"""simple docstring"""







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


__SCREAMING_SNAKE_CASE    	=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        UpperCamelCase		(			UpperCamelCase__  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      np.ndarray:


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_	:				str													=	self.get_masked_index(lowercase_			)
        lowercase_	:				Union[str, Any]													=	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 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        if isinstance(lowercase_     ,lowercase_			):
             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(lowercase_			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=None     ,**__UpperCamelCase			)	->      Dict[str, GenericTensor]:


        '''simple docstring'''


        if return_tensors is None:
             lowercase_	:				List[str]													=	self.framework
        lowercase_	:				int													=	self.tokenizer(lowercase_     ,return_tensors=lowercase_			)
        self.ensure_exactly_one_mask_token(lowercase_			)
        return model_inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.model(**lowercase_			)
        lowercase_	:				List[Any]													=	model_inputs['input_ids']
        return model_outputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=5     ,__UpperCamelCase=None			)	->      Optional[Any]:


        '''simple docstring'''


        if target_ids is not None and target_ids.shape[0] < top_k:
             lowercase_	:				List[str]													=	target_ids.shape[0]
        lowercase_	:				List[Any]													=	model_outputs['input_ids'][0]
        lowercase_	:				Tuple													=	model_outputs['logits']

        if self.framework == "tf":
             lowercase_	:				Optional[Any]													=	tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()[:, 0]

             lowercase_	:				Any													=	outputs.numpy()

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

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

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

             lowercase_							,  lowercase_	:				Optional[int]													=	probs.topk(lowercase_			)

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

                  lowercase_	:				Tuple													=	p
                  # Filter padding out:
                  lowercase_	:				Any													=	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
                  lowercase_	:				List[str]													=	self.tokenizer.decode(lowercase_     ,skip_special_tokens=lowercase_			)
                  lowercase_	:				Union[str, Any]													=	{'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]			), 'sequence': sequence}
                  row.append(lowercase_			)
             result.append(lowercase_			)
        if single_mask:
             return result[0]
        return result
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=None			)	->      Optional[Any]:


        '''simple docstring'''


        if isinstance(lowercase_     ,lowercase_			):
             lowercase_	:				List[Any]													=	[targets]
        try:
             lowercase_	:				List[str]													=	self.tokenizer.get_vocab()
        except Exception:
             lowercase_	:				Optional[Any]													=	{}
        lowercase_	:				Any													=	[]
        for target in targets:
             lowercase_	:				List[str]													=	vocab.get(lowercase_     ,lowercase_			)
             if id_ is None:
                  lowercase_	:				Union[str, Any]													=	self.tokenizer(
                      lowercase_     ,add_special_tokens=lowercase_     ,return_attention_mask=lowercase_     ,return_token_type_ids=lowercase_     ,max_length=1     ,truncation=lowercase_     ,)['input_ids']
                  if len(lowercase_			) == 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
                  lowercase_	:				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_			)
        lowercase_	:				Tuple													=	list(set(lowercase_			)			)
        if len(lowercase_			) == 0:
             raise ValueError('At least one target must be provided when passed.'			)
        lowercase_	:				Optional[int]													=	np.array(lowercase_			)
        return target_ids
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None			)	->      int:


        '''simple docstring'''


        lowercase_	:				str													=	{}

        if targets is not None:
             lowercase_	:				List[Any]													=	self.get_target_ids(lowercase_     ,lowercase_			)
             lowercase_	:				List[str]													=	target_ids

        if top_k is not None:
             lowercase_	:				Tuple													=	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     ,__UpperCamelCase     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	super().__call__(lowercase_     ,**lowercase_			)
        if isinstance(lowercase_     ,lowercase_			) and len(lowercase_			) == 1:
             return outputs[0]
        return outputs








'''simple docstring'''

import functools





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    A__				=			len(_lowerCamelCase       )
    A__				=			len(_lowerCamelCase       )

    @functools.cache
    def min_distance(_lowerCamelCase					:					int						,       _lowerCamelCase					:					int       ) -> int:
        # if first word index is overflow - delete all from the second word
        if indexa >= len_worda:
            return len_worda - indexa
        # if second word index is overflow - delete all from the first word
        if indexa >= len_worda:
            return len_worda - indexa
        A__				=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
        return min(
            1 + min_distance(indexa + 1						,       _lowerCamelCase       )						,       1 + min_distance(_lowerCamelCase						,       indexa + 1       )						,       diff + min_distance(indexa + 1						,       indexa + 1       )						,       )

    return min_distance(0						,       0       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



