





import unittest

import numpy as np






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = None ,				)		->						np.ndarray:






				"""simple docstring"""


				snake_case_						:    List[Any]				=  np.shape(_UpperCamelCase )
				snake_case_						:    Dict				=  np.shape(_UpperCamelCase )
				snake_case_						:    Any				=  np.shape(_UpperCamelCase )

				if shape_a[0] != shape_b[0]:
								snake_case_						:    List[Any]				=  (
								    '''Expected the same number of rows for A and B. '''
								    f'''Instead found A of size {shape_a} and B of size {shape_b}'''
								)
								raise ValueError(_UpperCamelCase )

				if shape_b[1] != shape_c[1]:
								snake_case_						:    Optional[int]				=  (
								    '''Expected the same number of columns for B and C. '''
								    f'''Instead found B of size {shape_b} and C of size {shape_c}'''
								)
								raise ValueError(_UpperCamelCase )

				snake_case_						:    Tuple				=  pseudo_inv
				if a_inv is None:
								try:
												snake_case_						:    Tuple				=  np.linalg.inv(_UpperCamelCase )
								except np.linalg.LinAlgError:
												raise ValueError(
												    '''Input matrix A is not invertible. Cannot compute Schur complement.''' )

				return mat_c - mat_b.T @ a_inv @ mat_b



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def 			lowerCamelCase  (self    )     ->   None:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
							snake_case_						:    List[Any]				=  np.array([[0, 3], [3, 0], [2, 3]]    )
							snake_case_						:    Dict				=  np.array([[2, 1], [6, 3]]    )

							snake_case_						:    Optional[Any]				=  schur_complement(__magic_name__					,							__magic_name__					,							__magic_name__    )

							snake_case_						:    List[Any]				=  np.block([[a, b], [b.T, c]]    )

							snake_case_						:    Tuple				=  np.linalg.det(__magic_name__    )
							snake_case_						:    Union[str, Any]				=  np.linalg.det(__magic_name__    )
							snake_case_						:    List[str]				=  np.linalg.det(__magic_name__    )

							self.assertAlmostEqual(__magic_name__					,							det_a * det_s    )






			def 			lowerCamelCase  (self    )     ->   None:


							'''simple docstring'''



							snake_case_						:    Dict				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
							snake_case_						:    str				=  np.array([[0, 3], [3, 0], [2, 3]]    )
							snake_case_						:    Any				=  np.array([[2, 1], [6, 3]]    )

							with self.assertRaises(__magic_name__    ):
											schur_complement(__magic_name__					,							__magic_name__					,							__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   None:


							'''simple docstring'''



							snake_case_						:    Dict				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
							snake_case_						:    Any				=  np.array([[0, 3], [3, 0], [2, 3]]    )
							snake_case_						:    Optional[int]				=  np.array([[2, 1, 3], [6, 3, 5]]    )

							with self.assertRaises(__magic_name__    ):
											schur_complement(__magic_name__					,							__magic_name__					,							__magic_name__    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	unittest.main()







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase_    =				logging.getLogger(__name__)



@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     str
			lowerCamelCase_     :     List[str]
			lowerCamelCase_     :     Optional[List[str]]




@dataclass
class 					__lowerCAmelCase       :
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     List[int]
			lowerCamelCase_     :     Optional[List[int]]       			=      None
			lowerCamelCase_     :     Optional[List[int]]       			=      None




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     str       			=      '''train'''
			lowerCamelCase_     :     List[str]       			=      '''dev'''
			lowerCamelCase_     :     List[Any]       			=      '''test'''




class 					__lowerCAmelCase       :






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__    )     ->   List[InputExample]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							raise NotImplementedError






			@staticmethod
			def 			lowerCamelCase  (__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=False					,							__magic_name__="[CLS]"					,							__magic_name__=1					,							__magic_name__="[SEP]"					,							__magic_name__=False					,							__magic_name__=False					,							__magic_name__=0					,							__magic_name__=0					,							__magic_name__=-100					,							__magic_name__=0					,							__magic_name__=True					,							)     ->   List[InputFeatures]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  {label: i for i, label in enumerate(__magic_name__    )}

							snake_case_						:    Dict				=  []
							for ex_index, example in enumerate(__magic_name__    ):
											if ex_index % 1_0000 == 0:
															logger.info('''Writing example %d of %d'''					,							__magic_name__					,							len(__magic_name__    )    )

											snake_case_						:    List[str]				=  []
											snake_case_						:    List[str]				=  []
											for word, label in zip(example.words					,							example.labels    ):
															snake_case_						:    Optional[Any]				=  tokenizer.tokenize(__magic_name__    )

															# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
															if len(__magic_name__    ) > 0:
																			tokens.extend(__magic_name__    )
																			# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																			label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__magic_name__    ) - 1)    )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
											snake_case_						:    Union[str, Any]				=  tokenizer.num_special_tokens_to_add()
											if len(__magic_name__    ) > max_seq_length - special_tokens_count:
															snake_case_						:    str				=  tokens[: (max_seq_length - special_tokens_count)]
															snake_case_						:    Any				=  label_ids[: (max_seq_length - special_tokens_count)]

											# The convention in BERT is:
											# (a) For sequence pairs:
											#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
											#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
											# (b) For single sequences:
											#  tokens:   [CLS] the dog is hairy . [SEP]
											#  type_ids:   0   0   0   0  0     0   0
											#
											# Where "type_ids" are used to indicate whether this is the first
											# sequence or the second sequence. The embedding vectors for `type=0` and
											# `type=1` were learned during pre-training and are added to the wordpiece
											# embedding vector (and position vector). This is not *strictly* necessary
											# since the [SEP] token unambiguously separates the sequences, but it makes
											# it easier for the model to learn the concept of sequences.
											#
											# For classification tasks, the first vector (corresponding to [CLS]) is
											# used as the "sentence vector". Note that this only makes sense because
											# the entire model is fine-tuned.
											tokens += [sep_token]
											label_ids += [pad_token_label_id]
											if sep_token_extra:
															# roberta uses an extra separator b/w pairs of sentences
															tokens += [sep_token]
															label_ids += [pad_token_label_id]
											snake_case_						:    Union[str, Any]				=  [sequence_a_segment_id] * len(__magic_name__    )

											if cls_token_at_end:
															tokens += [cls_token]
															label_ids += [pad_token_label_id]
															segment_ids += [cls_token_segment_id]
											else:
															snake_case_						:    Union[str, Any]				=  [cls_token] + tokens
															snake_case_						:    List[Any]				=  [pad_token_label_id] + label_ids
															snake_case_						:    Optional[Any]				=  [cls_token_segment_id] + segment_ids

											snake_case_						:    Optional[Any]				=  tokenizer.convert_tokens_to_ids(__magic_name__    )

											# The mask has 1 for real tokens and 0 for padding tokens. Only real
											# tokens are attended to.
											snake_case_						:    int				=  [1 if mask_padding_with_zero else 0] * len(__magic_name__    )

											# Zero-pad up to the sequence length.
											snake_case_						:    Optional[int]				=  max_seq_length - len(__magic_name__    )
											if pad_on_left:
															snake_case_						:    Optional[Any]				=  ([pad_token] * padding_length) + input_ids
															snake_case_						:    Optional[int]				=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
															snake_case_						:    Optional[Any]				=  ([pad_token_segment_id] * padding_length) + segment_ids
															snake_case_						:    Dict				=  ([pad_token_label_id] * padding_length) + label_ids
											else:
															input_ids += [pad_token] * padding_length
															input_mask += [0 if mask_padding_with_zero else 1] * padding_length
															segment_ids += [pad_token_segment_id] * padding_length
															label_ids += [pad_token_label_id] * padding_length

											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length
											assert len(__magic_name__    ) == max_seq_length

											if ex_index < 5:
															logger.info('''*** Example ***'''    )
															logger.info('''guid: %s'''					,							example.guid    )
															logger.info('''tokens: %s'''					,							''' '''.join([str(__magic_name__    ) for x in tokens]    )    )
															logger.info('''input_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in input_ids]    )    )
															logger.info('''input_mask: %s'''					,							''' '''.join([str(__magic_name__    ) for x in input_mask]    )    )
															logger.info('''segment_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in segment_ids]    )    )
															logger.info('''label_ids: %s'''					,							''' '''.join([str(__magic_name__    ) for x in label_ids]    )    )

											if "token_type_ids" not in tokenizer.model_input_names:
															snake_case_						:    int				=  None

											features.append(
											    InputFeatures(
											        input_ids=__magic_name__					,							attention_mask=__magic_name__					,							token_type_ids=__magic_name__					,							label_ids=__magic_name__    )    )
							return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset



	class 					__lowerCAmelCase       (			_a     ):
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      nn.CrossEntropyLoss().ignore_index






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Union[str, Any]:


								'''simple docstring'''



								snake_case_						:    List[str]				=  os.path.join(
								    __magic_name__					,							'''cached_{}_{}_{}'''.format(mode.value					,							tokenizer.__class__.__name__					,							str(__magic_name__    )    )					,							)

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								snake_case_						:    Dict				=  cached_features_file + '''.lock'''
								with FileLock(__magic_name__    ):
												if os.path.exists(__magic_name__    ) and not overwrite_cache:
																logger.info(F'''Loading features from cached file {cached_features_file}'''    )
																snake_case_						:    Dict				=  torch.load(__magic_name__    )
												else:
																logger.info(F'''Creating features from dataset file at {data_dir}'''    )
																snake_case_						:    Any				=  token_classification_task.read_examples_from_file(__magic_name__					,							__magic_name__    )
																# TODO clean up all this to leverage built-in features of tokenizers
																snake_case_						:    int				=  token_classification_task.convert_examples_to_features(
																    __magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							cls_token_at_end=bool(model_type in ['''xlnet''']    )					,							cls_token=tokenizer.cls_token					,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,							sep_token=tokenizer.sep_token					,							sep_token_extra=__magic_name__					,							pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,							pad_token=tokenizer.pad_token_id					,							pad_token_segment_id=tokenizer.pad_token_type_id					,							pad_token_label_id=self.pad_token_label_id					,							)
																logger.info(F'''Saving features into cached file {cached_features_file}'''    )
																torch.save(self.features					,							__magic_name__    )






				def __len__(self    )     ->   Optional[Any]:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]


if is_tf_available():
	import tensorflow as tf



	class 					__lowerCAmelCase       :
				lowerCamelCase_     :     List[InputFeatures]
				lowerCamelCase_     :     int       			=      -100






				def __init__(self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__ = None					,							__magic_name__=False					,							__magic_name__ = Split.train					,							)     ->   Optional[int]:


								'''simple docstring'''



								snake_case_						:    Optional[int]				=  token_classification_task.read_examples_from_file(__magic_name__					,							__magic_name__    )
								# TODO clean up all this to leverage built-in features of tokenizers
								snake_case_						:    int				=  token_classification_task.convert_examples_to_features(
								    __magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							cls_token_at_end=bool(model_type in ['''xlnet''']    )					,							cls_token=tokenizer.cls_token					,							cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,							sep_token=tokenizer.sep_token					,							sep_token_extra=__magic_name__					,							pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,							pad_token=tokenizer.pad_token_id					,							pad_token_segment_id=tokenizer.pad_token_type_id					,							pad_token_label_id=self.pad_token_label_id					,							)

								def gen():
												for ex in self.features:
																if ex.token_type_ids is None:
																				yield (
																				    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																				    ex.label_ids,
																				)
																else:
																				yield (
																				    {
																				        "input_ids": ex.input_ids,
																				        "attention_mask": ex.attention_mask,
																				        "token_type_ids": ex.token_type_ids,
																				    },
																				    ex.label_ids,
																				)

								if "token_type_ids" not in tokenizer.model_input_names:
												snake_case_						:    Optional[Any]				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)					,							(
												        {'''input_ids''': tf.TensorShape([None]    ), '''attention_mask''': tf.TensorShape([None]    )},
												        tf.TensorShape([None]    ),
												    )					,							)
								else:
												snake_case_						:    int				=  tf.data.Dataset.from_generator(
												    __magic_name__					,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)					,							(
												        {
												            '''input_ids''': tf.TensorShape([None]    ),
												            '''attention_mask''': tf.TensorShape([None]    ),
												            '''token_type_ids''': tf.TensorShape([None]    ),
												        },
												        tf.TensorShape([None]    ),
												    )					,							)






				def 			lowerCamelCase  (self    )     ->   List[Any]:


								'''simple docstring'''



								snake_case_						:    Optional[Any]				=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

								return self.dataset






				def __len__(self    )     ->   str:


								'''simple docstring'''



								return len(self.features    )






				def __getitem__(self					,							__magic_name__    )     ->   InputFeatures:


								'''simple docstring'''



								return self.features[i]
