
"""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


UpperCamelCase     :    Optional[int]       =			logging.get_logger(__name__)

@add_end_docstrings(
    _lowerCAmelCase      ,  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 __lowerCAmelCase		(				_lowerCAmelCase      ):





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      if self.framework == "tf":
       __UpperCamelCase      =	tf.where(input_ids == self.tokenizer.mask_token_id				).numpy()
      elif self.framework == "pt":
       __UpperCamelCase      =	torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,					as_tuple=_lowercase				)
      else:
       raise ValueError('Unsupported framework'				)
      return masked_index





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.get_masked_index(_lowercase				)
      __UpperCamelCase      =	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				):

      '''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				):

      '''simple docstring'''




      if return_tensors is None:
       __UpperCamelCase      =	self.framework
      __UpperCamelCase      =	self.tokenizer(_lowercase     ,					return_tensors=_lowercase				)
      self.ensure_exactly_one_mask_token(_lowercase				)
      return model_inputs





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model(**_lowercase				)
      __UpperCamelCase      =	model_inputs['input_ids']
      return model_outputs





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=5     ,					__UpperCAmelCase=None				):

      '''simple docstring'''




      if target_ids is not None and target_ids.shape[0] < top_k:
       __UpperCamelCase      =	target_ids.shape[0]
      __UpperCamelCase      =	model_outputs['input_ids'][0]
      __UpperCamelCase      =	model_outputs['logits']

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

       __UpperCamelCase      =	outputs.numpy()

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

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

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

       __UpperCamelCase  ,     __UpperCamelCase      =	probs.topk(_lowercase				)

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

        __UpperCamelCase      =	p
        # Filter padding out:
        __UpperCamelCase      =	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      =	self.tokenizer.decode(_lowercase     ,					skip_special_tokens=_lowercase				)
        __UpperCamelCase      =	{'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				):

      '''simple docstring'''




      if isinstance(_lowercase     ,					_lowercase				):
       __UpperCamelCase      =	[targets]
      try:
       __UpperCamelCase      =	self.tokenizer.get_vocab()
      except Exception:
       __UpperCamelCase      =	{}
      __UpperCamelCase      =	[]
      for target in targets:
       __UpperCamelCase      =	vocab.get(_lowercase     ,					_lowercase				)
       if id_ is None:
        __UpperCamelCase      =	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
        __UpperCamelCase      =	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      =	list(set(_lowercase				)				)
      if len(_lowercase				) == 0:
       raise ValueError('At least one target must be provided when passed.'				)
      __UpperCamelCase      =	np.array(_lowercase				)
      return target_ids





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None				):

      '''simple docstring'''




      __UpperCamelCase      =	{}

      if targets is not None:
       __UpperCamelCase      =	self.get_target_ids(_lowercase     ,					_lowercase				)
       __UpperCamelCase      =	target_ids

      if top_k is not None:
       __UpperCamelCase      =	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				):

      '''simple docstring'''




      __UpperCamelCase      =	super().__call__(_lowercase     ,					**_lowercase				)
      if isinstance(_lowercase     ,					_lowercase				) and len(_lowercase				) == 1:
       return outputs[0]
      return outputs







"""simple docstring"""

def 							A						(    snake_case				:list[list[int]]    , snake_case				:int    , snake_case				:int    , snake_case				:list[int]		)				->     bool:

 # 1. Validate that path exists between current and next vertices
 if graph[path[curr_ind - 1]][next_ver] == 0:
  return False

 # 2. Validate that next vertex is not already in path
 return not any(vertex == next_ver for vertex in path		)






def 							A						(    snake_case				:list[list[int]]    , snake_case				:list[int]    , snake_case				:int		)				->     bool:

 # Base Case
 if curr_ind == len(snake_case		):
  # return whether path exists between current and starting vertices
  return graph[path[curr_ind - 1]][path[0]] == 1

 # Recursive Step
 for next_ver in range(0    , len(snake_case		)		):
  if valid_connection(snake_case    , snake_case    , snake_case    , snake_case		):
   # Insert current vertex  into path as next transition
   __UpperCamelCase      =	next_ver
   # Validate created path
   if util_hamilton_cycle(snake_case    , snake_case    , curr_ind + 1		):
    return True
   # Backtrack
   __UpperCamelCase      =	-1
 return False






def 							A						(    snake_case				:list[list[int]]    , snake_case				:int = 0		)				->     list[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		) + 1)
 # initialize start and end of path with starting index
 __UpperCamelCase      =	__UpperCamelCase      =	start_index
 # evaluate and if we find answer return path either return empty array
 return path if util_hamilton_cycle(snake_case    , snake_case    , 1		) else []



