


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

def  lowerCAmelCase__				(    a__:	Union[str, Any]    ,						a__:	List[str]    ,						a__:	List[str]    ,						a__:	List[str]    ,						a__:	Any    ,						a__:	Optional[int]    ,						a__:	Tuple=None )						->	Any:


							'''simple docstring'''





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

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

														_UpperCAmelCase					=    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

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

def  lowerCAmelCase__				(    a__:	Union[str, Any]    ,						a__:	Optional[int]    ,						a__:	Optional[Any] )						->	Optional[int]:


							'''simple docstring'''





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

							# Preprocess train dataset
							_UpperCAmelCase					=    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'] )

							_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 a__ : tokenizer(e['text']    ,						truncation=a__    ,						padding='max_length' )    ,						batched=a__ )
							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(a__    ,						drop_remainder=a__ )
							_UpperCAmelCase					=    tf_test_dataset.batch(a__    ,						drop_remainder=a__ )

							return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
		lowerCAmelCase__						: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=1_6)
		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__    ,						lowerCAmelCase__						:str         =							parser.parse_known_args()

		# Set up logging
		lowerCAmelCase__						: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()

				lowerCAmelCase__						:List[str]         =							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__						:int         =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		lowerCAmelCase__						:Optional[Any]         =							AutoTokenizer.from_pretrained(args.model_name_or_path)

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

				lowerCAmelCase__						:int         =							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__						:List[str]         =							model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
				logger.info('''*** Evaluate ***''')

				lowerCAmelCase__						:Dict         =							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)




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
		from .tokenization_deberta_va import DebertaVaTokenizer
else:
		lowerCAmelCase__						:int         =							None

lowerCAmelCase__						:List[str]         =							logging.get_logger(__name__)

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[str]         =							{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						:Any         =							{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}

class 	__a   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(
									    _SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE    ,					token_ids_a=_SCREAMING_SNAKE_CASE    ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									if token_ids_a is None:
																return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> Tuple[str]:




									"""simple docstring"""





									if not self.can_save_slow_tokenizer:
																raise ValueError(
																    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																    'tokenizer.'		)

									if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		):
																copyfile(self.vocab_file    ,					_SCREAMING_SNAKE_CASE		)

									return (out_vocab_file,)
