

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_ :				int						 =    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 						__A    (				self					, A__    ):

						if self.framework == "tf":
							A__							:					Dict       =	tf.where(input_ids == self.tokenizer.mask_token_id    ).numpy()
						elif self.framework == "pt":
							A__							:					List[Any]       =	torch.nonzero(input_ids == self.tokenizer.mask_token_id					, as_tuple=__SCREAMING_SNAKE_CASE    )
						else:
							raise ValueError("""Unsupported framework"""    )
						return masked_index






					def 						__A    (				self					, A__    ):
						A__							:					Union[str, Any]       =	self.get_masked_index(__SCREAMING_SNAKE_CASE    )
						A__							:					Optional[int]       =	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 						__A    (				self					, A__    ):

						if isinstance(__SCREAMING_SNAKE_CASE					, __SCREAMING_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(__SCREAMING_SNAKE_CASE    )






					def 						__A    (				self					, A__					, A__=None					, **A__    ):

						if return_tensors is None:
							A__							:					List[Any]       =	self.framework
						A__							:					Union[str, Any]       =	self.tokenizer(__SCREAMING_SNAKE_CASE					, return_tensors=__SCREAMING_SNAKE_CASE    )
						self.ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE    )
						return model_inputs






					def 						__A    (				self					, A__    ):
						A__							:					Any       =	self.model(**__SCREAMING_SNAKE_CASE    )
						A__							:					List[Any]       =	model_inputs["""input_ids"""]
						return model_outputs






					def 						__A    (				self					, A__					, A__=5					, A__=None    ):

						if target_ids is not None and target_ids.shape[0] < top_k:
							A__							:					str       =	target_ids.shape[0]
						A__							:					int       =	model_outputs["""input_ids"""][0]
						A__							:					List[Any]       =	model_outputs["""logits"""]

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

							A__							:					List[Any]       =	outputs.numpy()

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

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

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

							A__     ,     A__							:					Dict       =	probs.topk(__SCREAMING_SNAKE_CASE    )

						A__							:					Any       =	[]
						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__							:					Any       =	input_ids.numpy().copy()
								if target_ids is not None:
									A__							:					str       =	target_ids[p].tolist()

								A__							:					Tuple       =	p
								# Filter padding out:
								A__							:					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
								A__							:					Union[str, Any]       =	self.tokenizer.decode(__SCREAMING_SNAKE_CASE					, skip_special_tokens=__SCREAMING_SNAKE_CASE    )
								A__							:					int       =	{"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]    ), """sequence""": sequence}
								row.append(__SCREAMING_SNAKE_CASE    )
							result.append(__SCREAMING_SNAKE_CASE    )
						if single_mask:
							return result[0]
						return result






					def 						__A    (				self					, A__					, A__=None    ):

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






					def 						__A    (				self					, A__=None					, A__=None    ):
						A__							:					Tuple       =	{}

						if targets is not None:
							A__							:					Optional[int]       =	self.get_target_ids(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE    )
							A__							:					Optional[int]       =	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					, A__					, *A__					, **A__    ):
						A__							:					Tuple       =	super().__call__(__SCREAMING_SNAKE_CASE					, **__SCREAMING_SNAKE_CASE    )
						if isinstance(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE    ) and len(__SCREAMING_SNAKE_CASE    ) == 1:
							return outputs[0]
						return outputs






import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


A_ :				Union[str, Any]						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def __init__(				self					, *A__					, **A__    ):
						warnings.warn(
						    """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
						    """ Please use PoolFormerImageProcessor instead."""					, A__					, )
						super().__init__(*A__					, **A__    )



