



"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE  :						Any    							=      logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {'''vocab_file''': '''spiece.model'''}

__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      '''▁'''


class 							lowerCamelCase_(   A__    ):





				'''simple docstring'''



				lowercase__   :       List[str]										= VOCAB_FILES_NAMES
				lowercase__   :       List[Any]										= PRETRAINED_VOCAB_FILES_MAP
				lowercase__   :       Tuple										= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

				def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__="[CLS]"				,       lowerCamelCase__="[SEP]"				,       lowerCamelCase__="<unk>"				,       lowerCamelCase__="[SEP]"				,       lowerCamelCase__="<pad>"				,       lowerCamelCase__="[CLS]"				,       lowerCamelCase__="[MASK]"				,       lowerCamelCase__ = None				,       **lowerCamelCase__				,       ):
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								_lowerCamelCase        =				(
								    AddedToken(lowerCamelCase__				,       lstrip=lowerCamelCase__				,       rstrip=lowerCamelCase__				,       normalized=lowerCamelCase__   )
								    if isinstance(lowerCamelCase__				,       lowerCamelCase__   )
								    else mask_token
								)

								_lowerCamelCase        =				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=lowerCamelCase__				,       remove_space=lowerCamelCase__				,       keep_accents=lowerCamelCase__				,       bos_token=lowerCamelCase__				,       eos_token=lowerCamelCase__				,       unk_token=lowerCamelCase__				,       sep_token=lowerCamelCase__				,       pad_token=lowerCamelCase__				,       cls_token=lowerCamelCase__				,       mask_token=lowerCamelCase__				,       sp_model_kwargs=self.sp_model_kwargs				,       **lowerCamelCase__				,       )

								_lowerCamelCase        =				do_lower_case
								_lowerCamelCase        =				remove_space
								_lowerCamelCase        =				keep_accents
								_lowerCamelCase        =				vocab_file

								_lowerCamelCase        =				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(lowerCamelCase__   )

				@property
				def 			snake_case__      (    self   ):
								return len(self.sp_model   )

				def 			snake_case__      (    self   ):
								_lowerCamelCase        =				{self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
								vocab.update(self.added_tokens_encoder   )
								return vocab

				def __getstate__(    self   ):
								_lowerCamelCase        =				self.__dict__.copy()
								_lowerCamelCase        =				None
								return state

				def __setstate__(    self				,       lowerCamelCase__   ):
								_lowerCamelCase        =				d

								# for backward compatibility
								if not hasattr(self				,       '''sp_model_kwargs'''   ):
												_lowerCamelCase        =				{}

								_lowerCamelCase        =				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(self.vocab_file   )

				def 			snake_case__      (    self				,       lowerCamelCase__   ):
								if self.remove_space:
												_lowerCamelCase        =				''' '''.join(inputs.strip().split()   )
								else:
												_lowerCamelCase        =				inputs
								_lowerCamelCase        =				outputs.replace('''``'''				,       '''"'''   ).replace('''\'\''''				,       '''"'''   )

								if not self.keep_accents:
												_lowerCamelCase        =				unicodedata.normalize('''NFKD'''				,       lowerCamelCase__   )
												_lowerCamelCase        =				''''''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase__   )]   )
								if self.do_lower_case:
												_lowerCamelCase        =				outputs.lower()

								return outputs

				def 			snake_case__      (    self				,       lowerCamelCase__   ):
								_lowerCamelCase        =				self.preprocess_text(lowerCamelCase__   )
								_lowerCamelCase        =				self.sp_model.encode(lowerCamelCase__				,       out_type=lowerCamelCase__   )
								_lowerCamelCase        =				[]
								for piece in pieces:
												if len(lowerCamelCase__   ) > 1 and piece[-1] == str(''','''   ) and piece[-2].isdigit():
																_lowerCamelCase        =				self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase__				,       ''''''   )   )
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																				if len(cur_pieces[0]   ) == 1:
																								_lowerCamelCase        =				cur_pieces[1:]
																				else:
																								_lowerCamelCase        =				cur_pieces[0][1:]
																cur_pieces.append(piece[-1]   )
																new_pieces.extend(lowerCamelCase__   )
												else:
																new_pieces.append(lowerCamelCase__   )

								return new_pieces

				def 			snake_case__      (    self				,       lowerCamelCase__   ):
								return self.sp_model.PieceToId(lowerCamelCase__   )

				def 			snake_case__      (    self				,       lowerCamelCase__   ):
								return self.sp_model.IdToPiece(lowerCamelCase__   )

				def 			snake_case__      (    self				,       lowerCamelCase__   ):
								_lowerCamelCase        =				[]
								_lowerCamelCase        =				''''''
								_lowerCamelCase        =				False
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																if not prev_is_special:
																				out_string += " "
																out_string += self.sp_model.decode(lowerCamelCase__   ) + token
																_lowerCamelCase        =				True
																_lowerCamelCase        =				[]
												else:
																current_sub_tokens.append(lowerCamelCase__   )
																_lowerCamelCase        =				False
								out_string += self.sp_model.decode(lowerCamelCase__   )
								return out_string.strip()

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
								_lowerCamelCase        =				[self.sep_token_id]
								_lowerCamelCase        =				[self.cls_token_id]
								if token_ids_a is None:
												return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = False   ):

								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=lowerCamelCase__				,       token_ids_a=lowerCamelCase__				,       already_has_special_tokens=lowerCamelCase__   )

								if token_ids_a is not None:
												return [1] + ([0] * len(lowerCamelCase__   )) + [1] + ([0] * len(lowerCamelCase__   )) + [1]
								return [1] + ([0] * len(lowerCamelCase__   )) + [1]

				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
								_lowerCamelCase        =				[self.sep_token_id]
								_lowerCamelCase        =				[self.cls_token_id]

								if token_ids_a is None:
												return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]





				def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None   ):
								if not os.path.isdir(lowerCamelCase__   ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
												return
								_lowerCamelCase        =				os.path.join(
								    lowerCamelCase__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCamelCase__   ) and os.path.isfile(self.vocab_file   ):
												copyfile(self.vocab_file				,       lowerCamelCase__   )
								elif not os.path.isfile(self.vocab_file   ):
												with open(lowerCamelCase__				,       '''wb'''   ) as fi:
																_lowerCamelCase        =				self.sp_model.serialized_model_proto()
																fi.write(lowerCamelCase__   )

								return (out_vocab_file,)





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   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(lowercase_						,   lowercase_							)
								# Normalize the resulting output vector.
								_lowerCamelCase        =				w / np.linalg.norm(lowercase_							)
								# 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(lowercase_						,   np.dot(lowercase_						,   lowercase_							)							)

								# 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        lowerCAmelCase_(   )	->       None:
				_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(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# 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(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
