


'''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(
    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 		_snake_case					(     a__	):




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.framework == "tf":
											UpperCAmelCase__  :					List[Any]        =     tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
								elif self.framework == "pt":
											UpperCAmelCase__  :					Tuple        =     torch.nonzero(input_ids == self.tokenizer.mask_token_id							,							as_tuple=_lowerCamelCase)
								else:
											raise ValueError("""Unsupported framework""")
								return masked_index




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Union[str, Any]        =     self.get_masked_index(_lowerCamelCase)
								UpperCAmelCase__  :					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							,							_lowerCamelCase):
								if isinstance(_lowerCamelCase							,							_lowerCamelCase):
											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(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							**_lowerCamelCase):
								if return_tensors is None:
											UpperCAmelCase__  :					List[str]        =     self.framework
								UpperCAmelCase__  :					Optional[Any]        =     self.tokenizer(_lowerCamelCase							,							return_tensors=_lowerCamelCase)
								self.ensure_exactly_one_mask_token(_lowerCamelCase)
								return model_inputs




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     self.model(**_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     model_inputs["""input_ids"""]
								return model_outputs




					def 				snake_case__						(      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:
											UpperCAmelCase__  :					str        =     target_ids.shape[0]
								UpperCAmelCase__  :					List[str]        =     model_outputs["""input_ids"""][0]
								UpperCAmelCase__  :					str        =     model_outputs["""logits"""]

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

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

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

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

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

											UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     probs.topk(_lowerCamelCase)

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

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




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




					def 				snake_case__						(      self							,							_lowerCamelCase=None							,							_lowerCamelCase=None):
								UpperCAmelCase__  :					Optional[int]        =     {}

								if targets is not None:
											UpperCAmelCase__  :					Tuple        =     self.get_target_ids(_lowerCamelCase							,							_lowerCamelCase)
											UpperCAmelCase__  :					Any        =     target_ids

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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A	={
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'NezhaForNextSentencePrediction',
		    'NezhaForMaskedLM',
		    'NezhaForPreTraining',
		    'NezhaForMultipleChoice',
		    'NezhaForQuestionAnswering',
		    'NezhaForSequenceClassification',
		    'NezhaForTokenClassification',
		    'NezhaModel',
		    'NezhaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

		__A	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)