

'''simple docstring'''




import argparse
import logging
import os
import re

import tensorflow as tf

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


_a					:			Tuple							=    logging.getLogger(__name__)

_a					:			Optional[Any]							=    tf.data.AUTOTUNE


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						argparse.ArgumentParser(description='Train a masked language model on TPU.'    )
  parser.add_argument(
      '--pretrained_model_config'							, type=SCREAMING_SNAKE_CASE							, 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=SCREAMING_SNAKE_CASE							, 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=SCREAMING_SNAKE_CASE							, 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=SCREAMING_SNAKE_CASE							, 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=SCREAMING_SNAKE_CASE							, help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'							, )

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

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

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

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

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

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

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

  UpperCAmelCase    				=						parser.parse_args()
  return args


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
  try:
    if args.tpu_name:
      UpperCAmelCase    				=						tf.distribute.cluster_resolver.TPUClusterResolver(
          args.tpu_name							, zone=args.tpu_zone							, project=args.gcp_project    )
    else:
      UpperCAmelCase    				=						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(SCREAMING_SNAKE_CASE    )
  tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE    )

  return tpu


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]    ):
  UpperCAmelCase    				=						0
  for file in file_list:
    UpperCAmelCase    				=						file.split('/'    )[-1]
    UpperCAmelCase    				=						re.search(R'-\d+-(\d+)\.tfrecord'							, SCREAMING_SNAKE_CASE    ).group(1    )
    UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
    num_samples += sample_count

  return num_samples


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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]    ):
  if not args.no_tpu:
    UpperCAmelCase    				=						initialize_tpu(SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE    )
  else:
    UpperCAmelCase    				=						tf.distribute.OneDeviceStrategy(device='/gpu:0'    )

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

  UpperCAmelCase    				=						AutoTokenizer.from_pretrained(args.tokenizer    )
  UpperCAmelCase    				=						AutoConfig.from_pretrained(args.pretrained_model_config    )
  UpperCAmelCase    				=						tokenizer.vocab_size

  UpperCAmelCase    				=						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}.'''    )
  UpperCAmelCase    				=						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}.'''    )

  UpperCAmelCase    				=						count_samples(SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
  UpperCAmelCase    				=						steps_per_epoch * args.num_epochs

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

  def decode_fn(SCREAMING_SNAKE_CASE   : Tuple    ):
    UpperCAmelCase    				=						{
        '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(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )

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

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

  UpperCAmelCase    				=						args.per_replica_batch_size * strategy.num_replicas_in_sync

  UpperCAmelCase    				=						prepare_dataset(
      SCREAMING_SNAKE_CASE							, decode_fn=SCREAMING_SNAKE_CASE							, mask_fn=SCREAMING_SNAKE_CASE							, batch_size=SCREAMING_SNAKE_CASE							, shuffle=SCREAMING_SNAKE_CASE							, shuffle_buffer_size=args.shuffle_buffer_size							, )

  UpperCAmelCase    				=						prepare_dataset(
      SCREAMING_SNAKE_CASE							, decode_fn=SCREAMING_SNAKE_CASE							, mask_fn=SCREAMING_SNAKE_CASE							, batch_size=SCREAMING_SNAKE_CASE							, shuffle=SCREAMING_SNAKE_CASE							, )

  UpperCAmelCase    				=						[]
  if args.hub_model_id:
    callbacks.append(
        PushToHubCallback(output_dir=args.output_dir							, hub_model_id=args.hub_model_id							, tokenizer=SCREAMING_SNAKE_CASE    )    )

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

  model.save_pretrained(args.output_dir    )


if __name__ == "__main__":
  _a					:			Any							=    parse_args()
  main(args)





'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)
    processor.save_pretrained(
        self.tmpdirname ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			speaker_embeddings_directory=self.speaker_embeddings_directory ,			)

    UpperCAmelCase    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        self.tmpdirname ,			self.speaker_embeddings_dict_path ,			bos_token='(BOS)' ,			eos_token='(EOS)' ,			)

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab()  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)  ),
        'fine_prompt': np.ones((nb_codebooks_total, seq_len)  ),
    }

    # test providing already loaded voice_preset
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from the hub
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=self.voice_preset  )





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key] ,			encoded_processor[key].squeeze().tolist()  )


