





'''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


lowercase__		:     Optional[Any]			=							logging.get_logger(__name__)
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			[label.strip() for label in labels.split(''','''	) if label.strip()]
            return labels







       def __call__(				self  :						List[Any]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            if len(lowerCAmelCase__	) == 0 or len(lowerCAmelCase__	) == 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(lowerCAmelCase__	)	)

            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			[sequences]

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

            return sequence_pairs, sequences

@add_end_docstrings(__magic_name__					)
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def __init__(				self  :						Any			,					lowerCAmelCase__  :						List[Any]=ZeroShotClassificationArgumentHandler()			,					*lowerCAmelCase__  :						Optional[int]			,					**lowerCAmelCase__  :						Optional[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			args_parser
            super().__init__(*lowerCAmelCase__			,					**lowerCAmelCase__	)
            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 snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            for label, ind in self.model.config.labelaid.items():
                 if label.lower().startswith('''entail'''	):
                      return ind
            return -1





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Union[str, Any]=True			,					lowerCAmelCase__  :						Any=True			,					lowerCAmelCase__  :						Optional[int]=TruncationStrategy.ONLY_FIRST			,					**lowerCAmelCase__  :						int	)       ->	Optional[Any]:

            '''simple docstring'''




            _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(
                     lowerCAmelCase__			,					add_special_tokens=lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					)
            except Exception as e:
                 if "too short" in str(lowerCAmelCase__	):
                      # 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(
                          lowerCAmelCase__			,					add_special_tokens=lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					padding=lowerCAmelCase__			,					truncation=TruncationStrategy.DO_NOT_TRUNCATE			,					)
                 else:
                      raise e

            return inputs





       def snake_case__		(				self  :						List[Any]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            if kwargs.get('''multi_class'''			,					lowerCAmelCase__	) 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  :						List[Any]			,					lowerCAmelCase__  :						Union[str, List[str]]			,					*lowerCAmelCase__  :						List[Any]			,					**lowerCAmelCase__  :						Dict			,					)       ->	Union[str, Any]:

            '''simple docstring'''




            if len(lowerCAmelCase__	) == 0:
                 pass
            elif len(lowerCAmelCase__	) == 1 and "candidate_labels" not in kwargs:
                 _UpperCamelCase							       =			args[0]
            else:
                 raise ValueError(f"""Unable to understand extra arguments {args}"""	)

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





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						str=None			,					lowerCAmelCase__  :						Optional[int]="This example is {}."	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self._args_parser(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

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

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





       def snake_case__		(				self  :						str			,					lowerCAmelCase__  :						List[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			inputs['''candidate_label''']
            _UpperCamelCase							       =			inputs['''sequence''']
            _UpperCamelCase							       =			{k: inputs[k] for k in self.tokenizer.model_input_names}
            _UpperCamelCase							       =			self.model(**lowerCAmelCase__	)

            _UpperCamelCase							       =			{
                '''candidate_label''': candidate_label,
                '''sequence''': sequence,
                '''is_last''': inputs['''is_last'''],
                **outputs,
            }
            return model_outputs







       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Tuple=False	)       ->	Optional[Any]:

            '''simple docstring'''




            _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(lowerCAmelCase__	)
            _UpperCamelCase							       =			N // n
            _UpperCamelCase							       =			logits.reshape((num_sequences, n, -1)	)

            if multi_label or len(lowerCAmelCase__	) == 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(lowerCAmelCase__	) / np.exp(lowerCAmelCase__	).sum(-1			,					keepdims=lowerCAmelCase__	)
                 _UpperCamelCase							       =			scores[..., 1]
            else:
                 # softmax the "entailment" logits over all candidate labels
                 _UpperCamelCase							       =			reshaped_outputs[..., self.entailment_id]
                 _UpperCamelCase							       =			np.exp(lowerCAmelCase__	) / np.exp(lowerCAmelCase__	).sum(-1			,					keepdims=lowerCAmelCase__	)

            _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(),
            }




import math
class     lowerCAmelCase_     :







							def __init__(   self      :     Tuple ,			_A      :     int=0		):  # a graph with Node 0,1,...,N-1
												_UpperCamelCase			     =     n
												_UpperCamelCase			     =     [
												    [math.inf for j in range(0 ,			_A		)] for i in range(0 ,			_A		)
												]  # adjacency matrix for weight
												_UpperCamelCase			     =     [
												    [math.inf for j in range(0 ,			_A		)] for i in range(0 ,			_A		)
												]  # dp[i][j] stores minimum distance from i to j







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     str ,			_A      :     List[str] ,			_A      :     Optional[Any]		):
												_UpperCamelCase			     =     w







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												for k in range(0 ,			self.n		):
																	for i in range(0 ,			self.n		):
																						for j in range(0 ,			self.n		):
																											_UpperCamelCase			     =     min(self.dp[i][j] ,			self.dp[i][k] + self.dp[k][j]		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[int] ,			_A      :     Optional[int]		):
												return self.dp[u][v]


if __name__ == "__main__":
					_lowerCAmelCase       =		Graph(5)
					graph.add_edge(0, 2, 9)
					graph.add_edge(0, 4, 10)
					graph.add_edge(1, 3, 5)
					graph.add_edge(2, 3, 7)
					graph.add_edge(3, 0, 10)
					graph.add_edge(3, 1, 2)
					graph.add_edge(3, 2, 1)
					graph.add_edge(3, 4, 6)
					graph.add_edge(4, 1, 3)
					graph.add_edge(4, 2, 4)
					graph.add_edge(4, 3, 9)
					graph.floyd_warshall()
					graph.show_min(1, 4)
					graph.show_min(0, 3)
