'''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 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      str:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]			)    ->      Dict:


     '''simple docstring'''




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




class        __A      ( UpperCamelCase__	):
    a__       :						List[str]          =  """sigmoid"""
    a__       :						Tuple          =  """softmax"""
    a__       :						Union[str, Any]          =  """none"""




@add_end_docstrings(
    UpperCamelCase__		,	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        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  False
    a__       :						List[str]          =  ClassificationFunction.NONE



    def __init__(self    :    Dict   ,			**__a    :    Any ):
         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    :    Dict   ,			__a    :    Dict=None   ,			__a    :    Any=None   ,			__a    :    int=""   ,			**__a    :    Dict ):
         # Using "" as default argument because we're going to use `top_k=None` in user code to declare
         # "No top_k"
         UpperCAmelCase_           =   tokenizer_kwargs

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

         if isinstance(__a   ,			__a ) or top_k is None:
              UpperCAmelCase_           =   top_k
              UpperCAmelCase_           =   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:
                   UpperCAmelCase_           =   None
              else:
                   UpperCAmelCase_           =   1

         if isinstance(__a   ,			__a ):
              UpperCAmelCase_           =   ClassificationFunction[function_to_apply.upper()]

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



    def __call__(self    :    Optional[int]   ,			*__a    :    List[Any]   ,			**__a    :    Tuple ):
         UpperCAmelCase_           =   super().__call__(*__a   ,			**__a )
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         UpperCAmelCase_           =   "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    :    Optional[Any]   ,			__a    :    Dict   ,			**__a    :    List[str] ):
         UpperCAmelCase_           =   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    :    Dict   ,			__a    :    Optional[Any] ):
         return self.model(**__a )






    def 					_lowercase						(self    :    Dict   ,			__a    :    int   ,			__a    :    int=None   ,			__a    :    List[str]=1   ,			__a    :    List[str]=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:
                   UpperCAmelCase_           =   ClassificationFunction.SIGMOID
              elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                   UpperCAmelCase_           =   ClassificationFunction.SOFTMAX
              elif hasattr(self.model.config   ,			"function_to_apply" ) and function_to_apply is None:
                   UpperCAmelCase_           =   self.model.config.function_to_apply
              else:
                   UpperCAmelCase_           =   ClassificationFunction.NONE

         UpperCAmelCase_           =   model_outputs["logits"][0]
         UpperCAmelCase_           =   outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
              UpperCAmelCase_           =   sigmoid(__a )
         elif function_to_apply == ClassificationFunction.SOFTMAX:
              UpperCAmelCase_           =   softmax(__a )
         elif function_to_apply == ClassificationFunction.NONE:
              UpperCAmelCase_           =   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_           =   [
             {"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:
                   UpperCAmelCase_           =   dict_scores[:top_k]
         return dict_scores





import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )	)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""" ,     type=__UpperCamelCase ,     default=1 ,     help="""Number of TPU cores to use (1 or 8)."""	)

  # positional
  parser.add_argument(
      """training_script""" ,     type=__UpperCamelCase ,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ) ,     )

  # rest from the training program
  parser.add_argument("""training_script_args""" ,     nargs=__UpperCamelCase	)

  return parser.parse_args()



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

  xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores	)


if __name__ == "__main__":
   main()
