



'''simple docstring'''





import argparse
import logging
import os
import re

import tensorflow as tf

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


SCREAMING_SNAKE_CASE__      					=     logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__      					=     tf.data.AUTOTUNE




def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						argparse.ArgumentParser(description="Train a masked language model on TPU."       )
    parser.add_argument(
        "--pretrained_model_config"     ,type=_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=_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=_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=_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=_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=_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=_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=_snake_case     ,default=2**18     ,help="Size of the shuffle buffer (in samples)"     ,)

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

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

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

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

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

    lowercase__		       =						parser.parse_args()
    return args




def        lowerCamelCase  (				_snake_case  :						Optional[Any]       ):





    '''simple docstring'''


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

    return tpu




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


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

    return num_samples




def        lowerCamelCase  (				_snake_case  :						Union[str, Any]     ,_snake_case  :						List[Any]     ,_snake_case  :						int     ,_snake_case  :						str     ,_snake_case  :						Optional[int]     ,_snake_case  :						Any=None       ):





    '''simple docstring'''


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




def        lowerCamelCase  (				_snake_case  :						int       ):





    '''simple docstring'''


    if not args.no_tpu:
        lowercase__		       =						initialize_tpu(_snake_case       )
        lowercase__		       =						tf.distribute.TPUStrategy(_snake_case       )
    else:
        lowercase__		       =						tf.distribute.OneDeviceStrategy(device="/gpu:0"       )

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

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

    lowercase__		       =						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__		       =						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__		       =						count_samples(_snake_case       )

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

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

    def decode_fn(_snake_case  :						List[str]       ):
        lowercase__		       =						{
            "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(_snake_case     ,_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.
    lowercase__		       =						DataCollatorForLanguageModeling(
        tokenizer=_snake_case     ,mlm_probability=args.mlm_probability     ,mlm=_snake_case     ,return_tensors="tf"       )

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

    lowercase__		       =						args.per_replica_batch_size * strategy.num_replicas_in_sync

    lowercase__		       =						prepare_dataset(
        _snake_case     ,decode_fn=_snake_case     ,mask_fn=_snake_case     ,batch_size=_snake_case     ,shuffle=_snake_case     ,shuffle_buffer_size=args.shuffle_buffer_size     ,)

    lowercase__		       =						prepare_dataset(
        _snake_case     ,decode_fn=_snake_case     ,mask_fn=_snake_case     ,batch_size=_snake_case     ,shuffle=_snake_case     ,)

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

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

    model.save_pretrained(args.output_dir       )


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









'''simple docstring'''





from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "facebook/blenderbot_small-90M": 512,
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Any						   =					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :str						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :List[Any]						   =					BlenderbotSmallTokenizer



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_=False     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							Optional[Any]:
           super().__init__(
               ByteLevelBPETokenizer(
                   vocab=UpperCAmelCase_     ,merges=UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_     ,trim_offsets=UpperCAmelCase_     ,)     ,bos_token=UpperCAmelCase_     ,eos_token=UpperCAmelCase_     ,unk_token=UpperCAmelCase_     ,**UpperCAmelCase_     ,)
           lowercase__		       =						add_prefix_space



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							Optional[int]:
           lowercase__		       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							List[int]:
           lowercase__		       =						[self.sep_token_id]
           lowercase__		       =						[self.cls_token_id]

           if token_ids_a is None:
               return len(cls + token_ids_a + sep      ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




