


"""simple docstring"""




import argparse
import logging
import os
import re

import tensorflow as tf

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


lowercase_			     =  logging.getLogger(__name__)

lowercase_			     =  tf.data.AUTOTUNE

def        lowercase							(		) ->      Optional[int]:
		__a           =			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.'''			)

		__a           =			parser.parse_args()
		return args

def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      int:
		try:
				if args.tpu_name:
						__a           =			tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name    ,					zone=args.tpu_zone    ,					project=args.gcp_project			)
				else:
						__a           =			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        lowercase							(		lowerCAmelCase__ :     Dict			) ->      Dict:
		__a           =			0
		for file in file_list:
				__a           =			file.split('''/'''			)[-1]
				__a           =			re.search(r'''-\d+-(\d+)\.tfrecord'''    ,					lowerCAmelCase__			).group(1			)
				__a           =			int(lowerCAmelCase__			)
				num_samples += sample_count

		return num_samples

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Optional[int]=None			) ->      Tuple:
		__a           =			count_samples(lowerCAmelCase__			)
		__a           =			tf.data.Dataset.from_tensor_slices(lowerCAmelCase__			)
		if shuffle:
				__a           =			dataset.shuffle(len(lowerCAmelCase__			)			)
		__a           =			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
		__a           =			dataset.apply(tf.data.experimental.assert_cardinality(lowerCAmelCase__			)			)
		__a           =			dataset.map(lowerCAmelCase__    ,					num_parallel_calls=lowerCAmelCase__			)
		if shuffle:
				assert shuffle_buffer_size is not None
				__a           =			dataset.shuffle(args.shuffle_buffer_size			)
		__a           =			dataset.batch(lowerCAmelCase__    ,					drop_remainder=lowerCAmelCase__			)
		__a           =			dataset.map(lowerCAmelCase__    ,					num_parallel_calls=lowerCAmelCase__			)
		__a           =			dataset.prefetch(lowerCAmelCase__			)
		return dataset

def        lowercase							(		lowerCAmelCase__ :     str			) ->      List[str]:
		if not args.no_tpu:
				__a           =			initialize_tpu(lowerCAmelCase__			)
				__a           =			tf.distribute.TPUStrategy(lowerCAmelCase__			)
		else:
				__a           =			tf.distribute.OneDeviceStrategy(device='''/gpu:0'''			)

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

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

		__a           =			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}.'''			)
		__a           =			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}.'''			)

		__a           =			count_samples(lowerCAmelCase__			)

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

		with strategy.scope():
				__a           =			TFAutoModelForMaskedLM.from_config(lowerCAmelCase__			)
				model(model.dummy_inputs			)  # Pass some dummy inputs through the model to ensure all the weights are built
				__a      ,     __a           =			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__ :     Tuple			):
				__a           =			{
				    '''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.
		__a           =			DataCollatorForLanguageModeling(
		    tokenizer=lowerCAmelCase__    ,					mlm_probability=args.mlm_probability    ,					mlm=lowerCAmelCase__    ,					return_tensors='''tf'''			)

		def mask_with_collator(lowerCAmelCase__ :     List[Any]			):
				# TF really needs an isin() function
				__a           =			(
				    ~tf.cast(batch['''attention_mask''']    ,					tf.bool			)
				    | (batch['''input_ids'''] == tokenizer.cls_token_id)
				    | (batch['''input_ids'''] == tokenizer.sep_token_id)
				)
				__a      ,     __a           =			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

		__a           =			args.per_replica_batch_size * strategy.num_replicas_in_sync

		__a           =			prepare_dataset(
		    lowerCAmelCase__    ,					decode_fn=lowerCAmelCase__    ,					mask_fn=lowerCAmelCase__    ,					batch_size=lowerCAmelCase__    ,					shuffle=lowerCAmelCase__    ,					shuffle_buffer_size=args.shuffle_buffer_size    ,					)

		__a           =			prepare_dataset(
		    lowerCAmelCase__    ,					decode_fn=lowerCAmelCase__    ,					mask_fn=lowerCAmelCase__    ,					batch_size=lowerCAmelCase__    ,					shuffle=lowerCAmelCase__    ,					)

		__a           =			[]
		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__":
				lowercase_			     =  parse_args()
				main(args)










"""simple docstring"""




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='Translation'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def __call__(  self      ):
				return pa.struct({lang: pa.string() for lang in sorted(self.languages      )}      )





		def 			__UpperCAmelCase       (  self      ):
				from .features import Value

				return {k: Value('''string'''      ) for k in sorted(self.languages      )}







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[List]										=				None
		__UpperCAmelCase		:			Optional[int]										=				None
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='TranslationVariableLanguages'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			sorted(set(self.languages      )      ) if self.languages else None
				__a           =			len(self.languages      ) if self.languages else None

		def __call__(  self      ):
				return pa.struct({'''language''': pa.list_(pa.string()      ), '''translation''': pa.list_(pa.string()      )}      )

		def 			__UpperCAmelCase       (  self							, _a      ):
				__a           =			set(self.languages      )
				if self.languages and set(_a      ) - lang_set:
						raise ValueError(
						    f'''Some languages in example ({', '.join(sorted(set(_a      ) - lang_set      )      )}) are not in valid set ({', '.join(_a      )}).'''      )

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				__a           =			[]
				for lang, text in translation_dict.items():
						if isinstance(_a							, _a      ):
								translation_tuples.append((lang, text)      )
						else:
								translation_tuples.extend([(lang, el) for el in text]      )

        # Ensure translations are in ascending order by language code.
				__a      ,     __a           =			zip(*sorted(_a      )      )

				return {"language": languages, "translation": translations}





		def 			__UpperCAmelCase       (  self      ):
				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''      )      ),
				    "translation": Sequence(Value('''string'''      )      ),
				}






