






'''simple docstring'''







import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
			_A      :     Optional[Any]       	=argparse.ArgumentParser()

			# Hyperparameters sent by the client are passed as command-line arguments to the script.
			parser.add_argument('''--epochs''', type=int, default=1)
			parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
			parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
			parser.add_argument('''--model_name_or_path''', type=str)
			parser.add_argument('''--learning_rate''', type=str, default=5e-5)
			parser.add_argument('''--do_train''', type=bool, default=True)
			parser.add_argument('''--do_eval''', type=bool, default=True)
			parser.add_argument('''--output_dir''', type=str)

			_A	, _A      :     Union[str, Any]       	=parser.parse_known_args()

			# overwrite batch size until we have tf_glue.py
			_A      :     Dict       	=16
			_A      :     Any       	=16

			# Set up logging
			_A      :     str       	=logging.getLogger(__name__)

			logging.basicConfig(
			    level=logging.getLevelName('''INFO'''),
			    handlers=[logging.StreamHandler(sys.stdout)],
			    format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
			)

			# Load model and tokenizer
			_A      :     Any       	=TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			_A      :     int       	=AutoTokenizer.from_pretrained(args.model_name_or_path)

			# Load dataset
			_A	, _A      :     int       	=load_dataset('''imdb''', split=['''train''', '''test'''])
			_A      :     Union[str, Any]       	=train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
			_A      :     str       	=test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

			# Preprocess train dataset
			_A      :     Dict       	=train_dataset.map(
			    lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
			)
			train_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

			_A      :     Dict       	={
			    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
			    for x in ['''input_ids''', '''attention_mask''']
			}
			_A      :     str       	=tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
			    args.per_device_train_batch_size
			)

			# Preprocess test dataset
			_A      :     Tuple       	=test_dataset.map(
			    lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
			)
			test_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

			_A      :     int       	={
			    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
			    for x in ['''input_ids''', '''attention_mask''']
			}
			_A      :     str       	=tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
			    args.per_device_eval_batch_size
			)

			# fine optimizer and loss
			_A      :     List[str]       	=tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
			_A      :     Optional[Any]       	=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
			_A      :     int       	=[tf.keras.metrics.SparseCategoricalAccuracy()]
			model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

			_A      :     Any       	=time.time()
			_A      :     Any       	=model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
			_A      :     List[str]       	=time.time() - start_train_time

			logger.info('''*** Train ***''')
			logger.info(F'train_runtime = {end_train_time}')
			for key, value in train_results.history.items():
						logger.info(F'  {key} = {value}')









'''simple docstring'''







import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Dict:
							assert isinstance(UpperCamelCase							,     UpperCamelCase )
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      int			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														lowerCamelCase__   :      Any			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase							,     keep_in_memory=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,     )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							lowerCamelCase__   :      Optional[int]			    =			tmp_path / """cache"""
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							lowerCamelCase__   :      int			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      int			    =			features.copy() if features else default_expected_features
							lowerCamelCase__   :      List[Any]			    =			(
							    Features({feature: Value(UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
							)
							lowerCamelCase__   :      Union[str, Any]			    =			CsvDatasetReader(UpperCamelCase							,     features=UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train""" ), """train""", """test"""] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Union[str, Any]:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Union[str, Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      List[Any]			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase							,     split=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )
							assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""							,     [str, list] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Union[str, Any]:
							if issubclass(UpperCamelCase							,     UpperCamelCase ):
														lowerCamelCase__   :      int			    =			csv_path
							elif issubclass(UpperCamelCase							,     UpperCamelCase ):
														lowerCamelCase__   :      Tuple			    =			[csv_path]
							lowerCamelCase__   :      Optional[int]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      str			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      Dict			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_dataset(UpperCamelCase							,     UpperCamelCase )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase=("train",) ) ->			int:
							assert isinstance(UpperCamelCase							,     UpperCamelCase )
							for split in splits:
														lowerCamelCase__   :      Optional[Any]			    =			dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 3
														assert dataset.column_names == ["col_1", "col_2", "col_3"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""							,     [False, True] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Optional[Any]:
							lowerCamelCase__   :      List[Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Dict			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														lowerCamelCase__   :      Any			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase							,     keep_in_memory=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize(
    """features"""							,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]							,     )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			int:
							lowerCamelCase__   :      int			    =			tmp_path / """cache"""
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							lowerCamelCase__   :      Optional[Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      List[str]			    =			features.copy() if features else default_expected_features
							lowerCamelCase__   :      Tuple			    =			(
							    Features({feature: Value(UpperCamelCase ) for feature, dtype in features.items()} ) if features is not None else None
							)
							lowerCamelCase__   :      str			    =			CsvDatasetReader({"""train""": csv_path}							,     features=UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase )





@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train""" ), """train""", """test"""] )
def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase ) ->			Any:
							if split:
														lowerCamelCase__   :      Tuple			    =			{split: csv_path}
							else:
														lowerCamelCase__   :      Union[str, Any]			    =			{"""train""": csv_path, """test""": csv_path}
							lowerCamelCase__   :      Dict			    =			tmp_path / """cache"""
							lowerCamelCase__   :      Union[str, Any]			    =			{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
							lowerCamelCase__   :      Tuple			    =			CsvDatasetReader(UpperCamelCase							,     cache_dir=UpperCamelCase ).read()
							_check_csv_datasetdict(UpperCamelCase							,     UpperCamelCase							,     splits=list(path.keys() ) )
							assert all(dataset[split].split == split for split in path.keys() )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase ) ->			Tuple:
							with open(UpperCamelCase							,     encoding="""utf-8""" ) as csvfile:
														yield from csv.reader(UpperCamelCase )





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Optional[int]:
							lowerCamelCase__   :      Union[str, Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[str]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Optional[Any]			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=1 ).write()

							lowerCamelCase__   :      Optional[Any]			    =			iter_csv_file(UpperCamelCase )
							lowerCamelCase__   :      str			    =			iter_csv_file(UpperCamelCase )

							for rowa, rowa in zip(UpperCamelCase							,     UpperCamelCase ):
														assert rowa == rowa





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			List[str]:
							lowerCamelCase__   :      Any			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[str]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Optional[int]			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=2 ).write()

							lowerCamelCase__   :      Tuple			    =			iter_csv_file(UpperCamelCase )
							lowerCamelCase__   :      Union[str, Any]			    =			iter_csv_file(UpperCamelCase )

							for rowa, rowa in zip(UpperCamelCase							,     UpperCamelCase ):
														assert rowa == rowa





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase ) ->			Optional[int]:
							lowerCamelCase__   :      List[Any]			    =			tmp_path / """cache"""
							lowerCamelCase__   :      List[Any]			    =			os.path.join(UpperCamelCase							,     """tmp.csv""" )
							lowerCamelCase__   :      Dict			    =			CsvDatasetReader({"""train""": csv_path}							,     cache_dir=UpperCamelCase ).read()
							with pytest.raises(UpperCamelCase ):
														CsvDatasetWriter(dataset["""train"""]							,     UpperCamelCase							,     num_proc=0 )

