


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():
	_lowerCAmelCase:	Dict      							=					True
	_lowerCAmelCase:	Optional[int]      							=					'p3dn.24xlarge'
	import smdistributed.dataparallel.tensorflow as sdp
else:
	_lowerCAmelCase:	Union[str, Any]      							=					False






def      _lowercase(				__a							:	Dict						,    __a							:	Optional[int]						,    __a							:	Dict						,    __a							:	Tuple						,    __a							:	Tuple						,    __a							:	Any						,    __a							:	List[Any]=None     ):
							a__     						=tqdm(__a     )
							for i, batch in enumerate(__a     ):
														with tf.GradientTape() as tape:
																					a__						,    a__     						=batch
																					a__     						=model(__a     )
																					a__     						=loss(__a						,    outputs.logits     )

														if SDP_ENABLED:
																					a__     						=sdp.DistributedGradientTape(__a						,    sparse_as_dense=__a     )

														a__     						=tape.gradient(__a						,    model.trainable_variables     )
														opt.apply_gradients(zip(__a						,    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__     						={'loss': loss_value.numpy()}
							return train_results






def      _lowercase(				__a							:	Optional[Any]						,    __a							:	Optional[Any]						,    __a							:	List[Any]     ):
							# Load dataset
							a__						,    a__     						=load_dataset('imdb'						,    split=['train', 'test']     )

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

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

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

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

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

							return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
	_lowerCAmelCase:	Union[str, Any]      							=					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'])

	_lowerCAmelCase:	Optional[Any]      							=					parser.parse_known_args()

	# Set up logging
	_lowerCAmelCase:	Tuple      							=					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()

		_lowerCAmelCase:	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
	_lowerCAmelCase:	Tuple      							=					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	_lowerCAmelCase:	Union[str, Any]      							=					AutoTokenizer.from_pretrained(args.model_name_or_path)

	# get datasets
	_lowerCAmelCase:	List[str]      							=					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
	_lowerCAmelCase:	Tuple      							=					tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
	_lowerCAmelCase:	List[str]      							=					tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
	_lowerCAmelCase:	List[str]      							=					[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)
		_lowerCAmelCase:	List[str]      							=					time.time()
		_lowerCAmelCase:	List[Any]      							=					fit(
		    model,
		    loss,
		    optimizer,
		    tf_train_dataset,
		    args.epochs,
		    args.per_device_train_batch_size,
		    max_steps=args.max_steps,
		)
		_lowerCAmelCase:	Optional[int]      							=					time.time() - start_train_time
		logger.info('*** Train ***')
		logger.info(F"""train_runtime = {end_train_time}""")

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

		_lowerCAmelCase:	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)





from typing import Dict, Optional, Tuple

import torch






def      _lowercase(				__a							:	torch.Tensor    ):
							a__     						=boundaries[1] - boundaries[0]
							a__     						=boundaries + step / 2
							a__     						=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1    )]						,    dim=0    )
							return bin_centers






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	torch.Tensor						,    ):
							a__     						=_calculate_bin_centers(__a    )
							return (
							    torch.sum(aligned_distance_error_probs * bin_centers						,    dim=-1    ),
							    bin_centers[-1],
							)






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	int = 31						,    __a							:	int = 64						,    **__a							:	List[str]						,    ):
							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )
							a__						,    a__     						=_calculate_expected_aligned_error(
							    alignment_confidence_breaks=__a						,    aligned_distance_error_probs=__a						,    )

							return {
							    "aligned_confidence_probs": aligned_confidence_probs,
							    "predicted_aligned_error": predicted_aligned_error,
							    "max_predicted_aligned_error": max_predicted_aligned_error,
							}







def      _lowercase(				__a							:	torch.Tensor						,    __a							:	Optional[torch.Tensor] = None						,    __a							:	int = 31						,    __a							:	int = 64						,    __a							:	float = 1e-8						,    **__a							:	str						,    ):
							if residue_weights is None:
														a__     						=logits.new_ones(logits.shape[-2]    )

							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=_calculate_bin_centers(__a    )
							torch.sum(__a    )
							a__     						=logits.shape[-2]
							a__     						=max(__a						,    19    )

							a__     						=1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )

							a__     						=1.0 / (1 + (bin_centers**2) / (da**2))
							a__     						=torch.sum(probs * tm_per_bin						,    dim=-1    )

							a__     						=residue_weights / (eps + residue_weights.sum())
							a__     						=torch.sum(predicted_tm_term * normed_residue_mask						,    dim=-1    )

							a__     						=per_alignment * residue_weights

							a__     						=(weighted == torch.max(__a    )).nonzero()[0]
							return per_alignment[tuple(__a    )]

