



'''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 :      Any             =True
			__SCREAMING_SNAKE_CASE :      List[str]             ='p3dn.24xlarge'
			import smdistributed.dataparallel.tensorflow as sdp
else:
			__SCREAMING_SNAKE_CASE :      Any             =False



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Tuple							,							lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      Union[str, Any]							,							lowerCamelCase__   :      Tuple							,							lowerCamelCase__   :      str							,							lowerCamelCase__   :      str=None				):


			'''simple docstring'''







			A:      int														=   tqdm(lowerCamelCase__				)
			for i, batch in enumerate(lowerCamelCase__				):
						with tf.GradientTape() as tape:
									A							,		A:      Tuple														=   batch
									A:      Optional[Any]														=   model(lowerCamelCase__				)
									A:      int														=   loss(lowerCamelCase__							,							outputs.logits				)

						if SDP_ENABLED:
									A:      int														=   sdp.DistributedGradientTape(lowerCamelCase__							,							sparse_as_dense=lowerCamelCase__				)

						A:      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

			A:      List[str]														=   {"""loss""": loss_value.numpy()}
			return train_results



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[int]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      Union[str, Any]				):


			'''simple docstring'''







			A							,		A:      Dict														=   load_dataset("""imdb"""							,							split=["""train""", """test"""]				)

			# Preprocess train dataset
			A:      int														=   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"""]				)

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

			# Preprocess test dataset
			A:      Tuple														=   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"""]				)

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

			if SDP_ENABLED:
						A:      str														=   tf_train_dataset.shard(sdp.size()							,							sdp.rank()				)
						A:      Tuple														=   tf_test_dataset.shard(sdp.size()							,							sdp.rank()				)
			A:      Tuple														=   tf_train_dataset.batch(lowerCamelCase__							,							drop_remainder=lowerCamelCase__				)
			A:      Tuple														=   tf_test_dataset.batch(lowerCamelCase__							,							drop_remainder=lowerCamelCase__				)

			return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE :      Optional[int]             =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 :      Union[str, Any]             =parser.parse_known_args()

			# Set up logging
			__SCREAMING_SNAKE_CASE :      List[str]             =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 :      Optional[Any]             =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 :      Union[str, Any]             =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 :      Optional[int]             =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 :      Any             =tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
			__SCREAMING_SNAKE_CASE :      Tuple             =[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 :      Optional[int]             =time.time()
						__SCREAMING_SNAKE_CASE :      List[str]             =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 :      List[str]             =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 :      Any             =model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
						logger.info('*** Evaluate ***')

						__SCREAMING_SNAKE_CASE :      Any             =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'''
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


__SCREAMING_SNAKE_CASE :      List[Any]             =datasets.utils.logging.get_logger(__name__)


@dataclass
class   SCREAMING_SNAKE_CASE__				(   datasets.BuilderConfig					):
	"""simple docstring"""


	A__    :    Optional[datasets.Features]						=				None







class   SCREAMING_SNAKE_CASE__				(   datasets.ArrowBasedBuilder					):
	"""simple docstring"""


	A__    :    Optional[int]						=				ArrowConfig


	def      a__ (		self			) ->							Union[str, Any]:
				return datasets.DatasetInfo(features=self.config.features			)


	def      a__ (		self					,   A			) ->							Any:
				if not self.config.data_files:
							raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'			)
				A:      Tuple														=   dl_manager.download_and_extract(self.config.data_files			)
				if isinstance(A					,   (str, list, tuple)			):
							A:      Union[str, Any]														=   data_files
							if isinstance(A					,   A			):
										A:      Tuple														=   [files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							A:      str														=   [dl_manager.iter_files(A			) for file in files]
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,   gen_kwargs={"""files""": files}			)]
				A:      Union[str, Any]														=   []
				for split_name, files in data_files.items():
							if isinstance(A					,   A			):
										A:      List[str]														=   [files]
							# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
							A:      List[Any]														=   [dl_manager.iter_files(A			) for file in files]
							# Infer features is they are stoed in the arrow schema
							if self.info.features is None:
										for file in itertools.chain.from_iterable(A			):
													with open(A					,   """rb"""			) as f:
																A:      Dict														=   datasets.Features.from_arrow_schema(pa.ipc.open_stream(A			).schema			)
													break
							splits.append(datasets.SplitGenerator(name=A					,   gen_kwargs={"""files""": files}			)			)
				return splits


	def      a__ (		self					,   A			) ->							pa.Table:
				if self.info.features is not None:
							# more expensive cast to support nested features with keys in a different order
							# allows str <-> int/float or str to Audio for example
							A:      Dict														=   table_cast(A					,   self.info.features.arrow_schema			)
				return pa_table





	def      a__ (		self					,   A			) ->							Dict:
				for file_idx, file in enumerate(itertools.chain.from_iterable(A			)			):
							with open(A					,   """rb"""			) as f:
										try:
													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(A			)			):
																A:      Union[str, Any]														=   pa.Table.from_batches([record_batch]			)
																# Uncomment for debugging (will print the Arrow table size and elements)
																# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																yield f'{file_idx}_{batch_idx}', self._cast_table(A			)
										except ValueError as e:
													logger.error(f'Failed to read file \'{file}\' with error {type(A			)}: {e}'			)
													raise





