


"""simple docstring"""




import argparse
import logging
import os
import re

import tensorflow as tf

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


__magic_name__						: int    =				logging.getLogger(__name__)

__magic_name__						: int    =				tf.data.AUTOTUNE





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

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

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

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

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

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

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

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

			__lowerCamelCase          =      parser.parse_args()
			return args





def       a_ (	lowercase__ :str     ):
			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(lowercase__     )
			tf.tpu.experimental.initialize_tpu_system(lowercase__     )

			return tpu





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

			return num_samples





def       a_ (	lowercase__ :str,			lowercase__ :List[Any],			lowercase__ :Optional[Any],			lowercase__ :int,			lowercase__ :List[Any],			lowercase__ :Dict=None     ):
			__lowerCamelCase          =      count_samples(lowercase__     )
			__lowerCamelCase          =      tf.data.Dataset.from_tensor_slices(lowercase__     )
			if shuffle:
						__lowerCamelCase          =      dataset.shuffle(len(lowercase__     )     )
			__lowerCamelCase          =      tf.data.TFRecordDataset(lowercase__,			num_parallel_reads=lowercase__     )
			# 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(lowercase__     )     )
			__lowerCamelCase          =      dataset.map(lowercase__,			num_parallel_calls=lowercase__     )
			if shuffle:
						assert shuffle_buffer_size is not None
						__lowerCamelCase          =      dataset.shuffle(args.shuffle_buffer_size     )
			__lowerCamelCase          =      dataset.batch(lowercase__,			drop_remainder=lowercase__     )
			__lowerCamelCase          =      dataset.map(lowercase__,			num_parallel_calls=lowercase__     )
			__lowerCamelCase          =      dataset.prefetch(lowercase__     )
			return dataset





def       a_ (	lowercase__ :Optional[int]     ):
			if not args.no_tpu:
						__lowerCamelCase          =      initialize_tpu(lowercase__     )
						__lowerCamelCase          =      tf.distribute.TPUStrategy(lowercase__     )
			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(lowercase__     )

			__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(lowercase__     )
						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=lowercase__,			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=lowercase__,			metrics=["""accuracy"""]     )

			def decode_fn(lowercase__ :List[Any]     ):
						__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(lowercase__,			lowercase__     )

			# 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=lowercase__,			mlm_probability=args.mlm_probability,			mlm=lowercase__,			return_tensors="""tf"""     )

			def mask_with_collator(lowercase__ :Optional[int]     ):
						# 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(lowercase__     ),			mask_token_id=tokenizer.mask_token_id,			special_tokens_mask=lowercase__,			)
						return batch

			__lowerCamelCase          =      args.per_replica_batch_size * strategy.num_replicas_in_sync

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

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

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

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

			model.save_pretrained(args.output_dir     )


if __name__ == "__main__":
					__magic_name__						: Union[str, Any]    =				parse_args()
					main(args)








"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       42


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_text_to_video_synth import TextToVideoSDPipeline
					from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
					from .pipeline_text_to_video_zero import TextToVideoZeroPipeline




