





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__     =	logging.get_logger(__name__)

@add_end_docstrings(
    _a				,						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   (       _a    ):






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
											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=__lowerCAmelCase					)
											else:
															raise ValueError("""Unsupported framework"""					)
											return masked_index






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
											UpperCamelCase__			       =     self.get_masked_index(__lowerCAmelCase					)
											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 			_lowerCamelCase						(  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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=None					,	**__lowerCAmelCase					):
											if return_tensors is None:
															UpperCamelCase__			       =     self.framework
											UpperCamelCase__			       =     self.tokenizer(__lowerCAmelCase					,	return_tensors=__lowerCAmelCase					)
											self.ensure_exactly_one_mask_token(__lowerCAmelCase					)
											return model_inputs






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
											UpperCamelCase__			       =     self.model(**__lowerCAmelCase					)
											UpperCamelCase__			       =     model_inputs["""input_ids"""]
											return model_outputs






							def 			_lowerCamelCase						(  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:
															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(__lowerCAmelCase					,	axis=-1					)
															if target_ids is not None:
																			UpperCamelCase__			       =     tf.gather_nd(tf.squeeze(__lowerCAmelCase					,	0					)					,	target_ids.reshape(-1					,	1					)					)
																			UpperCamelCase__			       =     tf.expand_dims(__lowerCAmelCase					,	0					)

															UpperCamelCase__			       =     tf.math.top_k(__lowerCAmelCase					,	k=__lowerCAmelCase					)
															UpperCamelCase__       ,       UpperCamelCase__			       =     topk.values.numpy(), topk.indices.numpy()
											else:
															UpperCamelCase__			       =     torch.nonzero(input_ids == self.tokenizer.mask_token_id					,	as_tuple=__lowerCAmelCase					).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(__lowerCAmelCase					)

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






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					):
											UpperCamelCase__			       =     {}

											if targets is not None:
															UpperCamelCase__			       =     self.get_target_ids(__lowerCAmelCase					,	__lowerCAmelCase					)
															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					,	__lowerCAmelCase					,	*__lowerCAmelCase					,	**__lowerCAmelCase					):
											UpperCamelCase__			       =     super().__call__(__lowerCAmelCase					,	**__lowerCAmelCase					)
											if isinstance(__lowerCAmelCase					,	__lowerCAmelCase					) and len(__lowerCAmelCase					) == 1:
															return outputs[0]
											return outputs











def       _UpperCamelCase       (a__				:dict    ):






							"""simple docstring"""





							UpperCamelCase__			       =     set()
							# To detect a back edge, keep track of vertices currently in the recursion stack
							UpperCamelCase__			       =     set()
							return any(
							    node not in visited and depth_first_search(a__				,     a__				,     a__				,     a__    )
							    for node in graph    )





def       _UpperCamelCase       (a__				:dict				,     a__				:int				,     a__				:set				,     a__				:set    ):






							"""simple docstring"""





							visited.add(a__    )
							rec_stk.add(a__    )

							for node in graph[vertex]:
														if node not in visited:
																					if depth_first_search(a__				,     a__				,     a__				,     a__    ):
																												return True
														elif node in rec_stk:
																					return True

    # The node needs to be removed from recursion stack before function ends
							rec_stk.remove(a__    )
							return False


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

					testmod()




