




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


lowerCamelCase	:	Tuple				=   logging.get_logger(__name__)




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def      UpperCAmelCase				(				self    ,				A     )		->    Tuple:
         if isinstance(A    ,				A     ):
            snake_case				:    List[str]           =	[label.strip() for label in labels.split(""","""     ) if label.strip()]
         return labels

      def __call__(				self    ,				A    ,				A    ,				A     )		->    Optional[Any]:
         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     ):
            snake_case				:    Optional[int]           =	[sequences]

         snake_case				:    Tuple           =	[]
         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 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      def __init__(				self    ,				A=ZeroShotClassificationArgumentHandler()    ,				*A    ,				**A     )		->    int:
         snake_case				:    Optional[Any]           =	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      UpperCAmelCase				(				self     )		->    Any:
         for label, ind in self.model.config.labelaid.items():
            if label.lower().startswith("""entail"""     ):
               return ind
         return -1

      def      UpperCAmelCase				(				self    ,				A    ,				A=True    ,				A=True    ,				A=TruncationStrategy.ONLY_FIRST    ,				**A     )		->    str:
         snake_case				:    Optional[Any]           =	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				:    Tuple           =	self.tokenizer.eos_token
         try:
            snake_case				:    Any           =	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.

               snake_case				:    Optional[int]           =	self.tokenizer(
                   A    ,				add_special_tokens=A    ,				return_tensors=A    ,				padding=A    ,				truncation=TruncationStrategy.DO_NOT_TRUNCATE    ,				)
            else:
               raise e

         return inputs

      def      UpperCAmelCase				(				self    ,				**A     )		->    Any:
         if kwargs.get("""multi_class"""    ,				A     ) is not None:
            snake_case				:    str           =	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				:    Dict           =	{}
         if "candidate_labels" in kwargs:
            snake_case				:    Optional[Any]           =	self._args_parser._parse_labels(kwargs["""candidate_labels"""]     )
         if "hypothesis_template" in kwargs:
            snake_case				:    Tuple           =	kwargs["""hypothesis_template"""]

         snake_case				:    Optional[int]           =	{}
         if "multi_label" in kwargs:
            snake_case				:    List[str]           =	kwargs["""multi_label"""]
         return preprocess_params, {}, postprocess_params

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

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

      def      UpperCAmelCase				(				self    ,				A    ,				A=None    ,				A="This example is {}."     )		->    Optional[Any]:
         snake_case     , snake_case				:    List[Any]           =	self._args_parser(A    ,				A    ,				A     )

         for i, (candidate_label, sequence_pair) in enumerate(zip(A    ,				A     )     ):
            snake_case				:    int           =	self._parse_and_tokenize([sequence_pair]     )

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

      def      UpperCAmelCase				(				self    ,				A     )		->    Optional[Any]:
         snake_case				:    Optional[int]           =	inputs["""candidate_label"""]
         snake_case				:    List[str]           =	inputs["""sequence"""]
         snake_case				:    List[Any]           =	{k: inputs[k] for k in self.tokenizer.model_input_names}
         snake_case				:    List[str]           =	self.model(**A     )

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

      def      UpperCAmelCase				(				self    ,				A    ,				A=False     )		->    Optional[Any]:
         snake_case				:    Tuple           =	[outputs["""candidate_label"""] for outputs in model_outputs]
         snake_case				:    Optional[Any]           =	[outputs["""sequence"""] for outputs in model_outputs]
         snake_case				:    int           =	np.concatenate([output["""logits"""].numpy() for output in model_outputs]     )
         snake_case				:    Optional[int]           =	logits.shape[0]
         snake_case				:    Any           =	len(A     )
         snake_case				:    Tuple           =	N // n
         snake_case				:    Dict           =	logits.reshape((num_sequences, n, -1)     )

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

         snake_case				:    List[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(),
         }











from collections import defaultdict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       bool:
   snake_case				:    List[str]           =	first_str.lower().strip()
   snake_case				:    List[str]           =	second_str.lower().strip()

   # Remove whitespace
   snake_case				:    Any           =	first_str.replace(""" """						,""""""					)
   snake_case				:    List[str]           =	second_str.replace(""" """						,""""""					)

   # Strings of different lengths are not anagrams
   if len(lowercase					) != len(lowercase					):
      return False

   # Default values for count should be 0
   snake_case				:    defaultdict[str, int]           =	defaultdict(lowercase					)

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(lowercase					)					):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       lowerCamelCase	:	List[Any]				=   input('Enter the first string ').strip()
       lowerCamelCase	:	Optional[int]				=   input('Enter the second string ').strip()

       lowerCamelCase	:	Optional[Any]				=   check_anagrams(input_a, input_b)
       print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





