




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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=None   )							->						Tuple:

    """simple docstring"""


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

        if SDP_ENABLED:
            __UpperCamelCase			=    sdp.DistributedGradientTape(lowercase_       ,  sparse_as_dense=lowercase_   )

        __UpperCamelCase			=    tape.gradient(lowercase_       ,  model.trainable_variables   )
        opt.apply_gradients(zip(lowercase_       ,  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

    __UpperCamelCase			=    {'''loss''': loss_value.numpy()}
    return train_results




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    load_dataset('''imdb'''       ,  split=['''train''', '''test''']   )

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

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

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

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

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

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 a_ 		=  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"])

 a_		,     a_ 		=  parser.parse_known_args()

 # Set up logging
 a_ 		=  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()

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

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

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

  a_ 		=  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


_A      :     Tuple       	={
    '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Dict       	=['''ImageGPTFeatureExtractor''']
			_A      :     Union[str, Any]       	=['''ImageGPTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=[
			    '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ImageGPTForCausalImageModeling''',
			    '''ImageGPTForImageClassification''',
			    '''ImageGPTModel''',
			    '''ImageGPTPreTrainedModel''',
			    '''load_tf_weights_in_imagegpt''',
			]


if TYPE_CHECKING:
			from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
						from .image_processing_imagegpt import ImageGPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_imagegpt import (
						    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ImageGPTForCausalImageModeling,
						    ImageGPTForImageClassification,
						    ImageGPTModel,
						    ImageGPTPreTrainedModel,
						    load_tf_weights_in_imagegpt,
						)

else:
			import sys

			_A      :     str       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

