
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							:int							 =				logging.get_logger(__name__)





@add_end_docstrings(
    __SCREAMING_SNAKE_CASE			,			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      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: str							,A					: GenericTensor ):
					if self.framework == "tf":
									__A   			= tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
					elif self.framework == "pt":
									__A   			= torch.nonzero(input_ids == self.tokenizer.mask_token_id							,as_tuple=A )
					else:
									raise ValueError("Unsupported framework" )
					return masked_index






	def 				UpperCamelCase_  (       self					: int							,A					: GenericTensor ):
					__A   			= self.get_masked_index(A )
					__A   			= 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					: int							,A					: GenericTensor ):
					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					: List[str]							,A					: Optional[Any]							,A					: Dict=None							,**A					: Dict ):
					if return_tensors is None:
									__A   			= self.framework
					__A   			= self.tokenizer(A							,return_tensors=A )
					self.ensure_exactly_one_mask_token(A )
					return model_inputs






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: int ):
					__A   			= self.model(**A )
					__A   			= model_inputs["input_ids"]
					return model_outputs






	def 				UpperCamelCase_  (       self					: Any							,A					: int							,A					: Any=5							,A					: Any=None ):
					# Cap top_k if there are targets
					if target_ids is not None and target_ids.shape[0] < top_k:
									__A   			= target_ids.shape[0]
					__A   			= model_outputs["input_ids"][0]
					__A   			= model_outputs["logits"]

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

									__A   			= outputs.numpy()

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

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

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

									__A			,							__A   			= probs.topk(A )

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

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






	def 				UpperCamelCase_  (       self					: Dict							,A					: Union[str, Any]=None							,A					: int=None ):
					__A   			= {}

					if targets is not None:
									__A   			= self.get_target_ids(A							,A )
									__A   			= target_ids

					if top_k is not None:
									__A   			= 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					: Optional[Any]							,A					: Optional[int]							,*A					: Dict							,**A					: Tuple ):
					__A   			= super().__call__(A							,**A )
					if isinstance(A							,A ) and len(A ) == 1:
									return outputs[0]
					return outputs


import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



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

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

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

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

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

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

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

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= 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(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()
