
"""simple docstring"""







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 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   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.'	)

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

	return num_samples
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=None	):
	__lowerCAmelCase    :		Optional[int]		    =   count_samples(_UpperCamelCase	)
	__lowerCAmelCase    :		Union[str, Any]		    =   tf.data.Dataset.from_tensor_slices(_UpperCamelCase	)
	if shuffle:
		__lowerCAmelCase    :		Union[str, Any]		    =   dataset.shuffle(len(_UpperCamelCase	)	)
	__lowerCAmelCase    :		List[str]		    =   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
	__lowerCAmelCase    :		List[str]		    =   dataset.apply(tf.data.experimental.assert_cardinality(_UpperCamelCase	)	)
	__lowerCAmelCase    :		Tuple		    =   dataset.map(_UpperCamelCase ,						num_parallel_calls=_UpperCamelCase	)
	if shuffle:
		assert shuffle_buffer_size is not None
		__lowerCAmelCase    :		Union[str, Any]		    =   dataset.shuffle(args.shuffle_buffer_size	)
	__lowerCAmelCase    :		Tuple		    =   dataset.batch(_UpperCamelCase ,						drop_remainder=_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[int]		    =   dataset.map(_UpperCamelCase ,						num_parallel_calls=_UpperCamelCase	)
	__lowerCAmelCase    :		Any		    =   dataset.prefetch(_UpperCamelCase	)
	return dataset
def 					__lowerCAmelCase       (_UpperCamelCase	):
	if not args.no_tpu:
		__lowerCAmelCase    :		int		    =   initialize_tpu(_UpperCamelCase	)
		__lowerCAmelCase    :		Tuple		    =   tf.distribute.TPUStrategy(_UpperCamelCase	)
	else:
		__lowerCAmelCase    :		List[Any]		    =   tf.distribute.OneDeviceStrategy(device='/gpu:0'	)

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

	__lowerCAmelCase    :		Tuple		    =   AutoTokenizer.from_pretrained(args.tokenizer	)
	__lowerCAmelCase    :		int		    =   AutoConfig.from_pretrained(args.pretrained_model_config	)
	__lowerCAmelCase    :		Any		    =   tokenizer.vocab_size

	__lowerCAmelCase    :		Union[str, Any]		    =   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    :		str		    =   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    :		Optional[Any]		    =   count_samples(_UpperCamelCase	)

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

	with strategy.scope():
		__lowerCAmelCase    :		Dict		    =   TFAutoModelForMaskedLM.from_config(_UpperCamelCase	)
		model(model.dummy_inputs	)  # Pass some dummy inputs through the model to ensure all the weights are built
		__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   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(_UpperCamelCase	):
		__lowerCAmelCase    :		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(_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.
	__lowerCAmelCase    :		Tuple		    =   DataCollatorForLanguageModeling(
	    tokenizer=_UpperCamelCase ,						mlm_probability=args.mlm_probability ,						mlm=_UpperCamelCase ,						return_tensors='tf'	)

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

	__lowerCAmelCase    :		Tuple		    =   args.per_replica_batch_size * strategy.num_replicas_in_sync

	__lowerCAmelCase    :		Optional[int]		    =   prepare_dataset(
	    _UpperCamelCase ,						decode_fn=_UpperCamelCase ,						mask_fn=_UpperCamelCase ,						batch_size=_UpperCamelCase ,						shuffle=_UpperCamelCase ,						shuffle_buffer_size=args.shuffle_buffer_size ,						)

	__lowerCAmelCase    :		int		    =   prepare_dataset(
	    _UpperCamelCase ,						decode_fn=_UpperCamelCase ,						mask_fn=_UpperCamelCase ,						batch_size=_UpperCamelCase ,						shuffle=_UpperCamelCase ,						)

	__lowerCAmelCase    :		List[str]		    =   []
	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__           =	parse_args()
							main(args)

"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			['image_processor', 'tokenizer']
		A_		: Optional[Any]            =			'BlipImageProcessor'
		A_		: int            =			'AutoTokenizer'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   False
			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor

		def __call__(   self ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			if images is None and text is None:
				raise ValueError('You have to specify either images or text.'				)

			# Get only text
			if images is None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer
				__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				return text_encoding

			# add pixel_values
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE				)

			if text is not None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
			else:
				__lowerCAmelCase    :		int		    =   None

			if text_encoding is not None:
				encoding_image_processor.update(_SCREAMING_SNAKE_CASE				)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.tokenizer.model_input_names
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)