





'''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


__magic_name__   :						int    =			logging.get_logger(__name__)


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

    '''					,    )
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if self.framework == "tf":
         _snake_case     		=				tf.where(input_ids == self.tokenizer.mask_token_id	).numpy()
       elif self.framework == "pt":
         _snake_case     		=				torch.nonzero(input_ids == self.tokenizer.mask_token_id						,   as_tuple=lowerCamelCase	)
       else:
         raise ValueError("Unsupported framework"	)
       return masked_index

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.get_masked_index(lowerCamelCase	)
       _snake_case     		=				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						,   lowerCamelCase	):
       if isinstance(lowerCamelCase						,   lowerCamelCase	):
         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(lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None						,   **lowerCamelCase	):
       if return_tensors is None:
         _snake_case     		=				self.framework
       _snake_case     		=				self.tokenizer(lowerCamelCase						,   return_tensors=lowerCamelCase	)
       self.ensure_exactly_one_mask_token(lowerCamelCase	)
       return model_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.model(**lowerCamelCase	)
       _snake_case     		=				model_inputs["input_ids"]
       return model_outputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=5						,   lowerCamelCase=None	):
       # Cap top_k if there are targets
       if target_ids is not None and target_ids.shape[0] < top_k:
         _snake_case     		=				target_ids.shape[0]
       _snake_case     		=				model_outputs["input_ids"][0]
       _snake_case     		=				model_outputs["logits"]

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

         _snake_case     		=				outputs.numpy()

         _snake_case     		=				outputs[0, masked_index, :]
         _snake_case     		=				stable_softmax(lowerCamelCase						,   axis=-1	)
         if target_ids is not None:
           _snake_case     		=				tf.gather_nd(tf.squeeze(lowerCamelCase						,   0	)						,   target_ids.reshape(-1						,   1	)	)
           _snake_case     		=				tf.expand_dims(lowerCamelCase						,   0	)

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

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

         _snake_case		,		_snake_case     		=				probs.topk(lowerCamelCase	)

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

           _snake_case     		=				p
           # Filter padding out:
           _snake_case     		=				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
           _snake_case     		=				self.tokenizer.decode(lowerCamelCase						,   skip_special_tokens=lowerCamelCase	)
           _snake_case     		=				{"score": v, "token": p, "token_str": self.tokenizer.decode([p]	), "sequence": sequence}
           row.append(lowerCamelCase	)
         result.append(lowerCamelCase	)
       if single_mask:
         return result[0]
       return result

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       if isinstance(lowerCamelCase						,   lowerCamelCase	):
         _snake_case     		=				[targets]
       try:
         _snake_case     		=				self.tokenizer.get_vocab()
       except Exception:
         _snake_case     		=				{}
       _snake_case     		=				[]
       for target in targets:
         _snake_case     		=				vocab.get(lowerCamelCase						,   lowerCamelCase	)
         if id_ is None:
           _snake_case     		=				self.tokenizer(
               lowerCamelCase						,   add_special_tokens=lowerCamelCase						,   return_attention_mask=lowerCamelCase						,   return_token_type_ids=lowerCamelCase						,   max_length=1						,   truncation=lowerCamelCase						,   )["input_ids"]
           if len(lowerCamelCase	) == 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
           _snake_case     		=				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_	)
       _snake_case     		=				list(set(lowerCamelCase	)	)
       if len(lowerCamelCase	) == 0:
         raise ValueError("At least one target must be provided when passed."	)
       _snake_case     		=				np.array(lowerCamelCase	)
       return target_ids

     def 	UpperCamelCase(					self						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       _snake_case     		=				{}

       if targets is not None:
         _snake_case     		=				self.get_target_ids(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				target_ids

       if top_k is not None:
         _snake_case     		=				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						,   lowerCamelCase						,   *lowerCamelCase						,   **lowerCamelCase	):
       _snake_case     		=				super().__call__(lowerCamelCase						,   **lowerCamelCase	)
       if isinstance(lowerCamelCase						,   lowerCamelCase	) and len(lowerCamelCase	) == 1:
         return outputs[0]
       return outputs









'''simple docstring'''



def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[0 for i in range(len(SCREAMING_SNAKE_CASE__       )       )]

  # initialize interval's left pointer and right pointer
  _snake_case		,		_snake_case     		=				0, 0

  for i in range(1					,	len(SCREAMING_SNAKE_CASE__       )       ):
    # case when current index is inside the interval
    if i <= right_pointer:
      _snake_case     		=				min(right_pointer - i + 1					,	z_result[i - left_pointer]       )
      _snake_case     		=				min_edge

    while go_next(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      _snake_case		,		_snake_case     		=				i, i + z_result[i] - 1

  return z_result




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return i + z_result[i] < len(SCREAMING_SNAKE_CASE__       ) and s[z_result[i]] == s[i + z_result[i]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  _snake_case     		=				z_function(pattern + input_str       )

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(SCREAMING_SNAKE_CASE__       ):
      answer += 1

  return answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()


