



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


lowercase_													=      logging.get_logger(__name__)






@add_end_docstrings(
    SCREAMING_SNAKE_CASE						,     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			(  SCREAMING_SNAKE_CASE					):



	def 		UpperCamelCase				(    self				:     Optional[int]					,						snake_case_				:     GenericTensor)->				np.ndarray:
						if self.framework == "tf":
											__lowerCAmelCase						 =tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
						elif self.framework == "pt":
											__lowerCAmelCase						 =torch.nonzero(input_ids == self.tokenizer.mask_token_id					,						as_tuple=snake_case_)
						else:
											raise ValueError("""Unsupported framework""")
						return masked_index



	def 		UpperCamelCase				(    self				:     List[Any]					,						snake_case_				:     GenericTensor)->				np.ndarray:
						__lowerCAmelCase						 =self.get_masked_index(snake_case_)
						__lowerCAmelCase						 =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				:     int					,						snake_case_				:     GenericTensor)->				List[str]:
						if isinstance(snake_case_					,						snake_case_):
											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(snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Union[str, Any]					,						snake_case_				:     str=None					,						**snake_case_				:     Optional[int])->				Dict[str, GenericTensor]:
						if return_tensors is None:
											__lowerCAmelCase						 =self.framework
						__lowerCAmelCase						 =self.tokenizer(snake_case_					,						return_tensors=snake_case_)
						self.ensure_exactly_one_mask_token(snake_case_)
						return model_inputs



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     List[Any])->				Any:
						__lowerCAmelCase						 =self.model(**snake_case_)
						__lowerCAmelCase						 =model_inputs["""input_ids"""]
						return model_outputs



	def 		UpperCamelCase				(    self				:     Optional[int]					,						snake_case_				:     List[Any]					,						snake_case_				:     str=5					,						snake_case_				:     List[str]=None)->				Optional[int]:
						# Cap top_k if there are targets
						if target_ids is not None and target_ids.shape[0] < top_k:
											__lowerCAmelCase						 =target_ids.shape[0]
						__lowerCAmelCase						 =model_outputs["""input_ids"""][0]
						__lowerCAmelCase						 =model_outputs["""logits"""]

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

											__lowerCAmelCase						 =outputs.numpy()

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

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

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

											__lowerCAmelCase ,						__lowerCAmelCase						 =probs.topk(snake_case_)

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

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



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



	def 		UpperCamelCase				(    self				:     Union[str, Any]					,						snake_case_				:     str=None					,						snake_case_				:     List[Any]=None)->				Dict:
						__lowerCAmelCase						 ={}

						if targets is not None:
											__lowerCAmelCase						 =self.get_target_ids(snake_case_					,						snake_case_)
											__lowerCAmelCase						 =target_ids

						if top_k is not None:
											__lowerCAmelCase						 =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				:     List[Any]					,						snake_case_				:     List[str]					,						*snake_case_				:     Dict					,						**snake_case_				:     Any)->				Tuple:
						__lowerCAmelCase						 =super().__call__(snake_case_					,						**snake_case_)
						if isinstance(snake_case_					,						snake_case_) and len(snake_case_) == 1:
											return outputs[0]
						return outputs









def        __lowerCAmelCase		(	__lowerCamelCase						:     int						)	->    bool:
					if number < 0:
										raise ValueError("""number must not be negative"""						)
					return number & (number - 1) == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




