





"""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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple			):
 return 1.0 / (1.0 + np.exp(-_outputs			))

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]			):
 lowerCAmelCase    				= np.max(_outputs  ,     axis=-1  ,     keepdims=_UpperCAmelCase			)
 lowerCAmelCase    				= np.exp(_outputs - maxes			)
 return shifted_exp / shifted_exp.sum(axis=-1  ,     keepdims=_UpperCAmelCase			)







class       a							(   a__  ):
 snake_case__             =       '''sigmoid'''
 snake_case__             =       '''softmax'''
 snake_case__             =       '''none'''







@add_end_docstrings(
    a__   , 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							(   a__  ):
 snake_case__             =       False
 snake_case__             =       ClassificationFunction.NONE



 def __init__(     self				,       **_snake_case     ):







  """simple docstring"""






  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    UpperCamelCase__  (     self				,       _snake_case=None				,       _snake_case=None				,       _snake_case=""				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= tokenizer_kwargs

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

  if isinstance(_snake_case				,       _snake_case     ) or top_k is None:
   lowerCAmelCase    				= top_k
   lowerCAmelCase    				= 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:
    lowerCAmelCase    				= None
   else:
    lowerCAmelCase    				= 1

  if isinstance(_snake_case				,       _snake_case     ):
   lowerCAmelCase    				= ClassificationFunction[function_to_apply.upper()]

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



 def __call__(     self				,       *_snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= super().__call__(*_snake_case				,       **_snake_case     )
  # TODO try and retrieve it in a nicer way from _sanitize_parameters.
  lowerCAmelCase    				= '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    UpperCamelCase__  (     self				,       _snake_case				,       **_snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= 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    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.model(**_snake_case     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None				,       _snake_case=1				,       _snake_case=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:
    lowerCAmelCase    				= ClassificationFunction.SIGMOID
   elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
    lowerCAmelCase    				= ClassificationFunction.SOFTMAX
   elif hasattr(self.model.config				,       'function_to_apply'     ) and function_to_apply is None:
    lowerCAmelCase    				= self.model.config.function_to_apply
   else:
    lowerCAmelCase    				= ClassificationFunction.NONE

  lowerCAmelCase    				= model_outputs['logits'][0]
  lowerCAmelCase    				= outputs.numpy()

  if function_to_apply == ClassificationFunction.SIGMOID:
   lowerCAmelCase    				= sigmoid(_snake_case     )
  elif function_to_apply == ClassificationFunction.SOFTMAX:
   lowerCAmelCase    				= softmax(_snake_case     )
  elif function_to_apply == ClassificationFunction.NONE:
   lowerCAmelCase    				= 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()}

  lowerCAmelCase    				= [
      {'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:
    lowerCAmelCase    				= dict_scores[:top_k]
  return dict_scores







"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       str			)			->   bool:






    """simple docstring"""




    UpperCAmelCase          =       [int(lowerCAmelCase			) for i in ip_va_address.split('.'			) if i.isdigit()]
    return len(lowerCAmelCase			) == 4 and all(0 <= int(lowerCAmelCase			) <= 254 for octet in octets			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    input().strip()
   SCREAMING_SNAKE_CASE_            =    '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
   print(F'{ip} is a {valid_or_invalid} IP v4 address.')



