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():
      UpperCamelCase													=		True
      UpperCamelCase													=		'''p3dn.24xlarge'''
      import smdistributed.dataparallel.tensorflow as sdp
else:
      UpperCamelCase													=		False

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Dict	,			_lowerCamelCase							: List[Any]	,			_lowerCamelCase							: int	,			_lowerCamelCase							: Tuple	,			_lowerCamelCase							: Dict	,			_lowerCamelCase							: Any=None):
    lowercase__				:     Union[str, Any]					      =       tqdm(_lowerCamelCase)
    for i, batch in enumerate(_lowerCamelCase):
        with tf.GradientTape() as tape:
            lowercase__      ,     lowercase__				:     Tuple					      =       batch
            lowercase__				:     Optional[int]					      =       model(_lowerCamelCase)
            lowercase__				:     int					      =       loss(_lowerCamelCase	,			outputs.logits)

        if SDP_ENABLED:
            lowercase__				:     List[Any]					      =       sdp.DistributedGradientTape(_lowerCamelCase	,			sparse_as_dense=_lowerCamelCase)

        lowercase__				:     Union[str, Any]					      =       tape.gradient(_lowerCamelCase	,			model.trainable_variables)
        opt.apply_gradients(zip(_lowerCamelCase	,			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

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

def     lowercase_       (    _lowerCamelCase							: List[Any]	,			_lowerCamelCase							: List[Any]	,			_lowerCamelCase							: int):
    # Load dataset
    lowercase__      ,     lowercase__				:     Dict					      =       load_dataset("imdb"	,			split=["train", "test"])

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

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

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

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

    if SDP_ENABLED:
        lowercase__				:     Optional[int]					      =       tf_train_dataset.shard(sdp.size()	,			sdp.rank())
        lowercase__				:     List[Any]					      =       tf_test_dataset.shard(sdp.size()	,			sdp.rank())
    lowercase__				:     List[str]					      =       tf_train_dataset.batch(_lowerCamelCase	,			drop_remainder=_lowerCamelCase)
    lowercase__				:     List[Any]					      =       tf_test_dataset.batch(_lowerCamelCase	,			drop_remainder=_lowerCamelCase)

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      UpperCamelCase													=		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'''])

      UpperCamelCase							,	UpperCamelCase													=		parser.parse_known_args()

      # Set up logging
      UpperCamelCase													=		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()

            UpperCamelCase													=		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
      UpperCamelCase													=		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      UpperCamelCase													=		AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      UpperCamelCase							,	UpperCamelCase													=		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
      UpperCamelCase													=		tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      UpperCamelCase													=		tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      UpperCamelCase													=		[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)
            UpperCamelCase													=		time.time()
            UpperCamelCase													=		fit(
                model,
                loss,
                optimizer,
                tf_train_dataset,
                args.epochs,
                args.per_device_train_batch_size,
                max_steps=args.max_steps,
            )
            UpperCamelCase													=		time.time() - start_train_time
            logger.info('''*** Train ***''')
            logger.info(f"train_runtime = {end_train_time}")

            UpperCamelCase													=		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):
            UpperCamelCase													=		model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info('''*** Evaluate ***''')

            UpperCamelCase													=		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)




from typing import TYPE_CHECKING

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


UpperCamelCase													=		{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''MobileNetV1FeatureExtractor''']
      UpperCamelCase													=		['''MobileNetV1ImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MobileNetV1ForImageClassification''',
          '''MobileNetV1Model''',
          '''MobileNetV1PreTrainedModel''',
          '''load_tf_weights_in_mobilenet_v1''',
      ]


if TYPE_CHECKING:
      from .configuration_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileNetVaConfig,
          MobileNetVaOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
            from .image_processing_mobilenet_va import MobileNetVaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilenet_va import (
                MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileNetVaForImageClassification,
                MobileNetVaModel,
                MobileNetVaPreTrainedModel,
                load_tf_weights_in_mobilenet_va,
            )


else:
      import sys

      UpperCamelCase													=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



