

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


SCREAMING_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 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if self.framework == "tf":
														_UpperCAmelCase    :						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=A_		)
										else:
														raise ValueError("Unsupported framework"		)
										return masked_index


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.get_masked_index(A_		)
										_UpperCAmelCase    :						Tuple							=      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     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										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     _UpperCAmelCase			(    self  ,   A_  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										if return_tensors is None:
														_UpperCAmelCase    :						Optional[Any]							=      self.framework
										_UpperCAmelCase    :						Union[str, Any]							=      self.tokenizer(A_  ,   return_tensors=A_		)
										self.ensure_exactly_one_mask_token(A_		)
										return model_inputs


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.model(**A_		)
										_UpperCAmelCase    :						str							=      model_inputs["input_ids"]
										return model_outputs


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=5  ,   A_=None		):







										'''simple docstring'''


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

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

														_UpperCAmelCase    :						int							=      outputs.numpy()

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

														_UpperCAmelCase    :						Tuple							=      tf.math.top_k(A_  ,   k=A_		)
														_UpperCAmelCase						,							_UpperCAmelCase    :						Tuple							=      topk.values.numpy(), topk.indices.numpy()
										else:
														_UpperCAmelCase    :						Union[str, Any]							=      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    :						Any							=      outputs[0, masked_index, :]
														_UpperCAmelCase    :						str							=      logits.softmax(dim=-1		)
														if target_ids is not None:
																		_UpperCAmelCase    :						Any							=      probs[..., target_ids]

														_UpperCAmelCase						,							_UpperCAmelCase    :						Any							=      probs.topk(A_		)

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

																		_UpperCAmelCase    :						int							=      p
																		# Filter padding out:
																		_UpperCAmelCase    :						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
																		_UpperCAmelCase    :						Any							=      self.tokenizer.decode(A_  ,   skip_special_tokens=A_		)
																		_UpperCAmelCase    :						List[Any]							=      {"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     _UpperCAmelCase			(    self  ,   A_  ,   A_=None		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   A_=None  ,   A_=None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      {}

										if targets is not None:
														_UpperCAmelCase    :						str							=      self.get_target_ids(A_  ,   A_		)
														_UpperCAmelCase    :						Any							=      target_ids

										if top_k is not None:
														_UpperCAmelCase    :						Tuple							=      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'''


										_UpperCAmelCase    :						Optional[int]							=      super().__call__(A_  ,   **A_		)
										if isinstance(A_  ,   A_		) and len(A_		) == 1:
														return outputs[0]
										return outputs





from typing import Any

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      data
										_UpperCAmelCase    :						Any							=      None







class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.head
										while temp is not None:
														print(temp.data  ,   end=" "		)
														_UpperCAmelCase    :						str							=      temp.next
										print()


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      Node(A_		)
										_UpperCAmelCase    :						Tuple							=      self.head
										_UpperCAmelCase    :						Tuple							=      new_node





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if node_data_a == node_data_a:
														return
										else:
														_UpperCAmelCase    :						int							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						Tuple							=      node_a.next

														_UpperCAmelCase    :						Dict							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						List[Any]							=      node_a.next

														if node_a is None or node_a is None:
																		return

														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      node_a.data, node_a.data


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = LinkedList()
					for i in range(5, 0, -1):
										ll.push(i)

					ll.print_list()

					ll.swap_nodes(1, 4)
					print('After swapping')
					ll.print_list()


