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

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

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

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

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

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

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

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

       snake_case       :   str							=      parser.parse_args()
       return args


def 			UpperCamelCase					(    __lowerCamelCase     : List[str]    ):
       try:
              if args.tpu_name:
                     snake_case       :   List[str]							=      tf.distribute.cluster_resolver.TPUClusterResolver(
                         args.tpu_name				,					zone=args.tpu_zone				,					project=args.gcp_project    )
              else:
                     snake_case       :   List[str]							=      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(__lowerCamelCase    )
       tf.tpu.experimental.initialize_tpu_system(__lowerCamelCase    )

       return tpu


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       snake_case       :   Union[str, Any]							=      0
       for file in file_list:
              snake_case       :   Dict							=      file.split("/"    )[-1]
              snake_case       :   int							=      re.search(r"-\d+-(\d+)\.tfrecord"				,					__lowerCamelCase    ).group(1    )
              snake_case       :   Tuple							=      int(__lowerCamelCase    )
              num_samples += sample_count

       return num_samples


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


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       if not args.no_tpu:
              snake_case       :   Tuple							=      initialize_tpu(__lowerCamelCase    )
              snake_case       :   int							=      tf.distribute.TPUStrategy(__lowerCamelCase    )
       else:
              snake_case       :   List[str]							=      tf.distribute.OneDeviceStrategy(device="/gpu:0"    )

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

       snake_case       :   Tuple							=      AutoTokenizer.from_pretrained(args.tokenizer    )
       snake_case       :   Optional[Any]							=      AutoConfig.from_pretrained(args.pretrained_model_config    )
       snake_case       :   Dict							=      tokenizer.vocab_size

       snake_case       :   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}."""    )
       snake_case       :   Any							=      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}."""    )

       snake_case       :   List[Any]							=      count_samples(__lowerCamelCase    )

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

       with strategy.scope():
              snake_case       :   List[str]							=      TFAutoModelForMaskedLM.from_config(__lowerCamelCase    )
              model(model.dummy_inputs    )  # Pass some dummy inputs through the model to ensure all the weights are built
              snake_case				,					snake_case       :   Dict							=      create_optimizer(
                  num_train_steps=__lowerCamelCase				,					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=__lowerCamelCase				,					metrics=["accuracy"]    )

       def decode_fn(__lowerCamelCase     : List[Any]    ):
              snake_case       :   List[Any]							=      {
                  "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(__lowerCamelCase				,					__lowerCamelCase    )

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

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

       snake_case       :   Optional[Any]							=      args.per_replica_batch_size * strategy.num_replicas_in_sync

       snake_case       :   Any							=      prepare_dataset(
           __lowerCamelCase				,					decode_fn=__lowerCamelCase				,					mask_fn=__lowerCamelCase				,					batch_size=__lowerCamelCase				,					shuffle=__lowerCamelCase				,					shuffle_buffer_size=args.shuffle_buffer_size				,					)

       snake_case       :   List[Any]							=      prepare_dataset(
           __lowerCamelCase				,					decode_fn=__lowerCamelCase				,					mask_fn=__lowerCamelCase				,					batch_size=__lowerCamelCase				,					shuffle=__lowerCamelCase				,					)

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

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

       model.save_pretrained(args.output_dir    )


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







__lowerCamelCase							=       {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.602_176_634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_5818,
}


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : float    ):
       if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
              snake_case       :   List[Any]							=      (
                  f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                  f"""Valid values are: {', '.join(__lowerCamelCase    )}"""
              )
              raise ValueError(__lowerCamelCase    )
       return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





