






'''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						=	logging.get_logger(__name__)
class UpperCAmelCase_					(    __A				):




						"""simple docstring"""




						def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Dict		)  ->  Optional[int]:


							'''simple docstring'''

							if isinstance(UpperCAmelCase     ,      UpperCAmelCase		):
								lowercase					:   List[Any]				  =[label.strip() for label in labels.split(''','''		) if label.strip()]
							return labels






						def __call__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Dict:


							'''simple docstring'''

							if len(UpperCAmelCase		) == 0 or len(UpperCAmelCase		) == 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(UpperCAmelCase		)		)

							if isinstance(UpperCAmelCase     ,      UpperCAmelCase		):
								lowercase					:   int				  =[sequences]

							lowercase					:   Tuple				  =[]
							for sequence in sequences:
								sequence_pairs.extend([[sequence, hypothesis_template.format(UpperCAmelCase		)] for label in labels]		)

							return sequence_pairs, sequences





@add_end_docstrings(__A				)
class UpperCAmelCase_					(    __A				):




						"""simple docstring"""




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


							'''simple docstring'''

							lowercase					:   Union[str, Any]				  =args_parser
							super().__init__(*UpperCAmelCase     ,      **UpperCAmelCase		)
							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			:		List[str]		)  ->  Union[str, Any]:


							'''simple docstring'''

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






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


							'''simple docstring'''

							lowercase					:   int				  =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`'''		)
								lowercase					:   Optional[int]				  =self.tokenizer.eos_token
							try:
								lowercase					:   int				  =self.tokenizer(
								    UpperCAmelCase     ,      add_special_tokens=UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      )
							except Exception as e:
								if "too short" in str(UpperCAmelCase		):
									# 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.

									lowercase					:   List[str]				  =self.tokenizer(
									    UpperCAmelCase     ,      add_special_tokens=UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE     ,      )
								else:
									raise e

							return inputs






						def 				A__      ( self			:		Union[str, Any]     ,      **UpperCAmelCase			:		Union[str, Any]		)  ->  List[str]:


							'''simple docstring'''

							if kwargs.get('''multi_class'''     ,      UpperCAmelCase		) is not None:
								lowercase					:   Dict				  =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.'''		)
							lowercase					:   str				  ={}
							if "candidate_labels" in kwargs:
								lowercase					:   List[Any]				  =self._args_parser._parse_labels(kwargs['''candidate_labels''']		)
							if "hypothesis_template" in kwargs:
								lowercase					:   str				  =kwargs['''hypothesis_template''']

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






						def __call__( self			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, List[str]]     ,      *UpperCAmelCase			:		Dict     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  Optional[int]:


							'''simple docstring'''

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

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






						def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		int=None     ,      UpperCAmelCase			:		Union[str, Any]="This example is {}."		)  ->  Optional[int]:


							'''simple docstring'''

							lowercase					:   Optional[int]				  =self._args_parser(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

							for i, (candidate_label, sequence_pair) in enumerate(zip(UpperCAmelCase     ,      UpperCAmelCase		)		):
								lowercase					:   Tuple				  =self._parse_and_tokenize([sequence_pair]		)

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






						def 				A__      ( self			:		int     ,      UpperCAmelCase			:		List[Any]		)  ->  Dict:


							'''simple docstring'''

							lowercase					:   Optional[int]				  =inputs['''candidate_label''']
							lowercase					:   Union[str, Any]				  =inputs['''sequence''']
							lowercase					:   Union[str, Any]				  ={k: inputs[k] for k in self.tokenizer.model_input_names}
							lowercase					:   List[str]				  =self.model(**UpperCAmelCase		)

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






						def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		str=False		)  ->  Union[str, Any]:


							'''simple docstring'''

							lowercase					:   int				  =[outputs['''candidate_label'''] for outputs in model_outputs]
							lowercase					:   int				  =[outputs['''sequence'''] for outputs in model_outputs]
							lowercase					:   Tuple				  =np.concatenate([output['''logits'''].numpy() for output in model_outputs]		)
							lowercase					:   Union[str, Any]				  =logits.shape[0]
							lowercase					:   Tuple				  =len(UpperCAmelCase		)
							lowercase					:   Optional[Any]				  =N // n
							lowercase					:   int				  =logits.reshape((num_sequences, n, -1)		)

							if multi_label or len(UpperCAmelCase		) == 1:
								# softmax over the entailment vs. contradiction dim for each label independently
								lowercase					:   Union[str, Any]				  =self.entailment_id
								lowercase					:   Tuple				  =-1 if entailment_id == 0 else 0
								lowercase					:   Optional[int]				  =reshaped_outputs[..., [contradiction_id, entailment_id]]
								lowercase					:   int				  =np.exp(UpperCAmelCase		) / np.exp(UpperCAmelCase		).sum(-1     ,      keepdims=UpperCAmelCase		)
								lowercase					:   Union[str, Any]				  =scores[..., 1]
							else:
								# softmax the "entailment" logits over all candidate labels
								lowercase					:   Union[str, Any]				  =reshaped_outputs[..., self.entailment_id]
								lowercase					:   List[str]				  =np.exp(UpperCAmelCase		) / np.exp(UpperCAmelCase		).sum(-1     ,      keepdims=UpperCAmelCase		)

							lowercase					:   List[Any]				  =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'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



