
"""simple docstring"""




from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if is_torch_available():
										import torch

										if isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor			):
															return True

					return isinstance(SCREAMING_SNAKE_CASE_,     np.ndarray			)




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def 							A					(				self			)    ->    List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	fields(self			)

									# Safety and consistency checks
									if not len(lowercase__			):
														raise ValueError(f'''{self.__class__.__name__} has no fields.'''			)

									SCREAMING_SNAKE_CASE				=	getattr(self		,	class_fields[0].name			)
									SCREAMING_SNAKE_CASE				=	all(getattr(self		,	field.name			) is None for field in class_fields[1:]			)

									if other_fields_are_none and isinstance(lowercase__		,	lowercase__			):
														for key, value in first_field.items():
																			SCREAMING_SNAKE_CASE				=	value
									else:
														for field in class_fields:
																			SCREAMING_SNAKE_CASE				=	getattr(self		,	field.name			)
																			if v is not None:
																								SCREAMING_SNAKE_CASE				=	v


				def __delitem__(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''			)


				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''			)


				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    List[str]:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''			)


				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""simple docstring"""







									raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''			)


				def __getitem__(				self		,	lowercase__			)    ->    Optional[Any]:
									"""simple docstring"""







									if isinstance(lowercase__		,	lowercase__			):
														SCREAMING_SNAKE_CASE				=	dict(self.items()			)
														return inner_dict[k]
									else:
														return self.to_tuple()[k]


				def __setattr__(				self		,	lowercase__		,	lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									if name in self.keys() and value is not None:
														# Don't call self.__setitem__ to avoid recursion errors
														super().__setitem__(lowercase__		,	lowercase__			)
									super().__setattr__(lowercase__		,	lowercase__			)


				def __setitem__(				self		,	lowercase__		,	lowercase__			)    ->    Any:
									"""simple docstring"""







									super().__setitem__(lowercase__		,	lowercase__			)
									# Don't call self.__setattr__ to avoid recursion errors
									super().__setattr__(lowercase__		,	lowercase__			)




				def 							A					(				self			)    ->    Tuple[Any]:
									"""simple docstring"""







									return tuple(self[k] for k in self.keys()			)





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        __a     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					int=None				)      -> Optional[int]:






						'''simple docstring'''



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

												if SDP_ENABLED:
																		UpperCAmelCase_=   sdp.DistributedGradientTape(lowerCAmelCase_			,sparse_as_dense=lowerCAmelCase_				)

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



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					str				)      -> str:






						'''simple docstring'''



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

						# Preprocess train dataset
						UpperCAmelCase_=   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_=   {
						    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 lowerCAmelCase_				: tokenizer(e["""text"""]			,truncation=lowerCAmelCase_			,padding="""max_length"""				)			,batched=lowerCAmelCase_				)
						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(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)
						UpperCAmelCase_=   tf_test_dataset.batch(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)

						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)




