

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


_UpperCAmelCase						:  str									=			logging.get_logger(__name__)







class 					lowercase							(			lowercase_     ):




				def        a				(   self ,  snake_case			):



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




				def __call__(   self ,  snake_case ,  snake_case ,  snake_case			):



											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			):
																		snake_case_     							=       [sequences]

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

											return sequence_pairs, sequences







@add_end_docstrings(lowercase_     )
class 					lowercase							(			lowercase_     ):




				def __init__(   self ,  snake_case=ZeroShotClassificationArgumentHandler() ,  *snake_case ,  **snake_case			):
											snake_case_     							=       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        a				(   self			):



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




				def        a				(   self ,  snake_case ,  snake_case=True ,  snake_case=True ,  snake_case=TruncationStrategy.ONLY_FIRST ,  **snake_case			):
											snake_case_     							=       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`'			)
																		snake_case_     							=       self.tokenizer.eos_token
											try:
																		snake_case_     							=       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.

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

											return inputs




				def        a				(   self ,  **snake_case			):



											if kwargs.get('multi_class' ,  __lowerCAmelCase			) is not None:
																		snake_case_     							=       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.'			)
											snake_case_     							=       {}
											if "candidate_labels" in kwargs:
																		snake_case_     							=       self._args_parser._parse_labels(kwargs['candidate_labels']			)
											if "hypothesis_template" in kwargs:
																		snake_case_     							=       kwargs['hypothesis_template']

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




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



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

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




				def        a				(   self ,  snake_case ,  snake_case=None ,  snake_case="This example is {}."			):
											snake_case_							,       snake_case_     							=       self._args_parser(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase			)

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

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




				def        a				(   self ,  snake_case			):
											snake_case_     							=       inputs['candidate_label']
											snake_case_     							=       inputs['sequence']
											snake_case_     							=       {k: inputs[k] for k in self.tokenizer.model_input_names}
											snake_case_     							=       self.model(**__lowerCAmelCase			)

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




				def        a				(   self ,  snake_case ,  snake_case=False			):
											snake_case_     							=       [outputs['candidate_label'] for outputs in model_outputs]
											snake_case_     							=       [outputs['sequence'] for outputs in model_outputs]
											snake_case_     							=       np.concatenate([output['logits'].numpy() for output in model_outputs]			)
											snake_case_     							=       logits.shape[0]
											snake_case_     							=       len(__lowerCAmelCase			)
											snake_case_     							=       N // n
											snake_case_     							=       logits.reshape((num_sequences, n, -1)			)

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

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





"""simple docstring"""






from __future__ import annotations


def      UpperCAmelCase__			(							lowerCAmelCase__	:dict   ,						lowerCAmelCase__	:str						)					->       set[str]:
      '''simple docstring'''


      lowercase				,				lowercase								=      set(lowerCAmelCase__						), [start]

      while stack:
            lowercase								=      stack.pop()
            explored.add(lowerCAmelCase__						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(lowerCAmelCase__						)
      return explored


__lowerCAmelCase       :			str   	={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, """A"""))



