'''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__":
 snake_case__	:     Tuple      =    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)

 snake_case__     , snake_case__	:     List[str]      =    parser.parse_known_args()

 # overwrite batch size until we have tf_glue.py
 snake_case__	:     Union[str, Any]      =    16
 snake_case__	:     Optional[Any]      =    16

 # Set up logging
 snake_case__	:     Optional[int]      =    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
 snake_case__	:     int      =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 snake_case__	:     str      =    AutoTokenizer.from_pretrained(args.model_name_or_path)

 # Load dataset
 snake_case__     , snake_case__	:     List[str]      =    load_dataset('''imdb''', split=['''train''', '''test'''])
 snake_case__	:     Union[str, Any]      =    train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
 snake_case__	:     Tuple      =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

 # Preprocess train dataset
 snake_case__	:     Tuple      =    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'''])

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

 # Preprocess test dataset
 snake_case__	:     List[str]      =    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'''])

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

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

 snake_case__	:     Union[str, Any]      =    time.time()
 snake_case__	:     Optional[int]      =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
 snake_case__	:     Optional[int]      =    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'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase					:						Dict					     =							{
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						List[str]					     =							[
				    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DonutSwinModel',
				    'DonutSwinPreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						Any					     =							['DonutFeatureExtractor']
				_lowerCamelCase					:						Union[str, Any]					     =							['DonutImageProcessor']


if TYPE_CHECKING:
				from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
				from .processing_donut import DonutProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_donut_swin import (
								    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DonutSwinModel,
								    DonutSwinPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_donut import DonutFeatureExtractor
								from .image_processing_donut import DonutImageProcessor

else:
				import sys

				_lowerCamelCase					:						Optional[int]					     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

