


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"\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__	( A_		):



				"""simple docstring"""






				def _a (      self ,       A_   )  ->	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=A_   )
									else:
														raise ValueError('Unsupported framework'   )
									return masked_index






				def _a (      self ,       A_   )  ->	np.ndarray:
									__UpperCamelCase   							=self.get_masked_index(A_   )
									__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 _a (      self ,       A_   )  ->	Optional[Any]:
									if isinstance(A_ ,       A_   ):
														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(A_   )






				def _a (      self ,       A_ ,       A_=None ,       **A_   )  ->	Dict[str, GenericTensor]:
									if return_tensors is None:
														__UpperCamelCase   							=self.framework
									__UpperCamelCase   							=self.tokenizer(A_ ,       return_tensors=A_   )
									self.ensure_exactly_one_mask_token(A_   )
									return model_inputs






				def _a (      self ,       A_   )  ->	Optional[Any]:
									__UpperCamelCase   							=self.model(**A_   )
									__UpperCamelCase   							=model_inputs['input_ids']
									return model_outputs






				def _a (      self ,       A_ ,       A_=5 ,       A_=None   )  ->	int:
									# 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(A_ ,       axis=-1   )
														if target_ids is not None:
																			__UpperCamelCase   							=tf.gather_nd(tf.squeeze(A_ ,       0   ) ,       target_ids.reshape(-1 ,       1   )   )
																			__UpperCamelCase   							=tf.expand_dims(A_ ,       0   )

														__UpperCamelCase   							=tf.math.top_k(A_ ,       k=A_   )
														__UpperCamelCase							,				__UpperCamelCase   							=topk.values.numpy(), topk.indices.numpy()
									else:
														__UpperCamelCase   							=torch.nonzero(input_ids == self.tokenizer.mask_token_id ,       as_tuple=A_   ).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(A_   )

									__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(A_ ,       skip_special_tokens=A_   )
																			__UpperCamelCase   							={'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]   ), 'sequence': sequence}
																			row.append(A_   )
														result.append(A_   )
									if single_mask:
														return result[0]
									return result






				def _a (      self ,       A_ ,       A_=None   )  ->	Any:
									if isinstance(A_ ,       A_   ):
														__UpperCamelCase   							=[targets]
									try:
														__UpperCamelCase   							=self.tokenizer.get_vocab()
									except Exception:
														__UpperCamelCase   							={}
									__UpperCamelCase   							=[]
									for target in targets:
														__UpperCamelCase   							=vocab.get(A_ ,       A_   )
														if id_ is None:
																			__UpperCamelCase   							=self.tokenizer(
																			    A_ ,       add_special_tokens=A_ ,       return_attention_mask=A_ ,       return_token_type_ids=A_ ,       max_length=1 ,       truncation=A_ ,       )['input_ids']
																			if len(A_   ) == 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(A_   )   )
									if len(A_   ) == 0:
														raise ValueError('At least one target must be provided when passed.'   )
									__UpperCamelCase   							=np.array(A_   )
									return target_ids






				def _a (      self ,       A_=None ,       A_=None   )  ->	Optional[Any]:
									__UpperCamelCase   							={}

									if targets is not None:
														__UpperCamelCase   							=self.get_target_ids(A_ ,       A_   )
														__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 ,       A_ ,       *A_ ,       **A_   )  ->	Tuple:
									__UpperCamelCase   							=super().__call__(A_ ,       **A_   )
									if isinstance(A_ ,       A_   ) and len(A_   ) == 1:
														return outputs[0]
									return outputs









from __future__ import annotations

from typing import Any

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]    ):
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					[]      ,					0    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
   _A			=						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['A', 'B', 'C'])
   generate_all_subsequences(seq)





