[str, Any]  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   List[str]  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						np.max(_outputs	,axis=-1	,keepdims=snake_case__  )
     __snake_case			:Optional[int]          =						np.exp(_outputs - maxes  )
     return shifted_exp / shifted_exp.sum(axis=-1	,keepdims=snake_case__  )




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= "sigmoid"
       lowerCamelCase							:				Union[str, Any] 				= "softmax"
       lowerCamelCase							:				Any 				= "none"




@add_end_docstrings(
    lowercase_	,    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 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				int 				= False
       lowerCamelCase							:				List[Any] 				= ClassificationFunction.NONE



       def __init__(							self			,						**a__    ) ->      List[str]:






            '''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 					__lowercase						(							self			,						a__=None			,						a__=None			,						a__=""			,						**a__    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						tokenizer_kwargs

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

            if isinstance(a__			,						a__    ) or top_k is None:
                 __snake_case			:str          =						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`."""			,						a__			,						)
                 if return_all_scores:
                      __snake_case			:List[str]          =						None
                 else:
                      __snake_case			:Union[str, Any]          =						1

            if isinstance(a__			,						a__    ):
                 __snake_case			:Tuple          =						ClassificationFunction[function_to_apply.upper()]

            if function_to_apply is not None:
                 __snake_case			:List[Any]          =						function_to_apply
            return preprocess_params, {}, postprocess_params



       def __call__(							self			,						*a__			,						**a__    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Dict          =						super().__call__(*a__			,						**a__    )
            # TODO try and retrieve it in a nicer way from _sanitize_parameters.
            __snake_case			: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 					__lowercase						(							self			,						a__			,						**a__    ) ->      Dict[str, GenericTensor]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						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 					__lowercase						(							self			,						a__    ) ->      List[Any]:






            '''simple docstring'''





            return self.model(**a__    )



       def 					__lowercase						(							self			,						a__			,						a__=None			,						a__=1			,						a__=True    ) ->      Union[str, Any]:






            '''simple docstring'''





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

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

            if function_to_apply == ClassificationFunction.SIGMOID:
                 __snake_case			:int          =						sigmoid(a__    )
            elif function_to_apply == ClassificationFunction.SOFTMAX:
                 __snake_case			:Optional[int]          =						softmax(a__    )
            elif function_to_apply == ClassificationFunction.NONE:
                 __snake_case			: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()}

            __snake_case			: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:
                      __snake_case			:int          =						dict_scores[:top_k]
            return dict_scores










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__							=     {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """CanineForMultipleChoice""",
         """CanineForQuestionAnswering""",
         """CanineForSequenceClassification""",
         """CanineForTokenClassification""",
         """CanineLayer""",
         """CanineModel""",
         """CaninePreTrainedModel""",
         """load_tf_weights_in_canine""",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

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


