

"""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


_lowerCAmelCase         =							logging.get_logger(__name__)



@add_end_docstrings(
    _SCREAMING_SNAKE_CASE       ,       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  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		if self.framework == "tf":
			A_		:						str	    =   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=UpperCAmelCase__							)
		else:
			raise ValueError("""Unsupported framework"""							)
		return masked_index





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Optional[int]	    =   self.get_masked_index(UpperCAmelCase__							)
		A_		:						Union[str, 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    lowerCamelCase(self     ,     lowerCAmelCase_							):
		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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if return_tensors is None:
			A_		:						Optional[int]	    =   self.framework
		A_		:						Optional[Any]	    =   self.tokenizer(UpperCAmelCase__     ,     return_tensors=UpperCAmelCase__							)
		self.ensure_exactly_one_mask_token(UpperCAmelCase__							)
		return model_inputs





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Any	    =   self.model(**UpperCAmelCase__							)
		A_		:						Optional[int]	    =   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:
			A_		:						Optional[Any]	    =   target_ids.shape[0]
		A_		:						Dict	    =   model_outputs["""input_ids"""][0]
		A_		:						Optional[Any]	    =   model_outputs["""logits"""]

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

			A_		:						List[str]	    =   outputs.numpy()

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

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

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

			A_    ,     A_		:						Union[str, Any]	    =   probs.topk(UpperCAmelCase__							)

		A_		:						Any	    =   []
		A_		:						Tuple	    =   values.shape[0] == 1
		for i, (_values, _predictions) in enumerate(zip(values.tolist()     ,     predictions.tolist()							)							):
			A_		:						List[Any]	    =   []
			for v, p in zip(_values     ,     _predictions							):
				# Copy is important since we're going to modify this array in place
				A_		:						Union[str, Any]	    =   input_ids.numpy().copy()
				if target_ids is not None:
					A_		:						int	    =   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_		:						Dict	    =   self.tokenizer.decode(UpperCAmelCase__     ,     skip_special_tokens=UpperCAmelCase__							)
				A_		:						List[Any]	    =   {"""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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=None							):
		if isinstance(UpperCAmelCase__     ,     UpperCAmelCase__							):
			A_		:						Any	    =   [targets]
		try:
			A_		:						Any	    =   self.tokenizer.get_vocab()
		except Exception:
			A_		:						Any	    =   {}
		A_		:						List[Any]	    =   []
		for target in targets:
			A_		:						str	    =   vocab.get(UpperCAmelCase__     ,     UpperCAmelCase__							)
			if id_ is None:
				A_		:						Optional[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
				A_		:						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_							)
		A_		:						Optional[int]	    =   list(set(UpperCAmelCase__							)							)
		if len(UpperCAmelCase__							) == 0:
			raise ValueError("""At least one target must be provided when passed."""							)
		A_		:						Optional[int]	    =   np.array(UpperCAmelCase__							)
		return target_ids





	def    lowerCamelCase(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None							):
		A_		:						Optional[int]	    =   {}

		if targets is not None:
			A_		:						List[str]	    =   self.get_target_ids(UpperCAmelCase__     ,     UpperCAmelCase__							)
			A_		:						Tuple	    =   target_ids

		if top_k is not None:
			A_		:						Dict	    =   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_							):
		A_		:						Optional[Any]	    =   super().__call__(UpperCAmelCase__     ,     **UpperCAmelCase__							)
		if isinstance(UpperCAmelCase__     ,     UpperCAmelCase__							) and len(UpperCAmelCase__							) == 1:
			return outputs[0]
		return outputs









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =							{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase         =							[
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							_lowerCAmelCase         =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

