





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


_lowerCAmelCase							:					Dict		    =		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     __magic_name__     (							A__							):





     """simple docstring"""



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]	,					snake_case				:GenericTensor     ):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:GenericTensor     ):





        '''simple docstring'''




        A_    : Optional[int]								=	self.get_masked_index(snake_case     )
        A_    : List[Any]								=	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   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]	,					snake_case				:GenericTensor     ):





        '''simple docstring'''




        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   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:Union[str, Any]	,					snake_case				:Optional[int]=None	,					**snake_case				:Dict     ):





        '''simple docstring'''




        if return_tensors is None:
           A_    : int								=	self.framework
        A_    : str								=	self.tokenizer(snake_case	,					return_tensors=snake_case     )
        self.ensure_exactly_one_mask_token(snake_case     )
        return model_inputs



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]	,					snake_case				:int     ):





        '''simple docstring'''




        A_    : Any								=	self.model(**snake_case     )
        A_    : List[str]								=	model_inputs["input_ids"]
        return model_outputs



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]	,					snake_case				:List[str]	,					snake_case				:Tuple=5	,					snake_case				:List[Any]=None     ):





        '''simple docstring'''




        if target_ids is not None and target_ids.shape[0] < top_k:
           A_    : Union[str, Any]								=	target_ids.shape[0]
        A_    : Dict								=	model_outputs["input_ids"][0]
        A_    : Union[str, Any]								=	model_outputs["logits"]

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

           A_    : Tuple								=	outputs.numpy()

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

           A_    : List[Any]								=	tf.math.top_k(snake_case	,					k=snake_case     )
           A_					,  A_    : int								=	topk.values.numpy(), topk.indices.numpy()
        else:
           A_    : List[Any]								=	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

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

           A_					,  A_    : Tuple								=	probs.topk(snake_case     )

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

              A_    : Dict								=	p
              # Filter padding out:
              A_    : List[Any]								=	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
              A_    : str								=	self.tokenizer.decode(snake_case	,					skip_special_tokens=snake_case     )
              A_    : List[Any]								=	{"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   SCREAMING_SNAKE_CASE						(							self				:int	,					snake_case				:Optional[int]	,					snake_case				:Dict=None     ):





        '''simple docstring'''




        if isinstance(snake_case	,					snake_case     ):
           A_    : Tuple								=	[targets]
        try:
           A_    : List[str]								=	self.tokenizer.get_vocab()
        except Exception:
           A_    : int								=	{}
        A_    : List[str]								=	[]
        for target in targets:
           A_    : Optional[Any]								=	vocab.get(snake_case	,					snake_case     )
           if id_ is None:
              A_    : Dict								=	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
              A_    : Optional[int]								=	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_     )
        A_    : Union[str, Any]								=	list(set(snake_case     )     )
        if len(snake_case     ) == 0:
           raise ValueError("At least one target must be provided when passed."     )
        A_    : List[Any]								=	np.array(snake_case     )
        return target_ids



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:int=None	,					snake_case				:Any=None     ):





        '''simple docstring'''




        A_    : Optional[int]								=	{}

        if targets is not None:
           A_    : Optional[Any]								=	self.get_target_ids(snake_case	,					snake_case     )
           A_    : List[str]								=	target_ids

        if top_k is not None:
           A_    : str								=	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				:Optional[Any]	,					*snake_case				:Optional[Any]	,					**snake_case				:Optional[Any]     ):





        '''simple docstring'''




        A_    : Dict								=	super().__call__(snake_case	,					**snake_case     )
        if isinstance(snake_case	,					snake_case     ) and len(snake_case     ) == 1:
           return outputs[0]
        return outputs








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A__	:			Optional[int]			   = {}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__	:			Optional[int]			   = ['GPTSw3Tokenizer']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpt_swa import GPTSwaTokenizer

else:
							import sys

							A__	:			Optional[int]			   = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




