



"""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


lowercase  							=					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 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self	,       snake_case						)			->							np.ndarray:
									if self.framework == "tf":
											_UpperCAmelCase							 =		tf.where(input_ids == self.tokenizer.mask_token_id						).numpy()
									elif self.framework == "pt":
											_UpperCAmelCase							 =		torch.nonzero(input_ids == self.tokenizer.mask_token_id	,       as_tuple=snake_case						)
									else:
											raise ValueError('Unsupported framework'						)
									return masked_index






							def        lowerCamelCase_   (  self	,       snake_case						)			->							np.ndarray:
									_UpperCAmelCase							 =		self.get_masked_index(snake_case						)
									_UpperCAmelCase							 =		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	,       snake_case						)			->							List[str]:
									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        lowerCamelCase_   (  self	,       snake_case	,       snake_case=None	,       **snake_case						)			->							Dict[str, GenericTensor]:
									if return_tensors is None:
											_UpperCAmelCase							 =		self.framework
									_UpperCAmelCase							 =		self.tokenizer(snake_case	,       return_tensors=snake_case						)
									self.ensure_exactly_one_mask_token(snake_case						)
									return model_inputs






							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									_UpperCAmelCase							 =		self.model(**snake_case						)
									_UpperCAmelCase							 =		model_inputs['input_ids']
									return model_outputs






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=5	,       snake_case=None						)			->							Optional[Any]:
									# Cap top_k if there are targets
									if target_ids is not None and target_ids.shape[0] < top_k:
											_UpperCAmelCase							 =		target_ids.shape[0]
									_UpperCAmelCase							 =		model_outputs['input_ids'][0]
									_UpperCAmelCase							 =		model_outputs['logits']

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

											_UpperCAmelCase							 =		outputs.numpy()

											_UpperCAmelCase							 =		outputs[0, masked_index, :]
											_UpperCAmelCase							 =		stable_softmax(snake_case	,       axis=-1						)
											if target_ids is not None:
													_UpperCAmelCase							 =		tf.gather_nd(tf.squeeze(snake_case	,       0						)	,       target_ids.reshape(-1	,       1						)						)
													_UpperCAmelCase							 =		tf.expand_dims(snake_case	,       0						)

											_UpperCAmelCase							 =		tf.math.top_k(snake_case	,       k=snake_case						)
											_UpperCAmelCase ,					_UpperCAmelCase							 =		topk.values.numpy(), topk.indices.numpy()
									else:
											_UpperCAmelCase							 =		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

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

											_UpperCAmelCase ,					_UpperCAmelCase							 =		probs.topk(snake_case						)

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

													_UpperCAmelCase							 =		p
													# Filter padding out:
													_UpperCAmelCase							 =		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							 =		self.tokenizer.decode(snake_case	,       skip_special_tokens=snake_case						)
													_UpperCAmelCase							 =		{'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        lowerCamelCase_   (  self	,       snake_case	,       snake_case=None						)			->							Dict:
									if isinstance(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		[targets]
									try:
											_UpperCAmelCase							 =		self.tokenizer.get_vocab()
									except Exception:
											_UpperCAmelCase							 =		{}
									_UpperCAmelCase							 =		[]
									for target in targets:
											_UpperCAmelCase							 =		vocab.get(snake_case	,       snake_case						)
											if id_ is None:
													_UpperCAmelCase							 =		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
													_UpperCAmelCase							 =		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							 =		list(set(snake_case						)						)
									if len(snake_case						) == 0:
											raise ValueError('At least one target must be provided when passed.'						)
									_UpperCAmelCase							 =		np.array(snake_case						)
									return target_ids






							def        lowerCamelCase_   (  self	,       snake_case=None	,       snake_case=None						)			->							Optional[int]:
									_UpperCAmelCase							 =		{}

									if targets is not None:
											_UpperCAmelCase							 =		self.get_target_ids(snake_case	,       snake_case						)
											_UpperCAmelCase							 =		target_ids

									if top_k is not None:
											_UpperCAmelCase							 =		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	,       snake_case	,       *snake_case	,       **snake_case						)			->							int:
									_UpperCAmelCase							 =		super().__call__(snake_case	,       **snake_case						)
									if isinstance(snake_case	,       snake_case						) and len(snake_case						) == 1:
											return outputs[0]
									return outputs







"""simple docstring"""





def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		abs(A       )
		_UpperCAmelCase							 =		0
		while n > 0:
				res += n % 10
				n //= 10
		return res




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		abs(A       )
		return n if n < 10 else n % 10 + sum_of_digits(n // 10       )




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		return sum(int(A       ) for c in str(abs(A       )       )       )




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		from collections.abc import Callable
		from timeit import timeit

		def benchmark_a_function(A	:						Callable     ,				A	:						int       ) -> None:
				_UpperCAmelCase							 =		f'{func.__name__}({value})'
				_UpperCAmelCase							 =		timeit(f'__main__.{call}'     ,				setup='import __main__'       )
				print(f'{call:56} = {func(A       )} -- {timing:.4f} seconds'       )

		for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
				for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
						benchmark_a_function(A     ,				A       )
				print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()


