

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_						=					True
					SCREAMING_SNAKE_CASE_						=					"p3dn.24xlarge"
					import smdistributed.dataparallel.tensorflow as sdp
else:
					SCREAMING_SNAKE_CASE_						=					False

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Dict     ,					lowerCAmelCase:     List[Any]     ,					lowerCAmelCase:     List[str]     ,					lowerCAmelCase:     List[Any]     ,					lowerCAmelCase:     List[Any]     ,					lowerCAmelCase:     int     ,					lowerCAmelCase:     List[str]=None       )      ->				str:
				_UpperCAmelCase				:						List[Any]       						=			tqdm(_lowerCamelCase       )
				for i, batch in enumerate(_lowerCamelCase       ):
								with tf.GradientTape() as tape:
												_UpperCAmelCase				:						str       						=			batch
												_UpperCAmelCase				:						int       						=			model(_lowerCamelCase       )
												_UpperCAmelCase				:						List[Any]       						=			loss(_lowerCamelCase     ,					outputs.logits       )

								if SDP_ENABLED:
												_UpperCAmelCase				:						Union[str, Any]       						=			sdp.DistributedGradientTape(_lowerCamelCase     ,					sparse_as_dense=_lowerCamelCase       )

								_UpperCAmelCase				:						List[str]       						=			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

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

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int     ,					lowerCAmelCase:     Any     ,					lowerCAmelCase:     Tuple       )      ->				Dict:
				# Load dataset
				_UpperCAmelCase				:						Any       						=			load_dataset("imdb"     ,					split=["train", "test"]       )

				# Preprocess train dataset
				_UpperCAmelCase				:						Tuple       						=			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"]       )

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

				# Preprocess test dataset
				_UpperCAmelCase				:						Optional[int]       						=			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"]       )

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

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

				return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					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_						=					parser.parse_known_args()

					# Set up logging
					SCREAMING_SNAKE_CASE_						=					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_						=					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_						=					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					SCREAMING_SNAKE_CASE_						=					AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

										SCREAMING_SNAKE_CASE_						=					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


A_			: Dict				=			{"configuration_qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Tuple				=			[
          "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "QDQBertForMaskedLM",
          "QDQBertForMultipleChoice",
          "QDQBertForNextSentencePrediction",
          "QDQBertForQuestionAnswering",
          "QDQBertForSequenceClassification",
          "QDQBertForTokenClassification",
          "QDQBertLayer",
          "QDQBertLMHeadModel",
          "QDQBertModel",
          "QDQBertPreTrainedModel",
          "load_tf_weights_in_qdqbert",
      ]


if TYPE_CHECKING:
      from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_qdqbert import (
                QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                QDQBertForMaskedLM,
                QDQBertForMultipleChoice,
                QDQBertForNextSentencePrediction,
                QDQBertForQuestionAnswering,
                QDQBertForSequenceClassification,
                QDQBertForTokenClassification,
                QDQBertLayer,
                QDQBertLMHeadModel,
                QDQBertModel,
                QDQBertPreTrainedModel,
                load_tf_weights_in_qdqbert,
            )


else:
      import sys

      A_			: Tuple				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

