




import argparse
import logging
import os
import re

import tensorflow as tf

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


_lowerCAmelCase  :      Any						=      logging.getLogger(__name__)

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



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__							       =  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=0.15  ,							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."		)

 UpperCAmelCase__							       =  parser.parse_args()
 return args



def   lowerCAmelCase						(						_lowerCAmelCase							:      Any		):




 """simple docstring"""







 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(_UpperCamelCase		)
 tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase		)

 return tpu



def   lowerCAmelCase						(						_lowerCAmelCase							:      List[Any]		):




 """simple docstring"""







 UpperCAmelCase__							       =  0
 for file in file_list:
  UpperCAmelCase__							       =  file.split("/"		)[-1]
  UpperCAmelCase__							       =  re.search(R"-\d+-(\d+)\.tfrecord"  ,							_UpperCamelCase		).group(1		)
  UpperCAmelCase__							       =  int(_UpperCamelCase		)
  num_samples += sample_count

 return num_samples



def   lowerCAmelCase						(						_lowerCAmelCase							:      List[Any]  ,							_lowerCAmelCase							:      Dict  ,							_lowerCAmelCase							:      Dict  ,							_lowerCAmelCase							:      Optional[Any]  ,							_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      str=None		):




 """simple docstring"""







 UpperCAmelCase__							       =  count_samples(_UpperCamelCase		)
 UpperCAmelCase__							       =  tf.data.Dataset.from_tensor_slices(_UpperCamelCase		)
 if shuffle:
  UpperCAmelCase__							       =  dataset.shuffle(len(_UpperCamelCase		)		)
 UpperCAmelCase__							       =  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
 UpperCAmelCase__							       =  dataset.apply(tf.data.experimental.assert_cardinality(_UpperCamelCase		)		)
 UpperCAmelCase__							       =  dataset.map(_UpperCamelCase  ,							num_parallel_calls=_UpperCamelCase		)
 if shuffle:
  assert shuffle_buffer_size is not None
  UpperCAmelCase__							       =  dataset.shuffle(args.shuffle_buffer_size		)
 UpperCAmelCase__							       =  dataset.batch(_UpperCamelCase  ,							drop_remainder=_UpperCamelCase		)
 UpperCAmelCase__							       =  dataset.map(_UpperCamelCase  ,							num_parallel_calls=_UpperCamelCase		)
 UpperCAmelCase__							       =  dataset.prefetch(_UpperCamelCase		)
 return dataset



def   lowerCAmelCase						(						_lowerCAmelCase							:      Union[str, Any]		):




 """simple docstring"""







 if not args.no_tpu:
  UpperCAmelCase__							       =  initialize_tpu(_UpperCamelCase		)
  UpperCAmelCase__							       =  tf.distribute.TPUStrategy(_UpperCamelCase		)
 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(_UpperCamelCase		)

 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(_UpperCamelCase		)
  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=_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(_lowerCAmelCase							:      Any		):
  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(_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.
 UpperCAmelCase__							       =  DataCollatorForLanguageModeling(
     tokenizer=_UpperCamelCase  ,							mlm_probability=args.mlm_probability  ,							mlm=_UpperCamelCase  ,							return_tensors="tf"		)

 def mask_with_collator(_lowerCAmelCase							:      List[str]		):
  # 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(_UpperCamelCase		)  ,							mask_token_id=tokenizer.mask_token_id  ,							special_tokens_mask=_UpperCamelCase  ,							)
  return batch

 UpperCAmelCase__							       =  args.per_replica_batch_size * strategy.num_replicas_in_sync

 UpperCAmelCase__							       =  prepare_dataset(
     _UpperCamelCase  ,							decode_fn=_UpperCamelCase  ,							mask_fn=_UpperCamelCase  ,							batch_size=_UpperCamelCase  ,							shuffle=_UpperCamelCase  ,							shuffle_buffer_size=args.shuffle_buffer_size  ,							)

 UpperCAmelCase__							       =  prepare_dataset(
     _UpperCamelCase  ,							decode_fn=_UpperCamelCase  ,							mask_fn=_UpperCamelCase  ,							batch_size=_UpperCamelCase  ,							shuffle=_UpperCamelCase  ,							)

 UpperCAmelCase__							       =  []
 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__":
  _lowerCAmelCase  :      Union[str, Any]						=      parse_args()
  main(args)








'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
