





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_    (					_lowerCAmelCase					)						->			List[str]:
 return 1.0 / (1.0 + np.exp(-_outputs					))



def 		A_    (					_lowerCAmelCase					)						->			Optional[Any]:
 UpperCamelCase     : List[Any]	     =       np.max(_outputs							,       axis=-1							,       keepdims=a_					)
 UpperCamelCase     : Tuple	     =       np.exp(_outputs - maxes					)
 return shifted_exp / shifted_exp.sum(axis=-1							,       keepdims=a_					)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :str						   =       '''sigmoid'''
      _UpperCAmelCase     :Optional[Any]						   =       '''softmax'''
      _UpperCAmelCase     :int						   =       '''none'''



@add_end_docstrings(
    __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  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict						   =       False
      _UpperCAmelCase     :int						   =       ClassificationFunction.NONE





      def __init__(			self	,							**A_       ):
       '''simple docstring'''




       super().__init__(**lowerCAmelCase__       )

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





      def 	__UpperCamelCase(			self	,							A_=None	,							A_=None	,							A_=""	,							**A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       tokenizer_kwargs

       UpperCamelCase     : Any	     =       {}
       if hasattr(self.model.config	,							"return_all_scores"       ) and return_all_scores is None:
        UpperCamelCase     : Any	     =       self.model.config.return_all_scores

       if isinstance(lowerCAmelCase__	,							lowerCAmelCase__       ) or top_k is None:
        UpperCamelCase     : int	     =       top_k
        UpperCamelCase     : 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`."	,							lowerCAmelCase__	,							)
        if return_all_scores:
         UpperCamelCase     : Dict	     =       None
        else:
         UpperCamelCase     : Dict	     =       1

       if isinstance(lowerCAmelCase__	,							lowerCAmelCase__       ):
        UpperCamelCase     : List[Any]	     =       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_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       super().__call__(*lowerCAmelCase__	,							**lowerCAmelCase__       )
       # 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]	,							lowerCAmelCase__       ) and _legacy:
        # This pipeline is odd, and return a list when single item is run
        return [result]
       else:
        return result





      def 	__UpperCamelCase(			self	,							A_	,							**A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       return self.model(**lowerCAmelCase__       )





      def 	__UpperCamelCase(			self	,							A_	,							A_=None	,							A_=1	,							A_=True       ):
       '''simple docstring'''




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

       UpperCamelCase     : Optional[int]	     =       model_outputs["logits"][0]
       UpperCamelCase     : Any	     =       outputs.numpy()

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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase	:					str			  =					{"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""EncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Optional[Any]			  =					["""TFEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Union[str, Any]			  =					["""FlaxEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __lowerCamelCase	:					Dict			  =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
