





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_UpperCamelCase							=      logging.getLogger(__name__)

_UpperCamelCase							=      'pytorch_model.bin'
@dataclasses.dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		dataclasses.field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}   )
				__snake_case   :       Optional[str]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}       ,  )

@dataclasses.dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}   )
				__snake_case   :       str	 =		dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}   )
				__snake_case   :       Optional[str]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """A csv or a json file containing the validation data."""}   )
				__snake_case   :       Optional[str]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """The name of the task to train on."""}       ,  )
				__snake_case   :       Optional[List[str]]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """The list of labels for the task."""}   )

@dataclasses.dataclass
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""
				__snake_case   :       str	 =		dataclasses.field(
				    metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}   )
				__snake_case   :       Optional[str]	 =		dataclasses.field(
				    default="""accuracy"""       ,  metadata={"""help""": """The evaluation metric used for the task."""}   )
				__snake_case   :       Optional[str]	 =		dataclasses.field(
				    default="""no"""       ,  metadata={
				        """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
				    }       ,  )
				__snake_case   :       Optional[int]	 =		dataclasses.field(
				    default=10       ,  metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}       ,  )
				__snake_case   :       Optional[float]	 =		dataclasses.field(
				    default=0.0       ,  metadata={
				        """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
				    }       ,  )
				__snake_case   :       Optional[bool]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}       ,  )
				__snake_case   :       Optional[bool]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}       ,  )
				__snake_case   :       Optional[bool]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}       ,  )
				__snake_case   :       Optional[float]	 =		dataclasses.field(
				    default=0.0       ,  metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}       ,  )
				__snake_case   :       Optional[int]	 =		dataclasses.field(
				    default=1_00       ,  metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}       ,  )
				__snake_case   :       Optional[int]	 =		dataclasses.field(
				    default=snake_case__       ,  metadata={"""help""": """Random seed for initialization."""}       ,  )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[str]         =datasets.concatenate_datasets([infer_input, infer_output]    ,						axis=1   )

							if args.do_filter_by_confidence:
														__lowerCamelCase      :      Optional[Any]         =dataset.filter(lambda SCREAMING_SNAKE_CASE   : example["probability"] > args.confidence_threshold   )

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														__lowerCamelCase      :      Tuple         =int(eval_result * len(SCREAMING_SNAKE_CASE   )   )
														print(SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      int         =dataset.sort('''probability'''    ,						reverse=SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      Any         =dataset.select(range(SCREAMING_SNAKE_CASE   )   )

							__lowerCamelCase      :      int         =dataset.remove_columns(['''label''', '''probability''']   )
							__lowerCamelCase      :      int         =dataset.rename_column('''prediction'''    ,						'''label'''   )
							__lowerCamelCase      :      int         =dataset.map(lambda SCREAMING_SNAKE_CASE   : {"label": idalabel[example["label"]]}   )
							__lowerCamelCase      :      Dict         =dataset.shuffle(seed=args.seed   )

							__lowerCamelCase      :      Tuple         =os.path.join(SCREAMING_SNAKE_CASE    ,						F'train_pseudo.{args.data_file_extension}'   )
							if args.data_file_extension == "csv":
														dataset.to_csv(SCREAMING_SNAKE_CASE    ,						index=SCREAMING_SNAKE_CASE   )
							else:
														dataset.to_json(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						**SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,						datefmt='''%m/%d/%Y %H:%M:%S'''    ,						level=logging.INFO    ,						)
							logger.info(accelerator.state   )

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR   )

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							__lowerCamelCase      :      Union[str, Any]         =STModelArguments(model_name_or_path=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      int         =STDataArguments(train_file=SCREAMING_SNAKE_CASE    ,						infer_file=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      int         =STTrainingArguments(output_dir=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      int         =argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(SCREAMING_SNAKE_CASE   ).items():
																					setattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							for key, value in kwargs.items():
														if hasattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					setattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

    # Sanity checks
							__lowerCamelCase      :      Optional[int]         ={}
							__lowerCamelCase      :      Any         =None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							__lowerCamelCase      :      List[str]         =args.train_file
							__lowerCamelCase      :      int         =args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														__lowerCamelCase      :      Tuple         =args.eval_file

							for key in data_files:
														__lowerCamelCase      :      str         =data_files[key].split('''.'''   )[-1]
														assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
														if args.data_file_extension is None:
																					__lowerCamelCase      :      str         =extension
														else:
																					assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'

							assert (
							    args.eval_metric in datasets.list_metrics()
							), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed   )

							logger.info('''Creating the initial data directory for self-training...'''   )
							__lowerCamelCase      :      Dict         =F'{args.output_dir}/self-train_iter-{{}}'.format
							__lowerCamelCase      :      str         =data_dir_format(0   )

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir    ,						exist_ok=SCREAMING_SNAKE_CASE   )
																					os.makedirs(SCREAMING_SNAKE_CASE    ,						exist_ok=SCREAMING_SNAKE_CASE   )
							accelerator.wait_for_everyone()

							__lowerCamelCase      :      Tuple         =None
							__lowerCamelCase      :      Union[str, Any]         =None
							__lowerCamelCase      :      Any         =0
							__lowerCamelCase      :      str         =False
							# Show the progress bar
							__lowerCamelCase      :      Union[str, Any]         =tqdm(range(args.max_selftrain_iterations   )    ,						disable=not accelerator.is_local_main_process   )

							# Self-train
							for iteration in range(0    ,						int(args.max_selftrain_iterations   )   ):
														__lowerCamelCase      :      Any         =data_dir_format(SCREAMING_SNAKE_CASE   )
														assert os.path.exists(SCREAMING_SNAKE_CASE   )

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														__lowerCamelCase      :      Optional[int]         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''stage-1'''   )
														__lowerCamelCase      :      Optional[Any]         ={
														    '''accelerator''': accelerator,
														    '''model_name_or_path''': args.model_name_or_path,
														    '''cache_dir''': args.cache_dir,
														    '''do_train''': True,
														    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
														    '''do_eval''': True if args.eval_file is not None else False,
														    '''eval_file''': data_files['''eval'''],
														    '''do_predict''': True,
														    '''infer_file''': data_files['''infer'''],
														    '''task_name''': args.task_name,
														    '''label_list''': args.label_list,
														    '''output_dir''': current_output_dir,
														    '''eval_metric''': args.eval_metric,
														    '''evaluation_strategy''': args.evaluation_strategy,
														    '''early_stopping_patience''': args.early_stopping_patience,
														    '''early_stopping_threshold''': args.early_stopping_threshold,
														    '''seed''': args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																												arguments_dict.update({key: value}   )

														__lowerCamelCase      :      List[Any]         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''best-checkpoint'''    ,						SCREAMING_SNAKE_CASE   )
														if os.path.exists(SCREAMING_SNAKE_CASE   ):
																					logger.info(
																					    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						)
														else:
																					logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''    ,						SCREAMING_SNAKE_CASE   )
																					finetune(**SCREAMING_SNAKE_CASE   )
																					accelerator.wait_for_everyone()
																					assert os.path.exists(SCREAMING_SNAKE_CASE   )
																					logger.info('''Self-training job completed: iteration: %d, stage: 1.'''    ,						SCREAMING_SNAKE_CASE   )

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					__lowerCamelCase      :      Optional[Any]         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''best-checkpoint'''   )
																					__lowerCamelCase      :      str         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''stage-2'''   )
																					# Update arguments_dict
																					__lowerCamelCase      :      Union[str, Any]         =model_path
																					__lowerCamelCase      :      List[str]         =data_files['''train''']
																					__lowerCamelCase      :      Dict         =current_output_dir

																					__lowerCamelCase      :      Dict         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''best-checkpoint'''    ,						SCREAMING_SNAKE_CASE   )
																					if os.path.exists(SCREAMING_SNAKE_CASE   ):
																												logger.info(
																												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						)
																					else:
																												logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''    ,						SCREAMING_SNAKE_CASE   )
																												finetune(**SCREAMING_SNAKE_CASE   )
																												accelerator.wait_for_everyone()
																												assert os.path.exists(SCREAMING_SNAKE_CASE   )
																												logger.info('''Self-training job completed: iteration: %d, stage: 2.'''    ,						SCREAMING_SNAKE_CASE   )

														__lowerCamelCase      :      str         =iteration
														__lowerCamelCase      :      Union[str, Any]         =data_dir_format(iteration + 1   )

														__lowerCamelCase      :      Dict         =AutoConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE    ,						'''best-checkpoint'''   )   )
														__lowerCamelCase      :      Optional[Any]         =config.idalabel
														__lowerCamelCase      :      Any         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''eval_results_best-checkpoint.json'''   )
														__lowerCamelCase      :      int         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''test_results_best-checkpoint.json'''   )
														assert os.path.exists(SCREAMING_SNAKE_CASE   )

														with open(SCREAMING_SNAKE_CASE    ,						'''r'''   ) as f:
																					__lowerCamelCase      :      Tuple         =float(json.load(SCREAMING_SNAKE_CASE   )[args.eval_metric]   )
														__lowerCamelCase      :      str         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''infer_output_best-checkpoint.csv'''   )
														assert os.path.exists(SCREAMING_SNAKE_CASE   )
														# Loading the dataset from local csv or json files.
														__lowerCamelCase      :      List[Any]         =load_dataset(args.data_file_extension    ,						data_files={'''data''': data_files['''infer''']}   )['''data''']
														__lowerCamelCase      :      Tuple         =load_dataset('''csv'''    ,						data_files={'''data''': infer_output_file}   )['''data''']

														if accelerator.is_main_process:
																					os.makedirs(SCREAMING_SNAKE_CASE    ,						exist_ok=SCREAMING_SNAKE_CASE   )
																					shutil.copy(SCREAMING_SNAKE_CASE    ,						os.path.join(SCREAMING_SNAKE_CASE    ,						F'eval_results_iter-{iteration}.json'   )   )
																					if os.path.exists(SCREAMING_SNAKE_CASE   ):
																												shutil.copy(SCREAMING_SNAKE_CASE    ,						os.path.join(SCREAMING_SNAKE_CASE    ,						F'test_results_iter-{iteration}.json'   )   )
																					create_pseudo_labeled_data(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														accelerator.wait_for_everyone()

														__lowerCamelCase      :      Optional[Any]         =os.path.join(SCREAMING_SNAKE_CASE    ,						F'train_pseudo.{args.data_file_extension}'   )

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					__lowerCamelCase      :      List[Any]         =eval_result

																					if best_iteration is None:
																												__lowerCamelCase      :      List[str]         =new_iteration
																												__lowerCamelCase      :      Tuple         =new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			__lowerCamelCase      :      Any         =new_iteration
																																			__lowerCamelCase      :      int         =new_eval_result
																																			__lowerCamelCase      :      Tuple         =0
																												else:
																																			if new_eval_result == best_eval_result:
																																										__lowerCamelCase      :      Tuple         =new_iteration
																																										__lowerCamelCase      :      str         =new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			__lowerCamelCase      :      str         =True

														progress_bar.update(1   )

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info('''Best iteration: %d'''    ,						SCREAMING_SNAKE_CASE   )
														logger.info('''Best evaluation result: %s = %f'''    ,						args.eval_metric    ,						SCREAMING_SNAKE_CASE   )
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(SCREAMING_SNAKE_CASE    ,						F'eval_results_iter-{iteration}.json'   )    ,						os.path.join(SCREAMING_SNAKE_CASE    ,						'''eval_results_best-iteration.json'''   )    ,						)
							else:
														# Assume that the last iteration is the best
														logger.info('''Best iteration: %d'''    ,						args.max_selftrain_iterations - 1   )
														logger.info('''Best evaluation result: %s = %f'''    ,						args.eval_metric    ,						SCREAMING_SNAKE_CASE   )
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(SCREAMING_SNAKE_CASE    ,						F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'   )    ,						os.path.join(SCREAMING_SNAKE_CASE    ,						'''eval_results_best-iteration.json'''   )    ,						)











"""simple docstring"""





import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Any         =Vector([1, 2, 3]			)
											self.assertEqual(x.component(0			)						,    1			)
											self.assertEqual(x.component(2			)						,    3			)
											__lowerCamelCase      :      Any         =Vector()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Tuple         =Vector([0, 0, 0, 0, 0, 1]			)
											self.assertEqual(str(__lowercase			)						,    '''(0,0,0,0,0,1)'''			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      int         =Vector([1, 2, 3, 4]			)
											self.assertEqual(len(__lowercase			)						,    4			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Optional[Any]         =Vector([1, 2]			)
											__lowerCamelCase      :      Dict         =Vector([1, 2, 3, 4, 5]			)
											__lowerCamelCase      :      List[Any]         =Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]			)
											__lowerCamelCase      :      int         =Vector([1, -1, 1, -1, 2, -3, 4, -5]			)
											self.assertAlmostEqual(x.euclidean_length()						,    2.236						,    3			)
											self.assertAlmostEqual(y.euclidean_length()						,    7.416						,    3			)
											self.assertEqual(z.euclidean_length()						,    0			)
											self.assertAlmostEqual(w.euclidean_length()						,    7.616						,    3			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Tuple         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      Any         =Vector([1, 1, 1]			)
											self.assertEqual((x + y).component(0			)						,    2			)
											self.assertEqual((x + y).component(1			)						,    3			)
											self.assertEqual((x + y).component(2			)						,    4			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      Union[str, Any]         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      int         =Vector([1, 1, 1]			)
											self.assertEqual((x - y).component(0			)						,    0			)
											self.assertEqual((x - y).component(1			)						,    1			)
											self.assertEqual((x - y).component(2			)						,    2			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      List[Any]         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      List[Any]         =Vector([2, -1, 4]			)  # for test of dot product
											__lowerCamelCase      :      Any         =Vector([1, -2, -1]			)
											self.assertEqual(str(x * 3.0			)						,    '''(3.0,6.0,9.0)'''			)
											self.assertEqual((a * b)						,    0			)




				def 					__lowercase  (     self  :List[Any]			):
											self.assertEqual(str(zero_vector(10			)			).count('''0'''			)						,    10			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											self.assertEqual(str(unit_basis_vector(3						,    1			)			)						,    '''(0,1,0)'''			)




				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      Any         =Vector([1, 2, 3]			)
											__lowerCamelCase      :      Optional[int]         =Vector([1, 0, 1]			)
											self.assertEqual(str(axpy(2						,    __lowercase						,    __lowercase			)			)						,    '''(3,4,7)'''			)




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      List[Any]         =Vector([1, 0, 0, 0, 0, 0]			)
											__lowerCamelCase      :      Optional[int]         =x.copy()
											self.assertEqual(str(__lowercase			)						,    str(__lowercase			)			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      str         =Vector([1, 0, 0]			)
											x.change_component(0						,    0			)
											x.change_component(1						,    1			)
											self.assertEqual(str(__lowercase			)						,    '''(0,1,0)'''			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Any         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n'''						,    str(__lowercase			)			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Tuple         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      List[Any]         =[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
											for x in range(a.height()			):
																		for y in range(a.width()			):
																									self.assertEqual(minors[x][y]						,    a.minor(__lowercase						,    __lowercase			)			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      Optional[Any]         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      Tuple         =[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
											for x in range(a.height()			):
																		for y in range(a.width()			):
																									self.assertEqual(cofactors[x][y]						,    a.cofactor(__lowercase						,    __lowercase			)			)




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Tuple         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											self.assertEqual(-5						,    a.determinant()			)




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Union[str, Any]         =Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]						,    3						,    3			)
											__lowerCamelCase      :      Tuple         =Vector([1, 2, 3]			)
											self.assertEqual('''(14,32,50)'''						,    str(a * x			)			)
											self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n'''						,    str(a * 2			)			)




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[int]         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											a.change_component(0						,    2						,    5			)
											self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n'''						,    str(__lowercase			)			)




				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											self.assertEqual(7						,    a.component(2						,    1			)						,    0.01			)




				def 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      List[str]         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      List[str]         =Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3			)
											self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''						,    str(a + b			)			)




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      int         =Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]						,    3						,    3			)
											__lowerCamelCase      :      Optional[int]         =Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]						,    3						,    3			)
											self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''						,    str(a - b			)			)




				def 					__lowercase  (     self  :Any			):
											self.assertEqual(
											    '''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'''						,    str(square_zero_matrix(5			)			)						,    )


if __name__ == "__main__":
							unittest.main()




