



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


_snake_case	=						logging.get_logger(__name__)



@add_end_docstrings(
    __UpperCAmelCase     ,      r'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '     ,      )
class 						UpperCAmelCase_					( __UpperCAmelCase):







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														if self.framework == "tf":
																					_lowerCAmelCase  :							Union[str, Any]				     =					tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
														elif self.framework == "pt":
																					_lowerCAmelCase  :							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      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					self.get_masked_index(__SCREAMING_SNAKE_CASE)
														_lowerCAmelCase  :							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,    __a):

														'''simple docstring'''

														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      snake_case__   (     self,    __a,    __a=None,    **__a):

														'''simple docstring'''

														if return_tensors is None:
																					_lowerCAmelCase  :							Optional[int]				     =					self.framework
														_lowerCAmelCase  :							Optional[int]				     =					self.tokenizer(__SCREAMING_SNAKE_CASE,    return_tensors=__SCREAMING_SNAKE_CASE)
														self.ensure_exactly_one_mask_token(__SCREAMING_SNAKE_CASE)
														return model_inputs







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a,    __a=5,    __a=None):

														'''simple docstring'''

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

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

																					_lowerCAmelCase  :							Tuple				     =					outputs.numpy()

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

																					_lowerCAmelCase  :							str				     =					tf.math.top_k(__SCREAMING_SNAKE_CASE,    k=__SCREAMING_SNAKE_CASE)
																					_lowerCAmelCase     ,					_lowerCAmelCase  :							Optional[int]				     =					topk.values.numpy(), topk.indices.numpy()
														else:
																					_lowerCAmelCase  :							List[str]				     =					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

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

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

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

																												_lowerCAmelCase  :							int				     =					p
																												# Filter padding out:
																												_lowerCAmelCase  :							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
																												_lowerCAmelCase  :							int				     =					self.tokenizer.decode(__SCREAMING_SNAKE_CASE,    skip_special_tokens=__SCREAMING_SNAKE_CASE)
																												_lowerCAmelCase  :							List[Any]				     =					{"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      snake_case__   (     self,    __a,    __a=None):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a=None,    __a=None):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					{}

														if targets is not None:
																					_lowerCAmelCase  :							int				     =					self.get_target_ids(__SCREAMING_SNAKE_CASE,    __SCREAMING_SNAKE_CASE)
																					_lowerCAmelCase  :							str				     =					target_ids

														if top_k is not None:
																					_lowerCAmelCase  :							List[Any]				     =					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):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'upernet'







							def __init__(     self,    __a=None,    __a=512,    __a=0.02,    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=384,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														if backbone_config is None:
																					logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
																					_lowerCAmelCase  :							List[str]				     =					CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
														elif isinstance(__a,    __a):
																					_lowerCAmelCase  :							List[Any]				     =					backbone_config.get("model_type")
																					_lowerCAmelCase  :							Dict				     =					CONFIG_MAPPING[backbone_model_type]
																					_lowerCAmelCase  :							Optional[Any]				     =					config_class.from_dict(__a)

														_lowerCAmelCase  :							Tuple				     =					backbone_config
														_lowerCAmelCase  :							List[Any]				     =					hidden_size
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							str				     =					pool_scales
														_lowerCAmelCase  :							List[str]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Dict				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							Tuple				     =					auxiliary_in_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_channels
														_lowerCAmelCase  :							str				     =					auxiliary_num_convs
														_lowerCAmelCase  :							Union[str, Any]				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					loss_ignore_index







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							List[Any]				     =					self.backbone_config.to_dict()
														_lowerCAmelCase  :							Optional[Any]				     =					self.__class__.model_type
														return output
