
"""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


_a														=		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    _UpperCAmelCase(   lowerCamelCase     ):



		def 						UpperCAmelCase							( self    ,		__a)	->	np.ndarray:






							'''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=__a)
							else:
												raise ValueError('''Unsupported framework''')
							return masked_index



		def 						UpperCAmelCase							( self    ,		__a)	->	np.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_masked_index(__a)
							_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    ,		__a)	->	int:






							'''simple docstring'''
							if isinstance(__a    ,		__a):
												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(__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a=None    ,		**__a)	->	Dict[str, GenericTensor]:






							'''simple docstring'''
							if return_tensors is None:
												_UpperCamelCase										=  self.framework
							_UpperCamelCase										=  self.tokenizer(__a    ,		return_tensors=__a)
							self.ensure_exactly_one_mask_token(__a)
							return model_inputs



		def 						UpperCAmelCase							( self    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.model(**__a)
							_UpperCamelCase										=  model_inputs['''input_ids''']
							return model_outputs



		def 						UpperCAmelCase							( self    ,		__a    ,		__a=5    ,		__a=None)	->	Optional[int]:






							'''simple docstring'''
							# 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(__a    ,		axis=-1)
												if target_ids is not None:
																	_UpperCamelCase										=  tf.gather_nd(tf.squeeze(__a    ,		0)    ,		target_ids.reshape(-1    ,		1))
																	_UpperCamelCase										=  tf.expand_dims(__a    ,		0)

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

							_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(__a    ,		skip_special_tokens=__a)
																	_UpperCamelCase										=  {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]), '''sequence''': sequence}
																	row.append(__a)
												result.append(__a)
							if single_mask:
												return result[0]
							return result



		def 						UpperCAmelCase							( self    ,		__a    ,		__a=None)	->	Any:






							'''simple docstring'''
							if isinstance(__a    ,		__a):
												_UpperCamelCase										=  [targets]
							try:
												_UpperCamelCase										=  self.tokenizer.get_vocab()
							except Exception:
												_UpperCamelCase										=  {}
							_UpperCamelCase										=  []
							for target in targets:
												_UpperCamelCase										=  vocab.get(__a    ,		__a)
												if id_ is None:
																	_UpperCamelCase										=  self.tokenizer(
																	    __a    ,		add_special_tokens=__a    ,		return_attention_mask=__a    ,		return_token_type_ids=__a    ,		max_length=1    ,		truncation=__a    ,		)['''input_ids''']
																	if len(__a) == 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(__a))
							if len(__a) == 0:
												raise ValueError('''At least one target must be provided when passed.''')
							_UpperCamelCase										=  np.array(__a)
							return target_ids



		def 						UpperCAmelCase							( self    ,		__a=None    ,		__a=None)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  {}

							if targets is not None:
												_UpperCamelCase										=  self.get_target_ids(__a    ,		__a)
												_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    ,		__a    ,		*__a    ,		**__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  super().__call__(__a    ,		**__a)
							if isinstance(__a    ,		__a) and len(__a) == 1:
												return outputs[0]
							return outputs








"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






