


"""simple docstring"""



import argparse
import logging
import os
import re

import tensorflow as tf

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


UpperCAmelCase      		=			logging.getLogger(__name__)

UpperCAmelCase      		=			tf.data.AUTOTUNE






def       lowerCamelCase					():
       lowercase		:int       							=				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=512				,				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.''')

       lowercase		:Any       							=				parser.parse_args()
       return args






def       lowerCamelCase					(a_						:Tuple):
       try:
              if args.tpu_name:
                     lowercase		:Any       							=				tf.distribute.cluster_resolver.TPUClusterResolver(
                         args.tpu_name				,				zone=args.tpu_zone				,				project=args.gcp_project)
              else:
                     lowercase		:Union[str, Any]       							=				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       lowerCamelCase					(a_						:Tuple):
       lowercase		:List[Any]       							=				0
       for file in file_list:
              lowercase		:Optional[int]       							=				file.split('''/''')[-1]
              lowercase		:List[str]       							=				re.search(R'''-\d+-(\d+)\.tfrecord'''				,				a_).group(1)
              lowercase		:Optional[int]       							=				int(a_)
              num_samples += sample_count

       return num_samples






def       lowerCamelCase					(a_						:Dict				,				a_						:List[Any]				,				a_						:Tuple				,				a_						:Union[str, Any]				,				a_						:Union[str, Any]				,				a_						:List[Any]=None):
       lowercase		:Optional[int]       							=				count_samples(a_)
       lowercase		:List[Any]       							=				tf.data.Dataset.from_tensor_slices(a_)
       if shuffle:
              lowercase		:Union[str, Any]       							=				dataset.shuffle(len(a_))
       lowercase		:Dict       							=				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
       lowercase		:Optional[Any]       							=				dataset.apply(tf.data.experimental.assert_cardinality(a_))
       lowercase		:int       							=				dataset.map(a_				,				num_parallel_calls=a_)
       if shuffle:
              assert shuffle_buffer_size is not None
              lowercase		:Union[str, Any]       							=				dataset.shuffle(args.shuffle_buffer_size)
       lowercase		:List[str]       							=				dataset.batch(a_				,				drop_remainder=a_)
       lowercase		:List[Any]       							=				dataset.map(a_				,				num_parallel_calls=a_)
       lowercase		:List[str]       							=				dataset.prefetch(a_)
       return dataset






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

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

       lowercase		:Optional[Any]       							=				AutoTokenizer.from_pretrained(args.tokenizer)
       lowercase		:Any       							=				AutoConfig.from_pretrained(args.pretrained_model_config)
       lowercase		:str       							=				tokenizer.vocab_size

       lowercase		:int       							=				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}.""")
       lowercase		: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}.""")

       lowercase		:Any       							=				count_samples(a_)

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

       with strategy.scope():
              lowercase		:List[Any]       							=				TFAutoModelForMaskedLM.from_config(a_)
              model(model.dummy_inputs)  # Pass some dummy inputs through the model to ensure all the weights are built
              lowercase		:Optional[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_						:Any):
              lowercase		:int       							=				{
                  '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.
       lowercase		:Optional[int]       							=				DataCollatorForLanguageModeling(
           tokenizer=a_				,				mlm_probability=args.mlm_probability				,				mlm=a_				,				return_tensors='''tf''')

       def mask_with_collator(a_						:int):
              # TF really needs an isin() function
              lowercase		:Union[str, Any]       							=				(
                  ~tf.cast(batch['''attention_mask''']				,				tf.bool)
                  | (batch['input_ids'] == tokenizer.cls_token_id)
                  | (batch['input_ids'] == tokenizer.sep_token_id)
              )
              lowercase		:List[str]       							=				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

       lowercase		:Union[str, Any]       							=				args.per_replica_batch_size * strategy.num_replicas_in_sync

       lowercase		:str       							=				prepare_dataset(
           a_				,				decode_fn=a_				,				mask_fn=a_				,				batch_size=a_				,				shuffle=a_				,				shuffle_buffer_size=args.shuffle_buffer_size				,				)

       lowercase		:List[str]       							=				prepare_dataset(
           a_				,				decode_fn=a_				,				mask_fn=a_				,				batch_size=a_				,				shuffle=a_				,				)

       lowercase		:List[str]       							=				[]
       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__":
 UpperCAmelCase      		=			parse_args()
 main(args)





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase      		=			{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3TokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LayoutLMv3ForQuestionAnswering''',
     '''LayoutLMv3ForSequenceClassification''',
     '''LayoutLMv3ForTokenClassification''',
     '''LayoutLMv3Model''',
     '''LayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFLayoutLMv3ForQuestionAnswering''',
     '''TFLayoutLMv3ForSequenceClassification''',
     '''TFLayoutLMv3ForTokenClassification''',
     '''TFLayoutLMv3Model''',
     '''TFLayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3FeatureExtractor''']
 UpperCAmelCase      		=			['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

 UpperCAmelCase      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

