




'''simple docstring'''





import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
		_SCREAMING_SNAKE_CASE							:     Dict   	=				True
		_SCREAMING_SNAKE_CASE							:     Any   	=				"p3dn.24xlarge"
		import smdistributed.dataparallel.tensorflow as sdp
else:
		_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				False


def   UpperCamelCase_(			snake_case							: List[Any]							,       snake_case							: Dict							,       snake_case							: int							,       snake_case							: Any							,       snake_case							: Optional[int]							,       snake_case							: str							,       snake_case							: int=None  ):

							'''simple docstring'''






							snake_case_			      =   tqdm(snake_case  )
							for i, batch in enumerate(snake_case  ):
														with tf.GradientTape() as tape:
																					snake_case_      ,   snake_case_			      =   batch
																					snake_case_			      =   model(snake_case  )
																					snake_case_			      =   loss(snake_case							,       outputs.logits  )

														if SDP_ENABLED:
																					snake_case_			      =   sdp.DistributedGradientTape(snake_case							,       sparse_as_dense=snake_case  )

														snake_case_			      =   tape.gradient(snake_case							,       model.trainable_variables  )
														opt.apply_gradients(zip(snake_case							,       model.trainable_variables  )  )

														pbar.set_description(f'Loss: {loss_value:.4f}'  )

														if SDP_ENABLED and i == 0:
																					sdp.broadcast_variables(model.variables							,       root_rank=0  )
																					sdp.broadcast_variables(opt.variables()							,       root_rank=0  )

														if max_steps and i >= max_steps:
																					break

							snake_case_			      =   {"loss": loss_value.numpy()}
							return train_results


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: str							,       snake_case							: Union[str, Any]  ):

							'''simple docstring'''






							snake_case_      ,   snake_case_			      =   load_dataset("imdb"							,       split=["train", "test"]  )

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

							snake_case_			      =   {
							    x: train_dataset[x].to_tensor(default_value=0							,       shape=[None, tokenizer.model_max_length]  )
							    for x in ["input_ids", "attention_mask"]
							}
							snake_case_			      =   tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])  )

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

							snake_case_			      =   {
							    x: test_dataset[x].to_tensor(default_value=0							,       shape=[None, tokenizer.model_max_length]  )
							    for x in ["input_ids", "attention_mask"]
							}
							snake_case_			      =   tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])  )

							if SDP_ENABLED:
														snake_case_			      =   tf_train_dataset.shard(sdp.size()							,       sdp.rank()  )
														snake_case_			      =   tf_test_dataset.shard(sdp.size()							,       sdp.rank()  )
							snake_case_			      =   tf_train_dataset.batch(snake_case							,       drop_remainder=snake_case  )
							snake_case_			      =   tf_test_dataset.batch(snake_case							,       drop_remainder=snake_case  )

							return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:     str   	=				argparse.ArgumentParser()

		# Hyperparameters sent by the client are passed as command-line arguments to the script.
		parser.add_argument("--epochs", type=int, default=3)
		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)
		parser.add_argument("--max_steps", type=int, default=None)

		# Data, model, and output directories
		parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
		parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
		parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])

		_SCREAMING_SNAKE_CASE							:     List[Any]   	=				parser.parse_known_args()

		# Set up logging
		_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				logging.getLogger(__name__)

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

		if SDP_ENABLED:
				sdp.init()

				_SCREAMING_SNAKE_CASE							:     str   	=				tf.config.experimental.list_physical_devices("GPU")
				for gpu in gpus:
						tf.config.experimental.set_memory_growth(gpu, True)
				if gpus:
						tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")

    # Load model and tokenizer
		_SCREAMING_SNAKE_CASE							:     Tuple   	=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				AutoTokenizer.from_pretrained(args.model_name_or_path)

		# get datasets
		_SCREAMING_SNAKE_CASE							:     Tuple   	=				get_datasets(
		    tokenizer=tokenizer,
		    train_batch_size=args.per_device_train_batch_size,
		    eval_batch_size=args.per_device_eval_batch_size,
		)

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

		# Training
		if args.do_train:
				# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
				_SCREAMING_SNAKE_CASE							:     str   	=				time.time()
				_SCREAMING_SNAKE_CASE							:     Dict   	=				fit(
				    model,
				    loss,
				    optimizer,
				    tf_train_dataset,
				    args.epochs,
				    args.per_device_train_batch_size,
				    max_steps=args.max_steps,
				)
				_SCREAMING_SNAKE_CASE							:     str   	=				time.time() - start_train_time
				logger.info("*** Train ***")
				logger.info(F"train_runtime = {end_train_time}")

				_SCREAMING_SNAKE_CASE							:     Any   	=				os.path.join(args.output_dir, "train_results.txt")

				if not SDP_ENABLED or sdp.rank() == 0:
						with open(output_eval_file, "w") as writer:
								logger.info("***** Train results *****")
								logger.info(train_results)
								for key, value in train_results.items():
										logger.info(F"  {key} = {value}")
										writer.write(F"{key} = {value}\n")

    # Evaluation
		if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
				_SCREAMING_SNAKE_CASE							:     List[Any]   	=				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
				logger.info("*** Evaluate ***")

				_SCREAMING_SNAKE_CASE							:     str   	=				os.path.join(args.output_dir, "eval_results.txt")

				with open(output_eval_file, "w") as writer:
						logger.info("***** Eval results *****")
						logger.info(result)
						for key, value in result.items():
								logger.info(F"  {key} = {value}")
								writer.write(F"{key} = {value}\n")

    # Save result
		if SDP_ENABLED:
				if sdp.rank() == 0:
						model.save_pretrained(args.output_dir)
						tokenizer.save_pretrained(args.output_dir)
		else:
				model.save_pretrained(args.output_dir)
				tokenizer.save_pretrained(args.output_dir)






'''simple docstring'''





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:     Optional[Any]   	=				[
		    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MobileViTV2ForImageClassification",
		    "MobileViTV2ForSemanticSegmentation",
		    "MobileViTV2Model",
		    "MobileViTV2PreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileViTVaConfig,
		    MobileViTVaOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTVaForImageClassification,
				    MobileViTVaForSemanticSegmentation,
				    MobileViTVaModel,
				    MobileViTVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE							:     Any   	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




