






import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								self.assertEqual(x.component(0     )       ,					1     )
								self.assertEqual(x.component(2     )       ,					3     )
								__magic_name__		:Tuple        =      Vector()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 1]     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,0,0,0,0,1)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      Vector([1, 2, 3, 4]     )
								self.assertEqual(len(__lowerCAmelCase     )       ,					4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      Vector([1, 2]     )
								__magic_name__		:int        =      Vector([1, 2, 3, 4, 5]     )
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[Any]        =      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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								__magic_name__		:List[str]        =      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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 2, 3]     )
								__magic_name__		:Union[str, Any]        =      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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Vector([1, 2, 3]     )
								__magic_name__		:Optional[int]        =      Vector([2, -1, 4]     )  # for test of dot product
								__magic_name__		:List[Any]        =      Vector([1, -2, -1]     )
								self.assertEqual(str(x * 3.0     )       ,					'''(3.0,6.0,9.0)'''     )
								self.assertEqual((a * b)       ,					0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(zero_vector(1_0     )     ).count('''0'''     )       ,					1_0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(unit_basis_vector(3       ,					1     )     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Vector([1, 2, 3]     )
								__magic_name__		:List[Any]        =      Vector([1, 0, 1]     )
								self.assertEqual(str(axpy(2       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )     )       ,					'''(3,4,7)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[int]        =      x.copy()
								self.assertEqual(str(__lowerCAmelCase     )       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0]     )
								x.change_component(0       ,					0     )
								x.change_component(1       ,					1     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		: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(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:List[str]        =      [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(minors[x][y]       ,					a.minor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Any        =      [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(cofactors[x][y]       ,					a.cofactor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(-5       ,					a.determinant()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       ,					3       ,					3     )
								__magic_name__		:Any        =      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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      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(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(7       ,					a.component(2       ,					1     )       ,					0.01     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Optional[Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''       ,					str(a + b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''       ,					str(a - b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								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()














import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case = None,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      {}

				if train_file is not None:
								__magic_name__		:List[Any]        =      [train_file]
				if eval_file is not None:
								__magic_name__		:int        =      [eval_file]
				if test_file is not None:
								__magic_name__		:Any        =      [test_file]

				__magic_name__		:Optional[Any]        =      datasets.load_dataset('''csv''',  data_files=snake_case )
				__magic_name__		:List[Any]        =      list(ds[list(files.keys() )[0]].features.keys() )
				__magic_name__		:Optional[int]        =      features_name.pop(snake_case )
				__magic_name__		:List[str]        =      list(set(ds[list(files.keys() )[0]][label_name] ) )
				__magic_name__		:List[Any]        =      {label: i for i, label in enumerate(snake_case )}
				__magic_name__		:Optional[Any]        =      tokenizer.model_input_names
				__magic_name__		:Any        =      {}

				if len(snake_case ) == 1:
								for k in files.keys():
												__magic_name__		:Union[str, Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        example[features_name[0]],  truncation=snake_case,  max_length=snake_case,  padding='''max_length''' ),  batched=snake_case,  )
				elif len(snake_case ) == 2:
								for k in files.keys():
												__magic_name__		:Optional[Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]]),  truncation=snake_case,  max_length=snake_case,  padding='''max_length''',  ),  batched=snake_case,  )

				def gen_train():
								for ex in transformed_ds[datasets.Split.TRAIN]:
												__magic_name__		:Union[str, Any]        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:int        =      labelaid[ex[label_name]]
												yield (d, label)

				def gen_val():
								for ex in transformed_ds[datasets.Split.VALIDATION]:
												__magic_name__		:Optional[Any]        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:Dict        =      labelaid[ex[label_name]]
												yield (d, label)

				def gen_test():
								for ex in transformed_ds[datasets.Split.TEST]:
												__magic_name__		:Any        =      {k: v for k, v in ex.items() if k in input_names}
												__magic_name__		:List[Any]        =      labelaid[ex[label_name]]
												yield (d, label)

				__magic_name__		:Optional[Any]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.TRAIN in transformed_ds
				    else None
				)

				if train_ds is not None:
								__magic_name__		:Optional[int]        =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

				__magic_name__		:Any        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.VALIDATION in transformed_ds
				    else None
				)

				if val_ds is not None:
								__magic_name__		:str        =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

				__magic_name__		:Optional[int]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({k: tf.intaa for k in input_names}, tf.intaa),  ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )),  )
				    if datasets.Split.TEST in transformed_ds
				    else None
				)

				if test_ds is not None:
								__magic_name__		:Dict        =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

				return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE__      :			List[Any]							=		logging.getLogger(__name__)
@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(metadata={'''help''': '''Which column contains the label'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the training file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the development file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the test file'''} )
				a__					       =  field(
				    default=1_28			,				metadata={
				        '''help''': (
				            '''The maximum total input sequence length after tokenization. Sequences longer '''
				            '''than this will be truncated, sequences shorter will be padded.'''
				        )
				    }			,				)
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,				)







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:str        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Any        =      parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir )
				    and os.listdir(training_args.output_dir )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
								    ''' --overwrite_output_dir to overcome.''' )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',  datefmt='''%m/%d/%Y %H:%M:%S''',  level=logging.INFO,  )
				logger.info(
				    f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '''
				    f'''16-bits training: {training_args.fpaa}''' )
				logger.info(f'''Training/evaluation parameters {training_args}''' )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__magic_name__		:Union[str, Any]        =      AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,  cache_dir=model_args.cache_dir,  )

				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:int        =      get_tfds(
				    train_file=data_args.train_file,  eval_file=data_args.dev_file,  test_file=data_args.test_file,  tokenizer=snake_case,  label_column_id=data_args.label_column_id,  max_seq_length=data_args.max_seq_length,  )

				__magic_name__		:Optional[Any]        =      AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,  num_labels=len(snake_case ),  labelaid=snake_case,  idalabel={id: label for label, id in labelaid.items()},  finetuning_task='''text-classification''',  cache_dir=model_args.cache_dir,  )

				with training_args.strategy.scope():
								__magic_name__		:Dict        =      TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path,  from_pt=bool('''.bin''' in model_args.model_name_or_path ),  config=snake_case,  cache_dir=model_args.cache_dir,  )

				def compute_metrics(snake_case ) -> Dict:
								__magic_name__		:str        =      np.argmax(p.predictions,  axis=1 )

								return {"acc": (preds == p.label_ids).mean()}

				# Initialize our Trainer
				__magic_name__		:str        =      TFTrainer(
				    model=snake_case,  args=snake_case,  train_dataset=snake_case,  eval_dataset=snake_case,  compute_metrics=snake_case,  )

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir )

				# Evaluation
				__magic_name__		:Optional[Any]        =      {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''' )
								__magic_name__		:int        =      trainer.evaluate()
								__magic_name__		:Dict        =      os.path.join(training_args.output_dir,  '''eval_results.txt''' )

								with open(snake_case,  '''w''' ) as writer:
												logger.info('''***** Eval results *****''' )

												for key, value in result.items():
																logger.info(f'''  {key} = {value}''' )
																writer.write(f'''{key} = {value}\n''' )

												results.update(snake_case )

				return results


if __name__ == "__main__":
					main()






