'''simple docstring'''





from typing import Any




class 	__A				:



    def __init__(self   :   Union[str, Any]       ,      __a   :   Any			):
         UpperCAmelCase_	   =       data
         UpperCAmelCase_	   =       None




class 	__A				:



    def __init__(self   :   Any			):
         UpperCAmelCase_	   =       None



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       self.head
         while temp is not None:
              print(temp.data       ,      end=" "			)
              UpperCAmelCase_	   =       temp.next
         print()



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Any			):
         UpperCAmelCase_	   =       Node(__a			)
         UpperCAmelCase_	   =       self.head
         UpperCAmelCase_	   =       new_node






    def  _lowercase						(self   :   List[str]       ,      __a   :   List[Any]       ,      __a   :   str			):
         if node_data_a == node_data_a:
              return
         else:
              UpperCAmelCase_	   =       self.head
              while node_a is not None and node_a.data != node_data_a:
                   UpperCAmelCase_	   =       node_a.next

              UpperCAmelCase_	   =       self.head
              while node_a is not None and node_a.data != node_data_a:
                   UpperCAmelCase_	   =       node_a.next

              if node_a is None or node_a is None:
                   return

              UpperCAmelCase_   ,			UpperCAmelCase_	   =       node_a.data, node_a.data


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					List[str]  =LinkedList()
    for i in range(5, 0, -1):
        ll.push(i)

    ll.print_list()

    ll.swap_nodes(1, 4)
    print('After swapping')
    ll.print_list()





'''simple docstring'''





from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   Dict       ,      __a   :   Any			):
         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       [label.strip() for label in labels.split(","			) if label.strip()]
         return labels






    def __call__(self   :   Union[str, Any]       ,      __a   :   Optional[int]       ,      __a   :   Optional[int]       ,      __a   :   int			):
         if len(__a			) == 0 or len(__a			) == 0:
              raise ValueError("You must include at least one label and at least one sequence."			)
         if hypothesis_template.format(labels[0]			) == hypothesis_template:
              raise ValueError(
                  (
                      "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
                      "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
                  ).format(__a			)			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       [sequences]

         UpperCAmelCase_	   =       []
         for sequence in sequences:
              sequence_pairs.extend([[sequence, hypothesis_template.format(__a			)] for label in labels]			)

         return sequence_pairs, sequences




@add_end_docstrings(UpperCamelCase__     )
class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Optional[Any]       ,      __a   :   Union[str, Any]=ZeroShotClassificationArgumentHandler()       ,      *__a   :   Optional[int]       ,      **__a   :   List[str]			):
         UpperCAmelCase_	   =       args_parser
         super().__init__(*__a       ,      **__a			)
         if self.entailment_id == -1:
              logger.warning(
                  "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
                  "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."			)



    @property
    def  _lowercase						(self   :   str			):
         for label, ind in self.model.config.labelaid.items():
              if label.lower().startswith("entail"			):
                   return ind
         return -1



    def  _lowercase						(self   :   Any       ,      __a   :   Any       ,      __a   :   int=True       ,      __a   :   Dict=True       ,      __a   :   Any=TruncationStrategy.ONLY_FIRST       ,      **__a   :   Tuple			):
         UpperCAmelCase_	   =       self.framework
         if self.tokenizer.pad_token is None:
              # Override for tokenizers not supporting padding
              logger.error(
                  "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
                  " `pad_token=eos_token`"			)
              UpperCAmelCase_	   =       self.tokenizer.eos_token
         try:
              UpperCAmelCase_	   =       self.tokenizer(
                  __a       ,      add_special_tokens=__a       ,      return_tensors=__a       ,      padding=__a       ,      truncation=__a       ,      )
         except Exception as e:
              if "too short" in str(__a			):
                   # tokenizers might yell that we want to truncate
                   # to a value that is not even reached by the input.
                   # In that case we don't want to truncate.
                   # It seems there's not a really better way to catch that
                   # exception.

                   UpperCAmelCase_	   =       self.tokenizer(
                       __a       ,      add_special_tokens=__a       ,      return_tensors=__a       ,      padding=__a       ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE       ,      )
              else:
                   raise e

         return inputs



    def  _lowercase						(self   :   List[str]       ,      **__a   :   Tuple			):
         if kwargs.get("multi_class"       ,      __a			) is not None:
              UpperCAmelCase_	   =       kwargs["multi_class"]
              logger.warning(
                  "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
                  "`multi_class` will be removed in a future version of Transformers."			)
         UpperCAmelCase_	   =       {}
         if "candidate_labels" in kwargs:
              UpperCAmelCase_	   =       self._args_parser._parse_labels(kwargs["candidate_labels"]			)
         if "hypothesis_template" in kwargs:
              UpperCAmelCase_	   =       kwargs["hypothesis_template"]

         UpperCAmelCase_	   =       {}
         if "multi_label" in kwargs:
              UpperCAmelCase_	   =       kwargs["multi_label"]
         return preprocess_params, {}, postprocess_params



    def __call__(self   :   Tuple       ,      __a   :   Union[str, List[str]]       ,      *__a   :   Optional[Any]       ,      **__a   :   Tuple       ,      ):
         if len(__a			) == 0:
              pass
         elif len(__a			) == 1 and "candidate_labels" not in kwargs:
              UpperCAmelCase_	   =       args[0]
         else:
              raise ValueError(f"""Unable to understand extra arguments {args}"""			)

         return super().__call__(__a       ,      **__a			)



    def  _lowercase						(self   :   Optional[int]       ,      __a   :   Optional[Any]       ,      __a   :   List[str]=None       ,      __a   :   Any="This example is {}."			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self._args_parser(__a       ,      __a       ,      __a			)

         for i, (candidate_label, sequence_pair) in enumerate(zip(__a       ,      __a			)			):
              UpperCAmelCase_	   =       self._parse_and_tokenize([sequence_pair]			)

              yield {
                  "candidate_label": candidate_label,
                  "sequence": sequences[0],
                  "is_last": i == len(__a			) - 1,
                  **model_input,
              }



    def  _lowercase						(self   :   List[str]       ,      __a   :   Any			):
         UpperCAmelCase_	   =       inputs["candidate_label"]
         UpperCAmelCase_	   =       inputs["sequence"]
         UpperCAmelCase_	   =       {k: inputs[k] for k in self.tokenizer.model_input_names}
         UpperCAmelCase_	   =       self.model(**__a			)

         UpperCAmelCase_	   =       {
             "candidate_label": candidate_label,
             "sequence": sequence,
             "is_last": inputs["is_last"],
             **outputs,
         }
         return model_outputs






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   List[str]       ,      __a   :   Tuple=False			):
         UpperCAmelCase_	   =       [outputs["candidate_label"] for outputs in model_outputs]
         UpperCAmelCase_	   =       [outputs["sequence"] for outputs in model_outputs]
         UpperCAmelCase_	   =       np.concatenate([output["logits"].numpy() for output in model_outputs]			)
         UpperCAmelCase_	   =       logits.shape[0]
         UpperCAmelCase_	   =       len(__a			)
         UpperCAmelCase_	   =       N // n
         UpperCAmelCase_	   =       logits.reshape((num_sequences, n, -1)			)

         if multi_label or len(__a			) == 1:
              # softmax over the entailment vs. contradiction dim for each label independently
              UpperCAmelCase_	   =       self.entailment_id
              UpperCAmelCase_	   =       -1 if entailment_id == 0 else 0
              UpperCAmelCase_	   =       reshaped_outputs[..., [contradiction_id, entailment_id]]
              UpperCAmelCase_	   =       np.exp(__a			) / np.exp(__a			).sum(-1       ,      keepdims=__a			)
              UpperCAmelCase_	   =       scores[..., 1]
         else:
              # softmax the "entailment" logits over all candidate labels
              UpperCAmelCase_	   =       reshaped_outputs[..., self.entailment_id]
              UpperCAmelCase_	   =       np.exp(__a			) / np.exp(__a			).sum(-1       ,      keepdims=__a			)

         UpperCAmelCase_	   =       list(reversed(scores[0].argsort()			)			)
         return {
             "sequence": sequences[0],
             "labels": [candidate_labels[i] for i in top_inds],
             "scores": scores[0, top_inds].tolist(),
         }




