
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(
    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  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Optional[int] ):
												if self.framework == "tf":
																			lowerCamelCase_					=       tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
												elif self.framework == "pt":
																			lowerCamelCase_					=       torch.nonzero(input_ids == self.tokenizer.mask_token_id ,       as_tuple=a_ )
												else:
																			raise ValueError("""Unsupported framework""" )
												return masked_index

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       self.get_masked_index(a_ )
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  int ):
												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  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  str=None ,       **__UpperCamelCase       :  str ):
												if return_tensors is None:
																			lowerCamelCase_					=       self.framework
												lowerCamelCase_					=       self.tokenizer(a_ ,       return_tensors=a_ )
												self.ensure_exactly_one_mask_token(a_ )
												return model_inputs

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       self.model(**a_ )
												lowerCamelCase_					=       model_inputs["""input_ids"""]
												return model_outputs

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  str=5 ,       __UpperCamelCase       :  Union[str, Any]=None ):
												# Cap top_k if there are targets
												if target_ids is not None and target_ids.shape[0] < top_k:
																			lowerCamelCase_					=       target_ids.shape[0]
												lowerCamelCase_					=       model_outputs["""input_ids"""][0]
												lowerCamelCase_					=       model_outputs["""logits"""]

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

																			lowerCamelCase_					=       outputs.numpy()

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

																			lowerCamelCase_					=       tf.math.top_k(a_ ,       k=a_ )
																			lowerCamelCase_	, lowerCamelCase_					=       topk.values.numpy(), topk.indices.numpy()
												else:
																			lowerCamelCase_					=       torch.nonzero(input_ids == self.tokenizer.mask_token_id ,       as_tuple=a_ ).squeeze(-1 )
																			# Fill mask pipeline supports only one ${mask_token} per sample

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

																			lowerCamelCase_	, lowerCamelCase_					=       probs.topk(a_ )

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

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

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  List[str]=None ,       __UpperCamelCase       :  Dict=None ):
												lowerCamelCase_					=       {}

												if targets is not None:
																			lowerCamelCase_					=       self.get_target_ids(a_ ,       a_ )
																			lowerCamelCase_					=       target_ids

												if top_k is not None:
																			lowerCamelCase_					=       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       :  str ,       __UpperCamelCase       :  Optional[int] ,       *__UpperCamelCase       :  Dict ,       **__UpperCamelCase       :  int ):
												lowerCamelCase_					=       super().__call__(a_ ,       **a_ )
												if isinstance(a_ ,       a_ ) and len(a_ ) == 1:
																			return outputs[0]
												return outputs










import numpy as np







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__ = 1E-12    ,			snake_case__ = 100    ,			)	->     tuple[float, np.ndarray]:



				"""simple docstring"""





				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[1]
				# Ensure proper dimensionality.
				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(snake_case__					) == np.iscomplexobj(snake_case__					)
				lowerCAmelCase__		  =  np.iscomplexobj(snake_case__					)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(snake_case__    ,			input_matrix.conj().T					)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				lowerCAmelCase__		  =  False
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  1E12

				while not convergence:
								# Multiple matrix by the vector.
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			snake_case__					)
								# Normalize the resulting output vector.
								lowerCAmelCase__		  =  w / np.linalg.norm(snake_case__					)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								lowerCAmelCase__		  =  vector.conj().T if is_complex else vector.T
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			np.dot(snake_case__    ,			snake_case__					)					)

								# Check convergence.
								lowerCAmelCase__		  =  np.abs(lambda_ - lambda_previous					) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												lowerCAmelCase__		  =  True

								lowerCAmelCase__		  =  lambda_

				if is_complex:
								lowerCAmelCase__		  =  np.real(lambda_					)

				return lambda_, vector







def     UpperCAmelCase_		(					)	->     None:



				"""simple docstring"""





				lowerCAmelCase__		  =  np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
				lowerCAmelCase__		  =  np.array([41, 4, 20]					)
				lowerCAmelCase__		  =  real_input_matrix.astype(np.complexaaa					)
				lowerCAmelCase__		  =  np.triu(1j * complex_input_matrix    ,			1					)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				lowerCAmelCase__		  =  np.array([41, 4, 20]					).astype(np.complexaaa					)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												lowerCAmelCase__		  =  real_input_matrix
												lowerCAmelCase__		  =  real_vector
								elif problem_type == "complex":
												lowerCAmelCase__		  =  complex_input_matrix
												lowerCAmelCase__		  =  complex_vector

								# Our implementation.
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  power_iteration(snake_case__    ,			snake_case__					)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  np.linalg.eigh(snake_case__					)
								# Last eigenvalue is the maximum one.
								lowerCAmelCase__		  =  eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								lowerCAmelCase__		  =  eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(snake_case__					) - np.abs(snake_case__					)					) <= 1E-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_power_iteration()
