

from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_		:      Any       	=					data
								UpperCAmelCase_		:      Node | None       	=					None



class 					lowercase						:


							def __init__(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					None
								UpperCAmelCase_		:      Optional[int]       	=					None


							def __iter__(			self)							->				Iterator[Any]:
								UpperCAmelCase_		:      Optional[int]       	=					self.head
								while self.head:
									yield node.data
									UpperCAmelCase_		:      Tuple       	=					node.next
									if node == self.head:
										break


							def __len__(			self)							->				int:
								return sum(1 for _ in self)


							def __repr__(			self)							->				str:
								return "->".join(str(_snake_case) for item in iter(self))


							def 						_snake_case							(			self    ,			_snake_case)							->				None:
								self.insert_nth(len(self)    ,			_snake_case)


							def 						_snake_case							(			self    ,			_snake_case)							->				None:
								self.insert_nth(0    ,			_snake_case)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				None:
								if index < 0 or index > len(self):
									raise IndexError('list index out of range.')
								UpperCAmelCase_		:      str       	=					Node(_snake_case)
								if self.head is None:
									UpperCAmelCase_		:      Union[str, Any]       	=					new_node  # first node points itself
									UpperCAmelCase_		:      List[str]       	=					new_node
								elif index == 0:  # insert at head
									UpperCAmelCase_		:      Optional[int]       	=					self.head
									UpperCAmelCase_		:      int       	=					new_node
								else:
									UpperCAmelCase_		:      List[Any]       	=					self.head
									for _ in range(index - 1):
										UpperCAmelCase_		:      Any       	=					temp.next
									UpperCAmelCase_		:      List[str]       	=					temp.next
									UpperCAmelCase_		:      List[Any]       	=					new_node
									if index == len(self) - 1:  # insert at tail
										UpperCAmelCase_		:      Optional[Any]       	=					new_node


							def 						_snake_case							(			self)							->				Any:
								return self.delete_nth(0)


							def 						_snake_case							(			self)							->				Any:
								return self.delete_nth(len(self) - 1)


							def 						_snake_case							(			self    ,			_snake_case = 0)							->				Any:
								if not 0 <= index < len(self):
									raise IndexError('list index out of range.')
								UpperCAmelCase_		:      Optional[int]       	=					self.head
								if self.head == self.tail:  # just one node
									UpperCAmelCase_		:      List[Any]       	=					None
								elif index == 0:  # delete head node
									UpperCAmelCase_		:      List[Any]       	=					self.tail.next.next
									UpperCAmelCase_		:      Union[str, Any]       	=					self.head.next
								else:
									UpperCAmelCase_		:      List[Any]       	=					self.head
									for _ in range(index - 1):
										UpperCAmelCase_		:      List[str]       	=					temp.next
									UpperCAmelCase_		:      Optional[int]       	=					temp.next
									UpperCAmelCase_		:      Union[str, Any]       	=					temp.next.next
									if index == len(self) - 1:  # delete at tail
										UpperCAmelCase_		:      Union[str, Any]       	=					temp
								return delete_node.data





							def 						_snake_case							(			self)							->				bool:
								return len(self) == 0







def 						SCREAMING_SNAKE_CASE(  )    ->    None:
	UpperCAmelCase_		:      Dict       	=					CircularLinkedList()
	assert len(UpperCamelCase  ) == 0
	assert circular_linked_list.is_empty() is True
	assert str(UpperCamelCase  ) == ""

	try:
		circular_linked_list.delete_front()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_tail()
		raise AssertionError  # This should not happen
	except IndexError:
		assert True  # This should happen

	try:
		circular_linked_list.delete_nth(-1  )
		raise AssertionError
	except IndexError:
		assert True

	try:
		circular_linked_list.delete_nth(0  )
		raise AssertionError
	except IndexError:
		assert True

	assert circular_linked_list.is_empty() is True
	for i in range(5  ):
		assert len(UpperCamelCase  ) == i
		circular_linked_list.insert_nth(UpperCamelCase   ,i + 1  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,6  )  )

	circular_linked_list.insert_tail(6  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,7  )  )
	circular_linked_list.insert_head(0  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(0   ,7  )  )

	assert circular_linked_list.delete_front() == 0
	assert circular_linked_list.delete_tail() == 6
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,6  )  )
	assert circular_linked_list.delete_nth(2  ) == 3

	circular_linked_list.insert_nth(2   ,3  )
	assert str(UpperCamelCase  ) == "->".join(str(UpperCamelCase  ) for i in range(1   ,6  )  )

	assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
						import doctest

						doctest.testmod()









'''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


lowerCAmelCase__										= 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 					lowercase						(  a_ ):


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


							def 						_snake_case							(			self    ,			_snake_case)							->				np.ndarray:
								UpperCAmelCase_		:      Optional[int]       	=					self.get_masked_index(_snake_case)
								UpperCAmelCase_		:      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 						_snake_case							(			self    ,			_snake_case)							->				int:
								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 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=None    ,			**_snake_case)							->				Dict[str, GenericTensor]:
								if return_tensors is None:
									UpperCAmelCase_		:      Optional[Any]       	=					self.framework
								UpperCAmelCase_		:      str       	=					self.tokenizer(_snake_case    ,			return_tensors=_snake_case)
								self.ensure_exactly_one_mask_token(_snake_case)
								return model_inputs


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      List[str]       	=					self.model(**_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					model_inputs['input_ids']
								return model_outputs


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

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

									UpperCAmelCase_		:      Tuple       	=					outputs.numpy()

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

									UpperCAmelCase_		:      int       	=					tf.math.top_k(_snake_case    ,			k=_snake_case)
									UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					topk.values.numpy(), topk.indices.numpy()
								else:
									UpperCAmelCase_		:      List[str]       	=					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_		:      int       	=					outputs[0, masked_index, :]
									UpperCAmelCase_		:      str       	=					logits.softmax(dim=-1)
									if target_ids is not None:
										UpperCAmelCase_		:      List[str]       	=					probs[..., target_ids]

									UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					probs.topk(_snake_case)

								UpperCAmelCase_		:      Optional[Any]       	=					[]
								UpperCAmelCase_		:      str       	=					values.shape[0] == 1
								for i, (_values, _predictions) in enumerate(zip(values.tolist()    ,			predictions.tolist())):
									UpperCAmelCase_		:      Union[str, 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_		:      str       	=					target_ids[p].tolist()

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


							def 						_snake_case							(			self    ,			_snake_case=None    ,			_snake_case=None)							->				Dict:
								UpperCAmelCase_		:      str       	=					{}

								if targets is not None:
									UpperCAmelCase_		:      Dict       	=					self.get_target_ids(_snake_case    ,			_snake_case)
									UpperCAmelCase_		:      Optional[int]       	=					target_ids

								if top_k is not None:
									UpperCAmelCase_		:      List[str]       	=					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)							->				Optional[int]:
								UpperCAmelCase_		:      Any       	=					super().__call__(_snake_case    ,			**_snake_case)
								if isinstance(_snake_case    ,			_snake_case) and len(_snake_case) == 1:
									return outputs[0]
								return outputs






