






'''simple docstring'''







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 		_UpperCAmelCase   (							):
				a_		:  Optional[Any]											=      argparse.ArgumentParser(description='''Train a masked language model on TPU.'''      )
				parser.add_argument(
				    '''--pretrained_model_config'''						,		type=__A						,		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=__A						,		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=__A						,		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=__A						,		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=__A						,		help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''						,		)

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

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

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

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

				parser.add_argument(
				    '''--max_length'''						,		type=__A						,		default=5_12						,		help='''Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'''						,		)

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

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

				a_		:  Optional[Any]											=      parser.parse_args()
				return args





def 		_UpperCAmelCase   (							__A							:   Dict      ):
				try:
								if args.tpu_name:
												a_		:  Any											=      tf.distribute.cluster_resolver.TPUClusterResolver(
												    args.tpu_name						,		zone=args.tpu_zone						,		project=args.gcp_project      )
								else:
												a_		:  Tuple											=      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(__A      )
				tf.tpu.experimental.initialize_tpu_system(__A      )

				return tpu





def 		_UpperCAmelCase   (							__A							:   int      ):
				a_		:  Tuple											=      0
				for file in file_list:
								a_		:  Optional[Any]											=      file.split('''/'''      )[-1]
								a_		:  Optional[int]											=      re.search(R'''-\d+-(\d+)\.tfrecord'''						,		__A      ).group(1      )
								a_		:  Optional[Any]											=      int(__A      )
								num_samples += sample_count

				return num_samples





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





def 		_UpperCAmelCase   (							__A							:   List[Any]      ):
				if not args.no_tpu:
								a_		:  Optional[int]											=      initialize_tpu(__A      )
								a_		:  Dict											=      tf.distribute.TPUStrategy(__A      )
				else:
								a_		:  Dict											=      tf.distribute.OneDeviceStrategy(device='''/gpu:0'''      )

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

				a_		:  List[Any]											=      AutoTokenizer.from_pretrained(args.tokenizer      )
				a_		:  Any											=      AutoConfig.from_pretrained(args.pretrained_model_config      )
				a_		:  Tuple											=      tokenizer.vocab_size

				a_		:  Tuple											=      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}.'      )
				a_		:  Optional[int]											=      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}.'      )

				a_		:  Any											=      count_samples(__A      )

				a_		:  Optional[Any]											=      num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
				a_		:  Union[str, Any]											=      steps_per_epoch * args.num_epochs

				with strategy.scope():
								a_		:  Optional[Any]											=      TFAutoModelForMaskedLM.from_config(__A      )
								model(model.dummy_inputs      )  # Pass some dummy inputs through the model to ensure all the weights are built
								a_		:  int											=      create_optimizer(
								    num_train_steps=__A						,		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=__A						,		metrics=['''accuracy''']      )

				def decode_fn(__A							:   Dict      ):
								a_		:  Tuple											=      {
								    '''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(__A						,		__A      )

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

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

				a_		:  List[str]											=      args.per_replica_batch_size * strategy.num_replicas_in_sync

				a_		:  Union[str, Any]											=      prepare_dataset(
				    __A						,		decode_fn=__A						,		mask_fn=__A						,		batch_size=__A						,		shuffle=__A						,		shuffle_buffer_size=args.shuffle_buffer_size						,		)

				a_		:  str											=      prepare_dataset(
				    __A						,		decode_fn=__A						,		mask_fn=__A						,		batch_size=__A						,		shuffle=__A						,		)

				a_		:  Dict											=      []
				if args.hub_model_id:
								callbacks.append(
								    PushToHubCallback(output_dir=args.output_dir						,		hub_model_id=args.hub_model_id						,		tokenizer=__A      )      )

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

				model.save_pretrained(args.output_dir      )


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












'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Tuple											=      UNetaDModel(
							    sample_size=(32, 64)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Any											=      UNetaDConditionModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')  ,						cross_attention_dim=10  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							torch.manual_seed(0					)
							a_		:  List[Any]											=      AutoencoderKL(
							    sample_size=(128, 64)  ,						in_channels=1  ,						out_channels=1  ,						latent_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')  ,						up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')  ,						)
							a_		:  List[Any]											=      UNetaDModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return vqvae, unet







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:  Union[str, Any]											=      Mel(
							    x_res=self.dummy_unet.config.sample_size[1]  ,						y_res=self.dummy_unet.config.sample_size[0]  ,						)

							a_		:  Any											=      DDPMScheduler()
							a_		:  str											=      AudioDiffusionPipeline(vqvae=__SCREAMING_SNAKE_CASE  ,						unet=self.dummy_unet  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[int]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4					)
							a_		:  List[Any]											=      output.audios[0]
							a_		:  Dict											=      output.images[0]

							a_		:  Dict											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  Optional[Any]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  str											=      np.frombuffer(image_from_tuple.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

							a_		:  str											=      Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]  ,						y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]  ,						)

							a_		:  int											=      DDIMScheduler()
							a_		:  Dict											=      self.dummy_vqvae_and_unet
							a_		:  List[str]											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=dummy_vqvae_and_unet[1]  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  List[str]											=      np.random.uniform(-1  ,						1  ,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
							a_		:  int											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  int											=      pipe(raw_audio=__SCREAMING_SNAKE_CASE  ,						generator=__SCREAMING_SNAKE_CASE  ,						start_step=5  ,						steps=10					)
							a_		:  List[str]											=      output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							a_		:  Optional[Any]											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

							a_		:  List[str]											=      self.dummy_unet_condition
							a_		:  Dict											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=__SCREAMING_SNAKE_CASE  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  Any											=      torch.rand((1, 1, 10)					)
							a_		:  Tuple											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						encoding=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.images[0]
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							a_		:  Any											=      torch_device

							a_		:  Optional[int]											=      DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''					)
							a_		:  Dict											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.audios[0]
							a_		:  Tuple											=      output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							a_		:  str											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  Tuple											=      np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0




