




'''simple docstring'''





import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray							,    snake_case							:	np.ndarray							,    snake_case							:	float = 1e-12							,    snake_case							:	int = 1_0_0							,    ):

							'''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					)
							snake_case_      =				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.

							snake_case_      =				False
							snake_case_      =				0
							snake_case_      =				0
							snake_case_      =				1e12

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

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

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

														snake_case_      =				lambda_

							if is_complex:
														snake_case_      =				np.real(lambda_					)

							return lambda_, vector


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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

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

														# Our implementation.
														snake_case_ ,				snake_case_      =				power_iteration(snake_case							,    snake_case					)

														# Numpy implementation.

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










'''simple docstring'''





import os
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							:       Tuple						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{"vocab_file": "sentencepiece.bpe.model"}

_SCREAMING_SNAKE_CASE							:       Tuple						=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_SCREAMING_SNAKE_CASE							:       Any						=			"▁"




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				List[Any]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				Tuple							=   ["input_ids", "attention_mask"]




			def __init__( self    ,							a__    ,							a__="<s>"    ,							a__="</s>"    ,							a__="</s>"    ,							a__="<s>"    ,							a__="<unk>"    ,							a__="<pad>"    ,							a__="<mask>"    ,							a__ = None    ,							**a__    ,							)    ->    None:

										'''simple docstring'''




										snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else mask_token

										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=a__    ,							eos_token=a__    ,							unk_token=a__    ,							sep_token=a__    ,							cls_token=a__    ,							pad_token=a__    ,							mask_token=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				vocab_file
										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(str(a__							)							)

										snake_case_      =				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										snake_case_      =				len(self.sp_model							) - 1
										snake_case_      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										snake_case_      =				[self.cls_token_id]
										snake_case_      =				[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

										'''simple docstring'''




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

										if token_ids_a is None:
																	return [1] + ([0] * len(a__							)) + [1]
										return [1] + ([0] * len(a__							)) + [1, 1] + ([0] * len(a__							)) + [1]




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										snake_case_      =				[self.sep_token_id]
										snake_case_      =				[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 + sep + token_ids_a + sep							) * [0]




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										return len(self.sp_model							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				{self.convert_ids_to_tokens(a__							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




										return self.sp_model.encode(a__    ,							out_type=a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

										'''simple docstring'''




										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										snake_case_      =				self.sp_model.PieceToId(a__							)

										return spm_id if spm_id else self.unk_token_id




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				[]
										snake_case_      =				""
										snake_case_      =				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(a__							) + token
																								snake_case_      =				True
																								snake_case_      =				[]
																	else:
																								current_sub_tokens.append(a__							)
																								snake_case_      =				False
										out_string += self.sp_model.decode(a__							)
										return out_string.strip()




			def __getstate__( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.__dict__.copy()
										snake_case_      =				None
										return state




			def __setstate__( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				d

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

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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

										'''simple docstring'''




										if not os.path.isdir(a__							):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																	return
										snake_case_      =				os.path.join(
										    a__    ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

										return (out_vocab_file,)




