import argparse
import logging
import os
import re

import tensorflow as tf

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


snake_case__				= logging.getLogger(__name__)

snake_case__				= tf.data.AUTOTUNE
def       __magic_name__(  )						->    Optional[Any]:



      '''simple docstring'''

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

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

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

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

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

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

      parser.add_argument(
          '''--mlm_probability''' ,						type=__UpperCAmelCase ,						default=0.1_5 ,						help='''Fraction of tokens to mask during training.''' ,						)

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

      _lowerCamelCase			    =				parser.parse_args()
      return args
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      try:
            if args.tpu_name:
                  _lowerCamelCase			    =				tf.distribute.cluster_resolver.TPUClusterResolver(
                      args.tpu_name ,						zone=args.tpu_zone ,						project=args.gcp_project		)
            else:
                  _lowerCamelCase			    =				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(__UpperCAmelCase		)
      tf.tpu.experimental.initialize_tpu_system(__UpperCAmelCase		)

      return tpu
def       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      for file in file_list:
            _lowerCamelCase			    =				file.split('''/'''		)[-1]
            _lowerCamelCase			    =				re.search(r'''-\d+-(\d+)\.tfrecord''' ,						__UpperCAmelCase		).group(1		)
            _lowerCamelCase			    =				int(__UpperCAmelCase		)
            num_samples += sample_count

      return num_samples
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None		)						->    str:



      '''simple docstring'''

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



      '''simple docstring'''

      if not args.no_tpu:
            _lowerCamelCase			    =				initialize_tpu(__UpperCAmelCase		)
            _lowerCamelCase			    =				tf.distribute.TPUStrategy(__UpperCAmelCase		)
      else:
            _lowerCamelCase			    =				tf.distribute.OneDeviceStrategy(device='''/gpu:0'''		)

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

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

      _lowerCamelCase			    =				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			    =				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			    =				count_samples(__UpperCAmelCase		)

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

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

      def decode_fn(__UpperCAmelCase		):
            _lowerCamelCase			    =				{
                '''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(__UpperCAmelCase ,						__UpperCAmelCase		)

      # 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			    =				DataCollatorForLanguageModeling(
          tokenizer=__UpperCAmelCase ,						mlm_probability=args.mlm_probability ,						mlm=__UpperCAmelCase ,						return_tensors='''tf'''		)

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

      _lowerCamelCase			    =				args.per_replica_batch_size * strategy.num_replicas_in_sync

      _lowerCamelCase			    =				prepare_dataset(
          __UpperCAmelCase ,						decode_fn=__UpperCAmelCase ,						mask_fn=__UpperCAmelCase ,						batch_size=__UpperCAmelCase ,						shuffle=__UpperCAmelCase ,						shuffle_buffer_size=args.shuffle_buffer_size ,						)

      _lowerCamelCase			    =				prepare_dataset(
          __UpperCAmelCase ,						decode_fn=__UpperCAmelCase ,						mask_fn=__UpperCAmelCase ,						batch_size=__UpperCAmelCase ,						shuffle=__UpperCAmelCase ,						)

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

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

      model.save_pretrained(args.output_dir		)


if __name__ == "__main__":
     snake_case__				= parse_args()
     main(args)
import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           warnings.warn(
               '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use DPTImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)