




from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
     import tensorflow as tf

     from ..tf_utils import stable_softmax


if is_torch_available():
     import torch


__a          =			logging.get_logger(__name__)

@add_end_docstrings(
    _UpperCamelCase    ,						R'''\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '''    ,						)
class   __a(					_UpperCamelCase       ):






    """simple docstring"""





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         if self.framework == "tf":
              UpperCAmelCase_      :							List[str]       =      tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
         elif self.framework == "pt":
              UpperCAmelCase_      :							Any       =      torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,as_tuple=_UpperCAmelCase  )
         else:
              raise ValueError('''Unsupported framework'''  )
         return masked_index





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         UpperCAmelCase_      :							int       =      self.get_masked_index(_UpperCAmelCase  )
         UpperCAmelCase_      :							List[str]       =      np.prod(masked_index.shape  )
         if numel < 1:
              raise PipelineException(
                  '''fill-mask'''       ,self.model.base_model_prefix       ,f'''No mask_token ({self.tokenizer.mask_token}) found on the input'''       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         if isinstance(_UpperCAmelCase       ,_UpperCAmelCase  ):
              for model_input in model_inputs:
                   self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]  )
         else:
              for input_ids in model_inputs["input_ids"]:
                   self._ensure_exactly_one_mask_token(_UpperCAmelCase  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None       ,**_SCREAMING_SNAKE_CASE  )			->		Any:
         if return_tensors is None:
              UpperCAmelCase_      :							Union[str, Any]       =      self.framework
         UpperCAmelCase_      :							Any       =      self.tokenizer(_UpperCAmelCase       ,return_tensors=_UpperCAmelCase  )
         self.ensure_exactly_one_mask_token(_UpperCAmelCase  )
         return model_inputs





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.model(**_UpperCAmelCase  )
         UpperCAmelCase_      :							Tuple       =      model_inputs['input_ids']
         return model_outputs





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=None  )			->		Union[str, Any]:
         # Cap top_k if there are targets
         if target_ids is not None and target_ids.shape[0] < top_k:
              UpperCAmelCase_      :							List[str]       =      target_ids.shape[0]
         UpperCAmelCase_      :							Dict       =      model_outputs['input_ids'][0]
         UpperCAmelCase_      :							Optional[int]       =      model_outputs['logits']

         if self.framework == "tf":
              UpperCAmelCase_      :							int       =      tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()[:, 0]

              UpperCAmelCase_      :							List[str]       =      outputs.numpy()

              UpperCAmelCase_      :							List[Any]       =      outputs[0, masked_index, :]
              UpperCAmelCase_      :							Optional[Any]       =      stable_softmax(_UpperCAmelCase       ,axis=-1  )
              if target_ids is not None:
                   UpperCAmelCase_      :							Optional[int]       =      tf.gather_nd(tf.squeeze(_UpperCAmelCase       ,0  )       ,target_ids.reshape(-1       ,1  )  )
                   UpperCAmelCase_      :							str       =      tf.expand_dims(_UpperCAmelCase       ,0  )

              UpperCAmelCase_      :							List[str]       =      tf.math.top_k(_UpperCAmelCase       ,k=_UpperCAmelCase  )
              UpperCAmelCase_      :							List[str]       =      topk.values.numpy(), topk.indices.numpy()
         else:
              UpperCAmelCase_      :							Union[str, Any]       =      torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,as_tuple=_UpperCAmelCase  ).squeeze(-1  )
              # Fill mask pipeline supports only one ${mask_token} per sample

              UpperCAmelCase_      :							List[str]       =      outputs[0, masked_index, :]
              UpperCAmelCase_      :							int       =      logits.softmax(dim=-1  )
              if target_ids is not None:
                   UpperCAmelCase_      :							List[str]       =      probs[..., target_ids]

              UpperCAmelCase_      :							Union[str, Any]       =      probs.topk(_UpperCAmelCase  )

         UpperCAmelCase_      :							str       =      []
         UpperCAmelCase_      :							Union[str, Any]       =      values.shape[0] == 1
         for i, (_values, _predictions) in enumerate(zip(values.tolist()       ,predictions.tolist()  )  ):
              UpperCAmelCase_      :							Tuple       =      []
              for v, p in zip(_values       ,_predictions  ):
                   # Copy is important since we're going to modify this array in place
                   UpperCAmelCase_      :							Optional[int]       =      input_ids.numpy().copy()
                   if target_ids is not None:
                        UpperCAmelCase_      :							Dict       =      target_ids[p].tolist()

                   UpperCAmelCase_      :							Optional[Any]       =      p
                   # Filter padding out:
                   UpperCAmelCase_      :							int       =      tokens[np.where(tokens != self.tokenizer.pad_token_id  )]
                   # Originally we skip special tokens to give readable output.
                   # For multi masks though, the other [MASK] would be removed otherwise
                   # making the output look odd, so we add them back
                   UpperCAmelCase_      :							int       =      self.tokenizer.decode(_UpperCAmelCase       ,skip_special_tokens=_UpperCAmelCase  )
                   UpperCAmelCase_      :							Union[str, Any]       =      {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]  ), 'sequence': sequence}
                   row.append(_UpperCAmelCase  )
              result.append(_UpperCAmelCase  )
         if single_mask:
              return result[0]
         return result





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		Optional[int]:
         if isinstance(_UpperCAmelCase       ,_UpperCAmelCase  ):
              UpperCAmelCase_      :							Dict       =      [targets]
         try:
              UpperCAmelCase_      :							str       =      self.tokenizer.get_vocab()
         except Exception:
              UpperCAmelCase_      :							Tuple       =      {}
         UpperCAmelCase_      :							Optional[int]       =      []
         for target in targets:
              UpperCAmelCase_      :							Tuple       =      vocab.get(_UpperCAmelCase       ,_UpperCAmelCase  )
              if id_ is None:
                   UpperCAmelCase_      :							List[Any]       =      self.tokenizer(
                       _UpperCAmelCase       ,add_special_tokens=_UpperCAmelCase       ,return_attention_mask=_UpperCAmelCase       ,return_token_type_ids=_UpperCAmelCase       ,max_length=1       ,truncation=_UpperCAmelCase       ,)['input_ids']
                   if len(_UpperCAmelCase  ) == 0:
                        logger.warning(
                            f'''The specified target token `{target}` does not exist in the model vocabulary. '''
                            '''We cannot replace it with anything meaningful, ignoring it'''  )
                        continue
                   UpperCAmelCase_      :							int       =      input_ids[0]
                   # XXX: If users encounter this pass
                   # it becomes pretty slow, so let's make sure
                   # The warning enables them to fix the input to
                   # get faster performance.
                   logger.warning(
                       f'''The specified target token `{target}` does not exist in the model vocabulary. '''
                       f'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_  )}`.'''  )
              target_ids.append(id_  )
         UpperCAmelCase_      :							Tuple       =      list(set(_UpperCAmelCase  )  )
         if len(_UpperCAmelCase  ) == 0:
              raise ValueError('''At least one target must be provided when passed.'''  )
         UpperCAmelCase_      :							Optional[int]       =      np.array(_UpperCAmelCase  )
         return target_ids





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None  )			->		str:
         UpperCAmelCase_      :							Dict       =      {}

         if targets is not None:
              UpperCAmelCase_      :							Dict       =      self.get_target_ids(_UpperCAmelCase       ,_UpperCAmelCase  )
              UpperCAmelCase_      :							Optional[int]       =      target_ids

         if top_k is not None:
              UpperCAmelCase_      :							Optional[Any]       =      top_k

         if self.tokenizer.mask_token_id is None:
              raise PipelineException(
                  '''fill-mask'''       ,self.model.base_model_prefix       ,'''The tokenizer does not define a `mask_token`.'''  )
         return {}, {}, postprocess_params





    def __call__(				self       ,_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      super().__call__(_UpperCAmelCase       ,**_UpperCAmelCase  )
         if isinstance(_UpperCAmelCase       ,_UpperCAmelCase  ) and len(_UpperCAmelCase  ) == 1:
              return outputs[0]
         return outputs





from collections import defaultdict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      first_str.lower().strip()
     UpperCAmelCase_      :							Any       =      second_str.lower().strip()

     # Remove whitespace
     UpperCAmelCase_      :							Any       =      first_str.replace(''' '''			,						''''''				)
     UpperCAmelCase_      :							int       =      second_str.replace(''' '''			,						''''''				)

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

     # Default values for count should be 0
     UpperCAmelCase_      :							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()
     __a          =			input('Enter the first string ').strip()
     __a          =			input('Enter the second string ').strip()

     __a          =			check_anagrams(input_a, input_b)
     print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")