






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(
    _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 							__a							(  _UpperCamelCase						):





		'''simple docstring'''



		def   __snake_case			(						self ,    UpperCamelCase__       ):
						if self.framework == "tf":
										SCREAMING_SNAKE_CASE_   :	Any													=   tf.where(input_ids == self.tokenizer.mask_token_id       ).numpy()
						elif self.framework == "pt":
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.nonzero(input_ids == self.tokenizer.mask_token_id ,    as_tuple=_UpperCAmelCase       )
						else:
										raise ValueError('Unsupported framework'       )
						return masked_index


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.get_masked_index(_UpperCAmelCase       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   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   __snake_case			(						self ,    UpperCamelCase__       ):
						if isinstance(_UpperCAmelCase ,    _UpperCAmelCase       ):
										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(_UpperCAmelCase       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None ,    **UpperCamelCase__       ):
						if return_tensors is None:
										SCREAMING_SNAKE_CASE_   :	str													=   self.framework
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.tokenizer(_UpperCAmelCase ,    return_tensors=_UpperCAmelCase       )
						self.ensure_exactly_one_mask_token(_UpperCAmelCase       )
						return model_inputs


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.model(**_UpperCAmelCase       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model_inputs['''input_ids''']
						return model_outputs


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=5 ,    UpperCamelCase__=None       ):
						# Cap top_k if there are targets
						if target_ids is not None and target_ids.shape[0] < top_k:
										SCREAMING_SNAKE_CASE_   :	int													=   target_ids.shape[0]
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   model_outputs['''input_ids'''][0]
						SCREAMING_SNAKE_CASE_   :	int													=   model_outputs['''logits''']

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

										SCREAMING_SNAKE_CASE_   :	Tuple													=   outputs.numpy()

										SCREAMING_SNAKE_CASE_   :	Dict													=   outputs[0, masked_index, :]
										SCREAMING_SNAKE_CASE_   :	List[str]													=   stable_softmax(_UpperCAmelCase ,    axis=-1       )
										if target_ids is not None:
														SCREAMING_SNAKE_CASE_   :	Any													=   tf.gather_nd(tf.squeeze(_UpperCAmelCase ,    0       ) ,    target_ids.reshape(-1 ,    1       )       )
														SCREAMING_SNAKE_CASE_   :	List[str]													=   tf.expand_dims(_UpperCAmelCase ,    0       )

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

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

										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   probs.topk(_UpperCAmelCase       )

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

														SCREAMING_SNAKE_CASE_   :	Tuple													=   p
														# Filter padding out:
														SCREAMING_SNAKE_CASE_   :	Dict													=   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
														SCREAMING_SNAKE_CASE_   :	str													=   self.tokenizer.decode(_UpperCAmelCase ,    skip_special_tokens=_UpperCAmelCase       )
														SCREAMING_SNAKE_CASE_   :	Dict													=   {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]       ), '''sequence''': sequence}
														row.append(_UpperCAmelCase       )
										result.append(_UpperCAmelCase       )
						if single_mask:
										return result[0]
						return result


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):
						if isinstance(_UpperCAmelCase ,    _UpperCAmelCase       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   [targets]
						try:
										SCREAMING_SNAKE_CASE_   :	Dict													=   self.tokenizer.get_vocab()
						except Exception:
										SCREAMING_SNAKE_CASE_   :	int													=   {}
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   []
						for target in targets:
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   vocab.get(_UpperCAmelCase ,    _UpperCAmelCase       )
										if id_ is None:
														SCREAMING_SNAKE_CASE_   :	Any													=   self.tokenizer(
														    _UpperCAmelCase ,    add_special_tokens=_UpperCAmelCase ,    return_attention_mask=_UpperCAmelCase ,    return_token_type_ids=_UpperCAmelCase ,    max_length=1 ,    truncation=_UpperCAmelCase ,    )['''input_ids''']
														if len(_UpperCAmelCase       ) == 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
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   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_       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   list(set(_UpperCAmelCase       )       )
						if len(_UpperCAmelCase       ) == 0:
										raise ValueError('At least one target must be provided when passed.'       )
						SCREAMING_SNAKE_CASE_   :	str													=   np.array(_UpperCAmelCase       )
						return target_ids


		def   __snake_case			(						self ,    UpperCamelCase__=None ,    UpperCamelCase__=None       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {}

						if targets is not None:
										SCREAMING_SNAKE_CASE_   :	Any													=   self.get_target_ids(_UpperCAmelCase ,    _UpperCAmelCase       )
										SCREAMING_SNAKE_CASE_   :	int													=   target_ids

						if top_k is not None:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   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__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   super().__call__(_UpperCAmelCase ,    **_UpperCAmelCase       )
						if isinstance(_UpperCAmelCase ,    _UpperCAmelCase       ) and len(_UpperCAmelCase       ) == 1:
										return outputs[0]
						return outputs










import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Union[str, Path]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				bool				    =						True
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				int				    =						1
		UpperCAmelCase__      :				Optional[Union[str, bool]]				    =						None
		UpperCAmelCase__      :				bool				    =						False
		UpperCAmelCase__      :				Optional[Dict]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None


		def   __snake_case			(						self       ):
						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__       ) for k, v in self.__dict__.items()}       )