




"""simple docstring"""






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


UpperCamelCase__    =      logging.getLogger(__name__)




@dataclass
class 	a__      :
						snake_case__							   =			42
						snake_case__							   =			42
						snake_case__							   =			42




@dataclass
class 	a__      :
						snake_case__							   =			42
						snake_case__							   =			42
						snake_case__							   =			None
						snake_case__							   =			None




class 	a__      (							lowerCamelCase__	):
						snake_case__							   =			"""train"""
						snake_case__							   =			"""dev"""
						snake_case__							   =			"""test"""




class 	a__      :





						@staticmethod
						def    __UpperCamelCase  (				a__   :						Union[str, Any]  ,a__   :						int)    ->							List[InputExample]:




											"""simple docstring"""







											raise NotImplementedError





						@staticmethod
						def    __UpperCamelCase  (				a__   :						List[str])    ->							List[str]:




											"""simple docstring"""







											raise NotImplementedError







						@staticmethod
						def    __UpperCamelCase  (				a__   :						List[Any]  ,a__   :						Union[str, Any]  ,a__   :						int  ,a__   :						str  ,a__   :						Union[str, Any]=False  ,a__   :						Union[str, Any]="[CLS]"  ,a__   :						List[Any]=1  ,a__   :						Dict="[SEP]"  ,a__   :						Dict=False  ,a__   :						List[str]=False  ,a__   :						Tuple=0  ,a__   :						Dict=0  ,a__   :						Union[str, Any]=-100  ,a__   :						List[Any]=0  ,a__   :						List[str]=True  ,)    ->							List[InputFeatures]:




											"""simple docstring"""







											_lowerCAmelCase:str						=	{label: i for i, label in enumerate(snake_case__)}

											_lowerCAmelCase:Optional[Any]						=	[]
											for ex_index, example in enumerate(snake_case__):
																if ex_index % 1_0000 == 0:
																					logger.info('''Writing example %d of %d'''  ,snake_case__  ,len(snake_case__))

																_lowerCAmelCase:Dict						=	[]
																_lowerCAmelCase:Optional[int]						=	[]
																for word, label in zip(example.words  ,example.labels):
																					_lowerCAmelCase:Dict						=	tokenizer.tokenize(snake_case__)

																					# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																					if len(snake_case__) > 0:
																										tokens.extend(snake_case__)
																										# 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(snake_case__) - 1))

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																_lowerCAmelCase:Any						=	tokenizer.num_special_tokens_to_add()
																if len(snake_case__) > max_seq_length - special_tokens_count:
																					_lowerCAmelCase:List[Any]						=	tokens[: (max_seq_length - special_tokens_count)]
																					_lowerCAmelCase:int						=	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]
																_lowerCAmelCase:Union[str, Any]						=	[sequence_a_segment_id] * len(snake_case__)

																if cls_token_at_end:
																					tokens += [cls_token]
																					label_ids += [pad_token_label_id]
																					segment_ids += [cls_token_segment_id]
																else:
																					_lowerCAmelCase:Tuple						=	[cls_token] + tokens
																					_lowerCAmelCase:Tuple						=	[pad_token_label_id] + label_ids
																					_lowerCAmelCase:Tuple						=	[cls_token_segment_id] + segment_ids

																_lowerCAmelCase:int						=	tokenizer.convert_tokens_to_ids(snake_case__)

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

																# Zero-pad up to the sequence length.
																_lowerCAmelCase:Optional[int]						=	max_seq_length - len(snake_case__)
																if pad_on_left:
																					_lowerCAmelCase:List[Any]						=	([pad_token] * padding_length) + input_ids
																					_lowerCAmelCase:Any						=	([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																					_lowerCAmelCase:List[Any]						=	([pad_token_segment_id] * padding_length) + segment_ids
																					_lowerCAmelCase:Union[str, Any]						=	([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(snake_case__) == max_seq_length
																assert len(snake_case__) == max_seq_length
																assert len(snake_case__) == max_seq_length
																assert len(snake_case__) == max_seq_length

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

																if "token_type_ids" not in tokenizer.model_input_names:
																					_lowerCAmelCase:Dict						=	None

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


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




				class 	a__      (							lowerCamelCase__	):
										snake_case__							   =			42
										snake_case__							   =			nn.CrossEntropyLoss().ignore_index





										def __init__(				self   :						int  ,a__   :						str  ,a__   :						List[str]  ,a__   :						Union[str, Any]  ,a__   :						Optional[int]  ,a__   :						int  ,a__   :						Tuple = None  ,a__   :						List[str]=False  ,a__   :						List[str] = Split.train  ,)    ->							Union[str, Any]:




															"""simple docstring"""







															_lowerCAmelCase:Tuple						=	os.path.join(
															    snake_case__  ,'''cached_{}_{}_{}'''.format(mode.value  ,tokenizer.__class__.__name__  ,str(snake_case__))  ,)

															# Make sure only the first process in distributed training processes the dataset,
															# and the others will use the cache.
															_lowerCAmelCase:List[Any]						=	cached_features_file + """.lock"""
															with FileLock(snake_case__):
																				if os.path.exists(snake_case__) and not overwrite_cache:
																									logger.info(F'Loading features from cached file {cached_features_file}')
																									_lowerCAmelCase:Union[str, Any]						=	torch.load(snake_case__)
																				else:
																									logger.info(F'Creating features from dataset file at {data_dir}')
																									_lowerCAmelCase:Dict						=	token_classification_task.read_examples_from_file(snake_case__  ,snake_case__)
																									# TODO clean up all this to leverage built-in features of tokenizers
																									_lowerCAmelCase:Dict						=	token_classification_task.convert_examples_to_features(
																									    snake_case__  ,snake_case__  ,snake_case__  ,snake_case__  ,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=snake_case__  ,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  ,snake_case__)





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




															"""simple docstring"""







															return len(self.features)







										def __getitem__(				self   :						Union[str, Any]  ,a__   :						int)    ->							InputFeatures:




															"""simple docstring"""







															return self.features[i]


if is_tf_available():
				import tensorflow as tf




				class 	a__      :
										snake_case__							   =			42
										snake_case__							   =			-1_0_0





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




															"""simple docstring"""







															_lowerCAmelCase:Optional[Any]						=	token_classification_task.read_examples_from_file(snake_case__  ,snake_case__)
															# TODO clean up all this to leverage built-in features of tokenizers
															_lowerCAmelCase:Optional[Any]						=	token_classification_task.convert_examples_to_features(
															    snake_case__  ,snake_case__  ,snake_case__  ,snake_case__  ,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=snake_case__  ,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:
																				_lowerCAmelCase:int						=	tf.data.Dataset.from_generator(
																				    snake_case__  ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)  ,(
																				        {'''input_ids''': tf.TensorShape([None]), '''attention_mask''': tf.TensorShape([None])},
																				        tf.TensorShape([None]),
																				    )  ,)
															else:
																				_lowerCAmelCase:Any						=	tf.data.Dataset.from_generator(
																				    snake_case__  ,({'''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    __UpperCamelCase  (				self   :						Dict)    ->							str:




															"""simple docstring"""







															_lowerCAmelCase:Tuple						=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

															return self.dataset





										def __len__(				self   :						Any)    ->							int:




															"""simple docstring"""







															return len(self.features)







										def __getitem__(				self   :						List[str]  ,a__   :						Optional[Any])    ->							InputFeatures:




															"""simple docstring"""







															return self.features[i]













def  __magic_name__  (				lowercase				)				-> list[list]:

							"""simple docstring"""



							lowercase_	:   int  				=     current_set.copy()
							for row_index, row in enumerate(lowercase				):
														lowercase_	:   Tuple  				=     row[0]
														for column_index, column in enumerate(lowercase				):
																					if magnitude == 0:
																												lowercase_	:   Optional[int]  				=     column
																												continue
																					lowercase_	:   List[str]  				=     column / magnitude
    # Subtract to cancel term
							lowercase_	:   List[str]  				=     current_set[0]
							lowercase_	:   Optional[int]  				=     [first_row]
							lowercase_	:   Optional[Any]  				=     current_set[1::]
							for row in current_set:
														lowercase_	:   Union[str, Any]  				=     []
														# If first term is 0, it is already in form we want, so we preserve it
														if row[0] == 0:
																					final_set.append(lowercase				)
																					continue
														for column_index in range(len(lowercase				)				):
																					temp_row.append(first_row[column_index] - row[column_index]				)
														final_set.append(lowercase				)
							# Create next recursion iteration set
							if len(final_set[0]				) != 3:
														lowercase_	:   Tuple  				=     final_set[0]
														lowercase_	:   Dict  				=     []
														lowercase_	:   Any  				=     []
														for row in final_set[1::]:
																					current_first_column.append(row[0]				)
																					next_iteration.append(row[1::]				)
														lowercase_	:   Optional[Any]  				=     simplify(lowercase				)
														for i in range(len(lowercase				)				):
																					resultant[i].insert(0				,       current_first_column[i]				)
														resultant.insert(0				,       lowercase				)
														lowercase_	:   Tuple  				=     resultant
							return final_set





def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



							if len(lowercase				) == 0:
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""				)
							lowercase_	:   Tuple  				=     len(lowercase				) + 1
							if any(len(lowercase				) != _length for item in equations				):
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""				)
							for row in equations:
														if any(not isinstance(lowercase				,       (int, float)				) for column in row				):
																					raise ValueError("""solve_simultaneous() requires lists of integers"""				)
							if len(lowercase				) == 1:
														return [equations[0][-1] / equations[0][0]]
							lowercase_	:   List[str]  				=     equations.copy()
							if any(0 in row for row in data_set				):
														lowercase_	:   int  				=     data_set.copy()
														lowercase_	:   Dict  				=     []
														for row_index, row in enumerate(lowercase				):
																					if 0 not in row:
																												lowercase_	:   Dict  				=     data_set.pop(lowercase				)
																												break
														if not full_row:
																					raise ValueError("""solve_simultaneous() requires at least 1 full equation"""				)
														data_set.insert(0				,       lowercase				)
							lowercase_	:   Tuple  				=     data_set.copy()
							lowercase_	:   List[Any]  				=     simplify(lowercase				)
							lowercase_	:   Dict  				=     simplified[::-1]
							lowercase_	:   list  				=     []
							for row in simplified:
														lowercase_	:   Union[str, Any]  				=     row[-1]
														if not solutions:
																					if row[-2] == 0:
																												solutions.append(0				)
																												continue
																					solutions.append(current_solution / row[-2]				)
																					continue
														lowercase_	:   Any  				=     row.copy()[: len(lowercase				) - 1 :]
														while temp_row[0] == 0:
																					temp_row.pop(0				)
														if len(lowercase				) == 0:
																					solutions.append(0				)
																					continue
														lowercase_	:   Any  				=     temp_row[1::]
														lowercase_	:   Union[str, Any]  				=     temp_row[::-1]
														for column_index, column in enumerate(lowercase				):
																					current_solution -= column * solutions[column_index]
														solutions.append(lowercase				)
							lowercase_	:   Any  				=     []
							for item in solutions:
														final.append(float(round(lowercase				,       5				)				)				)
							return final[::-1]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							UpperCAmelCase_							=				[
							    [2, 1, 1, 1, 1, 4],
							    [1, 2, 1, 1, 1, 5],
							    [1, 1, 2, 1, 1, 6],
							    [1, 1, 1, 2, 1, 7],
							    [1, 1, 1, 1, 2, 8],
							]
							print(solve_simultaneous(eq))
							print(solve_simultaneous([[4, 2]]))