


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__							(   SCREAMING_SNAKE_CASE__)       ->   Tuple:
  return 1.0 / (1.0 + np.exp(-_outputs))







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:
  __snake_case:						Optional[int]			  =						np.max(_outputs , axis=-1 , keepdims=SCREAMING_SNAKE_CASE__)
  __snake_case:						List[str]			  =						np.exp(_outputs - maxes)
  return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=SCREAMING_SNAKE_CASE__)







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					"""sigmoid"""
       lowerCAmelCase__						=					"""softmax"""
       lowerCAmelCase__						=					"""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    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					False
       lowerCAmelCase__						=					ClassificationFunction.NONE




       def __init__(  self			:   str							,    **A			:   str		):
         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			:   Union[str, Any]							,    A			:   Any=None							,    A			:   Union[str, Any]=None							,    A			:   Union[str, Any]=""							,    **A			:   Optional[int]		):
         # Using "" as default argument because we're going to use `top_k=None` in user code to declare
         # "No top_k"
         __snake_case:						str			  =						tokenizer_kwargs

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

         if isinstance(A							,    A		) or top_k is None:
           __snake_case:						Tuple			  =						top_k
           __snake_case:						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:
             __snake_case:						Optional[int]			  =						None
           else:
             __snake_case:						int			  =						1

         if isinstance(A							,    A		):
           __snake_case:						Optional[int]			  =						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			:   Union[str, Any]							,    *A			:   str							,    **A			:   List[Any]		):
         __snake_case:						Any			  =						super().__call__(*A							,    **A		)
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         __snake_case:						int			  =						"""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			:   Optional[int]							,    A			:   Optional[int]							,    **A			:   Dict		):
         __snake_case:						Dict			  =						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			:   Optional[int]							,    A			:   Optional[Any]		):
         return self.model(**A		)






       def 							UpperCAmelCase__			(  self			:   int							,    A			:   Tuple							,    A			:   Tuple=None							,    A			:   Dict=1							,    A			:   Optional[Any]=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:
             __snake_case:						Dict			  =						ClassificationFunction.SIGMOID
           elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
             __snake_case:						List[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:						int			  =						ClassificationFunction.NONE

         __snake_case:						str			  =						model_outputs["""logits"""][0]
         __snake_case:						Optional[int]			  =						outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
           __snake_case:						int			  =						sigmoid(A		)
         elif function_to_apply == ClassificationFunction.SOFTMAX:
           __snake_case:						List[str]			  =						softmax(A		)
         elif function_to_apply == ClassificationFunction.NONE:
           __snake_case:						Dict			  =						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[Any]			  =						[
             {"""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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase	:       List[str]    	=							{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[Any]    	=							["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Tuple    	=							[
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       Optional[int]    	=							[
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase	:       List[Any]    	=							[
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase	:       str    	=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






