
'''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


UpperCamelCase_   	=				logging.get_logger(__name__)


class      a_     (a_			):




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         if isinstance(lowercase_      ,      lowercase_     ):
           _lowerCAmelCase				:     Optional[Any]       =    [label.strip() for label in labels.split(""","""     ) if label.strip()]
         return labels







       def __call__(   self      ,      snake_case_      ,      snake_case_      ,      snake_case_     ):
         if len(lowercase_     ) == 0 or len(lowercase_     ) == 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(lowercase_     )     )

         if isinstance(lowercase_      ,      lowercase_     ):
           _lowerCAmelCase				:     Optional[Any]       =    [sequences]

         _lowerCAmelCase				:     List[str]       =    []
         for sequence in sequences:
           sequence_pairs.extend([[sequence, hypothesis_template.format(lowercase_     )] for label in labels]     )

         return sequence_pairs, sequences



@add_end_docstrings(a_			)
class      a_     (a_			):




       def __init__(   self      ,      snake_case_=ZeroShotClassificationArgumentHandler()      ,      *snake_case_      ,      **snake_case_     ):
         _lowerCAmelCase				:     Optional[int]       =    args_parser
         super().__init__(*lowercase_      ,      **lowercase_     )
         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 							__UpperCamelCase				(   self     ):
         for label, ind in self.model.config.labelaid.items():
           if label.lower().startswith("""entail"""     ):
             return ind
         return -1




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_=True      ,      snake_case_=True      ,      snake_case_=TruncationStrategy.ONLY_FIRST      ,      **snake_case_     ):
         _lowerCAmelCase				:     Dict       =    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`"""     )
           _lowerCAmelCase				:     Any       =    self.tokenizer.eos_token
         try:
           _lowerCAmelCase				:     Optional[Any]       =    self.tokenizer(
               lowercase_      ,      add_special_tokens=lowercase_      ,      return_tensors=lowercase_      ,      padding=lowercase_      ,      truncation=lowercase_      ,      )
         except Exception as e:
           if "too short" in str(lowercase_     ):
             # 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.

             _lowerCAmelCase				:     Tuple       =    self.tokenizer(
                 lowercase_      ,      add_special_tokens=lowercase_      ,      return_tensors=lowercase_      ,      padding=lowercase_      ,      truncation=TruncationStrategy.DO_NOT_TRUNCATE      ,      )
           else:
             raise e

         return inputs




       def 							__UpperCamelCase				(   self      ,      **snake_case_     ):
         if kwargs.get("""multi_class"""      ,      lowercase_     ) is not None:
           _lowerCAmelCase				:     int       =    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."""     )
         _lowerCAmelCase				:     List[str]       =    {}
         if "candidate_labels" in kwargs:
           _lowerCAmelCase				:     int       =    self._args_parser._parse_labels(kwargs["""candidate_labels"""]     )
         if "hypothesis_template" in kwargs:
           _lowerCAmelCase				:     List[str]       =    kwargs['''hypothesis_template''']

         _lowerCAmelCase				:     Optional[int]       =    {}
         if "multi_label" in kwargs:
           _lowerCAmelCase				:     Dict       =    kwargs['''multi_label''']
         return preprocess_params, {}, postprocess_params




       def __call__(   self      ,      snake_case_      ,      *snake_case_      ,      **snake_case_      ,      ):
         if len(lowercase_     ) == 0:
           pass
         elif len(lowercase_     ) == 1 and "candidate_labels" not in kwargs:
           _lowerCAmelCase				:     Tuple       =    args[0]
         else:
           raise ValueError(f'Unable to understand extra arguments {args}'     )

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




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_=None      ,      snake_case_="This example is {}."     ):
         _lowerCAmelCase				:     Union[str, Any]       =    self._args_parser(lowercase_      ,      lowercase_      ,      lowercase_     )

         for i, (candidate_label, sequence_pair) in enumerate(zip(lowercase_      ,      lowercase_     )     ):
           _lowerCAmelCase				:     Union[str, Any]       =    self._parse_and_tokenize([sequence_pair]     )

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




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         _lowerCAmelCase				:     Optional[Any]       =    inputs['''candidate_label''']
         _lowerCAmelCase				:     str       =    inputs['''sequence''']
         _lowerCAmelCase				:     Tuple       =    {k: inputs[k] for k in self.tokenizer.model_input_names}
         _lowerCAmelCase				:     Optional[int]       =    self.model(**lowercase_     )

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







       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_=False     ):
         _lowerCAmelCase				:     Optional[int]       =    [outputs['''candidate_label'''] for outputs in model_outputs]
         _lowerCAmelCase				:     Optional[int]       =    [outputs['''sequence'''] for outputs in model_outputs]
         _lowerCAmelCase				:     Optional[Any]       =    np.concatenate([output["""logits"""].numpy() for output in model_outputs]     )
         _lowerCAmelCase				:     Dict       =    logits.shape[0]
         _lowerCAmelCase				:     Union[str, Any]       =    len(lowercase_     )
         _lowerCAmelCase				:     Optional[int]       =    N // n
         _lowerCAmelCase				:     Tuple       =    logits.reshape((num_sequences, n, -1)     )

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

         _lowerCAmelCase				:     str       =    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

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCamelCase  :      Tuple							      =		TypeVar('''T''')







class 				SCREAMING_SNAKE_CASE					(		Generic[T]       ):






					"""simple docstring"""



					lowercase__				 =       42  # Cache store of keys
					lowercase__				 =       42  # References of the keys in cache
					lowercase__				 =       10  # Maximum capacity of cache







					def __init__(       self			:				Dict   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	deque()
						lowerCAmelCase__	:						Any  				=	set()
						if not n:
							lowerCAmelCase__	:						Optional[Any]  				=	sys.maxsize
						elif n < 0:
							raise ValueError('''n should be an integer greater than 0.'''		)
						else:
							lowerCAmelCase__	:						int  				=	n







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				T		):
						if x not in self.key_reference:
							if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
								lowerCAmelCase__	:						Any  				=	self.dq_store.pop()
								self.key_reference.remove(lowercase_		)
						else:
							self.dq_store.remove(lowercase_		)

						self.dq_store.appendleft(lowercase_		)
						self.key_reference.add(lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						for k in self.dq_store:
							print(lowercase_		)







					def __repr__(       self			:				Tuple		):
						return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}'


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__UpperCamelCase  :      LRUCache[str | int]							      =		LRUCache(4)
							lru_cache.refer('''A''')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('''A''')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




