






import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowerCamelCase						=		logging.getLogger(__name__)

lowerCamelCase						=		tf.data.AUTOTUNE






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[int]   					= argparse.ArgumentParser(description='''Train a masked language model on TPU.'''	)
		parser.add_argument(
		    '''--pretrained_model_config'''     ,       type=lowercase__     ,       default='''roberta-base'''     ,       help='''The model config to use. Note that we don\'t copy the model\'s weights, only the config!'''     ,       )
		parser.add_argument(
		    '''--tokenizer'''     ,       type=lowercase__     ,       default='''unigram-tokenizer-wikitext'''     ,       help='''The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'''     ,       )

		parser.add_argument(
		    '''--per_replica_batch_size'''     ,       type=lowercase__     ,       default=8     ,       help='''Batch size per TPU core.'''     ,       )

		parser.add_argument(
		    '''--no_tpu'''     ,       action='''store_true'''     ,       help='''If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'''     ,       )

		parser.add_argument(
		    '''--tpu_name'''     ,       type=lowercase__     ,       help='''Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'''     ,       default='''local'''     ,       )

		parser.add_argument(
		    '''--tpu_zone'''     ,       type=lowercase__     ,       help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''     ,       )

		parser.add_argument(
		    '''--gcp_project'''     ,       type=lowercase__     ,       help='''Google cloud project name. Only used for non-Colab TPU nodes.'''	)

		parser.add_argument(
		    '''--bfloat16'''     ,       action='''store_true'''     ,       help='''Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'''     ,       )

		parser.add_argument(
		    '''--train_dataset'''     ,       type=lowercase__     ,       help='''Path to training dataset to load. If the path begins with `gs://`'''
		    ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''     ,       )

		parser.add_argument(
		    '''--shuffle_buffer_size'''     ,       type=lowercase__     ,       default=2**18     ,       help='''Size of the shuffle buffer (in samples)'''     ,       )

		parser.add_argument(
		    '''--eval_dataset'''     ,       type=lowercase__     ,       help='''Path to evaluation dataset to load. If the path begins with `gs://`'''
		    ''' then the dataset will be loaded from a Google Cloud Storage bucket.'''     ,       )

		parser.add_argument(
		    '''--num_epochs'''     ,       type=lowercase__     ,       default=1     ,       help='''Number of epochs to train for.'''     ,       )

		parser.add_argument(
		    '''--learning_rate'''     ,       type=lowercase__     ,       default=1e-4     ,       help='''Learning rate to use for training.'''     ,       )

		parser.add_argument(
		    '''--weight_decay_rate'''     ,       type=lowercase__     ,       default=1e-3     ,       help='''Weight decay rate to use for training.'''     ,       )

		parser.add_argument(
		    '''--max_length'''     ,       type=lowercase__     ,       default=512     ,       help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''     ,       )

		parser.add_argument(
		    '''--mlm_probability'''     ,       type=lowercase__     ,       default=0.15     ,       help='''Fraction of tokens to mask during training.'''     ,       )

		parser.add_argument('''--output_dir'''     ,       type=lowercase__     ,       required=lowercase__     ,       help='''Path to save model checkpoints to.'''	)
		parser.add_argument('''--hub_model_id'''     ,       type=lowercase__     ,       help='''Model ID to upload to on the Hugging Face Hub.'''	)

		lowerCAmelCase__       :      int   					= parser.parse_args()
		return args






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		try:
				if args.tpu_name:
						lowerCAmelCase__       :      str   					= tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name     ,       zone=args.tpu_zone     ,       project=args.gcp_project	)
				else:
						lowerCAmelCase__       :      List[str]   					= tf.distribute.cluster_resolver.TPUClusterResolver()
		except ValueError:
				raise RuntimeError(
				    '''Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '''
				    '''--gcp_project. When running on a TPU VM, use --tpu_name local.'''	)

		tf.config.experimental_connect_to_cluster(lowercase__	)
		tf.tpu.experimental.initialize_tpu_system(lowercase__	)

		return tpu






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= 0
		for file in file_list:
				lowerCAmelCase__       :      Tuple   					= file.split('''/'''	)[-1]
				lowerCAmelCase__       :      Optional[int]   					= re.search(R'''-\d+-(\d+)\.tfrecord'''     ,       lowercase__	).group(1	)
				lowerCAmelCase__       :      Dict   					= int(lowercase__	)
				num_samples += sample_count

		return num_samples






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      str   					= count_samples(lowercase__	)
		lowerCAmelCase__       :      Optional[Any]   					= tf.data.Dataset.from_tensor_slices(lowercase__	)
		if shuffle:
				lowerCAmelCase__       :      Dict   					= dataset.shuffle(len(lowercase__	)	)
		lowerCAmelCase__       :      int   					= tf.data.TFRecordDataset(lowercase__     ,       num_parallel_reads=lowercase__	)
		# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
		lowerCAmelCase__       :      Dict   					= dataset.apply(tf.data.experimental.assert_cardinality(lowercase__	)	)
		lowerCAmelCase__       :      int   					= dataset.map(lowercase__     ,       num_parallel_calls=lowercase__	)
		if shuffle:
				assert shuffle_buffer_size is not None
				lowerCAmelCase__       :      str   					= dataset.shuffle(args.shuffle_buffer_size	)
		lowerCAmelCase__       :      Tuple   					= dataset.batch(lowercase__     ,       drop_remainder=lowercase__	)
		lowerCAmelCase__       :      str   					= dataset.map(lowercase__     ,       num_parallel_calls=lowercase__	)
		lowerCAmelCase__       :      Dict   					= dataset.prefetch(lowercase__	)
		return dataset






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		if not args.no_tpu:
				lowerCAmelCase__       :      Optional[int]   					= initialize_tpu(lowercase__	)
				lowerCAmelCase__       :      Any   					= tf.distribute.TPUStrategy(lowercase__	)
		else:
				lowerCAmelCase__       :      Tuple   					= tf.distribute.OneDeviceStrategy(device='''/gpu:0'''	)

		if args.bfloataa:
				tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''	)

		lowerCAmelCase__       :      int   					= AutoTokenizer.from_pretrained(args.tokenizer	)
		lowerCAmelCase__       :      Tuple   					= AutoConfig.from_pretrained(args.pretrained_model_config	)
		lowerCAmelCase__       :      Tuple   					= tokenizer.vocab_size

		lowerCAmelCase__       :      str   					= tf.io.gfile.glob(os.path.join(args.train_dataset     ,       '''*.tfrecord'''	)	)
		if not training_records:
				raise ValueError(f'No .tfrecord files found in {args.train_dataset}.'	)
		lowerCAmelCase__       :      str   					= tf.io.gfile.glob(os.path.join(args.eval_dataset     ,       '''*.tfrecord'''	)	)
		if not eval_records:
				raise ValueError(f'No .tfrecord files found in {args.eval_dataset}.'	)

		lowerCAmelCase__       :      List[str]   					= count_samples(lowercase__	)

		lowerCAmelCase__       :      List[Any]   					= num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
		lowerCAmelCase__       :      int   					= steps_per_epoch * args.num_epochs

		with strategy.scope():
				lowerCAmelCase__       :      List[Any]   					= TFAutoModelForMaskedLM.from_config(lowercase__	)
				model(model.dummy_inputs	)  # Pass some dummy inputs through the model to ensure all the weights are built
				lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= create_optimizer(
				    num_train_steps=lowercase__     ,       num_warmup_steps=total_train_steps // 20     ,       init_lr=args.learning_rate     ,       weight_decay_rate=args.weight_decay_rate     ,       )

				# Transformers models compute the right loss for their task by default when labels are passed, and will
				# use this for training unless you specify your own loss function in compile().
				model.compile(optimizer=lowercase__     ,       metrics=['''accuracy''']	)

		def decode_fn(_a	):
				lowerCAmelCase__       :      str   					= {
				    '''input_ids''': tf.io.FixedLenFeature(dtype=tf.intaa     ,       shape=(args.max_length,)	),
				    '''attention_mask''': tf.io.FixedLenFeature(dtype=tf.intaa     ,       shape=(args.max_length,)	),
				}
				return tf.io.parse_single_example(lowercase__     ,       lowercase__	)

		# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
		# use their methods in our data pipeline.
		lowerCAmelCase__       :      Optional[Any]   					= DataCollatorForLanguageModeling(
		    tokenizer=lowercase__     ,       mlm_probability=args.mlm_probability     ,       mlm=lowercase__     ,       return_tensors='''tf'''	)

		def mask_with_collator(_a	):
				# TF really needs an isin() function
				lowerCAmelCase__       :      Optional[int]   					= (
				    ~tf.cast(batch['''attention_mask''']     ,       tf.bool	)
				    | (batch['''input_ids'''] == tokenizer.cls_token_id)
				    | (batch['''input_ids'''] == tokenizer.sep_token_id)
				)
				lowerCAmelCase__					,     lowerCAmelCase__       :      str   					= data_collator.tf_mask_tokens(
				    batch['''input_ids''']     ,       vocab_size=len(lowercase__	)     ,       mask_token_id=tokenizer.mask_token_id     ,       special_tokens_mask=lowercase__     ,       )
				return batch

		lowerCAmelCase__       :      List[Any]   					= args.per_replica_batch_size * strategy.num_replicas_in_sync

		lowerCAmelCase__       :      Tuple   					= prepare_dataset(
		    lowercase__     ,       decode_fn=lowercase__     ,       mask_fn=lowercase__     ,       batch_size=lowercase__     ,       shuffle=lowercase__     ,       shuffle_buffer_size=args.shuffle_buffer_size     ,       )

		lowerCAmelCase__       :      int   					= prepare_dataset(
		    lowercase__     ,       decode_fn=lowercase__     ,       mask_fn=lowercase__     ,       batch_size=lowercase__     ,       shuffle=lowercase__     ,       )

		lowerCAmelCase__       :      int   					= []
		if args.hub_model_id:
				callbacks.append(
				    PushToHubCallback(output_dir=args.output_dir     ,       hub_model_id=args.hub_model_id     ,       tokenizer=lowercase__	)	)

		model.fit(
		    lowercase__     ,       validation_data=lowercase__     ,       epochs=args.num_epochs     ,       callbacks=lowercase__     ,       )

		model.save_pretrained(args.output_dir	)


if __name__ == "__main__":
		lowerCamelCase						=		parse_args()
		main(args)








from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .notes_encoder import SpectrogramNotesEncoder
		from .continous_encoder import SpectrogramContEncoder
		from .pipeline_spectrogram_diffusion import (
		    SpectrogramContEncoder,
		    SpectrogramDiffusionPipeline,
		    TaFilmDecoder,
		)

try:
		if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
		from .midi_utils import MidiProcessor




