






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__  :Optional[int]             =							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      ,   _SCREAMING_SNAKE_CASE						)			->	np.ndarray:




									"""simple docstring"""





									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=_SCREAMING_SNAKE_CASE						)
									else:
																raise ValueError('Unsupported framework'						)
									return masked_index
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	np.ndarray:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_masked_index(_SCREAMING_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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict[str, GenericTensor]:




									"""simple docstring"""





									if return_tensors is None:
																_UpperCAmelCase					=      self.framework
									_UpperCAmelCase					=      self.tokenizer(_SCREAMING_SNAKE_CASE      ,   return_tensors=_SCREAMING_SNAKE_CASE						)
									self.ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE						)
									return model_inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model_inputs['input_ids']
									return model_outputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=5      ,   _SCREAMING_SNAKE_CASE=None						)			->	Any:




									"""simple docstring"""





									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(_SCREAMING_SNAKE_CASE      ,   axis=-1						)
																if target_ids is not None:
																							_UpperCAmelCase					=      tf.gather_nd(tf.squeeze(_SCREAMING_SNAKE_CASE      ,   0						)      ,   target_ids.reshape(-1      ,   1						)						)
																							_UpperCAmelCase					=      tf.expand_dims(_SCREAMING_SNAKE_CASE      ,   0						)

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

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

																_UpperCAmelCase		,	_UpperCAmelCase					=      probs.topk(_SCREAMING_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(_SCREAMING_SNAKE_CASE      ,   skip_special_tokens=_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      {'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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	Optional[Any]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase					=      [targets]
									try:
																_UpperCAmelCase					=      self.tokenizer.get_vocab()
									except Exception:
																_UpperCAmelCase					=      {}
									_UpperCAmelCase					=      []
									for target in targets:
																_UpperCAmelCase					=      vocab.get(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																if id_ is None:
																							_UpperCAmelCase					=      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
																							_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(_SCREAMING_SNAKE_CASE						)						)
									if len(_SCREAMING_SNAKE_CASE						) == 0:
																raise ValueError('At least one target must be provided when passed.'						)
									_UpperCAmelCase					=      np.array(_SCREAMING_SNAKE_CASE						)
									return target_ids
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      {}

									if targets is not None:
																_UpperCAmelCase					=      self.get_target_ids(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_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      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      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




from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int	)  ->		tuple[int | None, int | None, float]:


							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							_UpperCAmelCase					=      (low + high) // 2
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  a__      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_subarray(a__      ,  mid + 1      ,  a__	)
							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      max_cross_sum(a__      ,  a__      ,  a__      ,  a__	)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum

def 	lowerCAmelCase__			(      a__:		Sequence[float]      ,  a__:		int      ,  a__:		int      ,  a__:		int	)  ->		tuple[int, int, float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1
							_UpperCAmelCase		,	_UpperCAmelCase					=      float('-inf'	), -1

							_UpperCAmelCase					=      0
							for i in range(a__      ,  low - 1      ,  -1	):
														summ += arr[i]
														if summ > left_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							_UpperCAmelCase					=      0
							for i in range(mid + 1      ,  high + 1	):
														summ += arr[i]
														if summ > right_sum:
																					_UpperCAmelCase					=      summ
																					_UpperCAmelCase					=      i

							return max_left, max_right, (left_sum + right_sum)

def 	lowerCAmelCase__			(      a__:		int	)  ->		float:


							'''simple docstring'''





							_UpperCAmelCase					=      [randint(1      ,  a__	) for _ in range(a__	)]
							_UpperCAmelCase					=      time.time()
							max_subarray(a__      ,  0      ,  input_size - 1	)
							_UpperCAmelCase					=      time.time()
							return end - start

def 	lowerCAmelCase__			(      )  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
							_UpperCAmelCase					=      [time_max_subarray(a__	) for input_size in input_sizes]
							print('No of Inputs\t\tTime Taken'	)
							for input_size, runtime in zip(a__      ,  a__	):
														print(a__      ,  '\t\t'      ,  a__	)
							plt.plot(a__      ,  a__	)
							plt.xlabel('Number of Inputs'	)
							plt.ylabel('Time taken in seconds'	)
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()
