
'''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    :							Optional[int]      =					logging.getLogger(__name__)

lowercase    :							Optional[int]      =					tf.data.AUTOTUNE







def 				SCREAMING_SNAKE_CASE__	(   )       ->	int:
	_snake_case   						=		argparse.ArgumentParser(description='Train a masked language model on TPU.'	)
	parser.add_argument(
	    '--pretrained_model_config'			,				type=__A			,				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=__A			,				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=__A			,				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=__A			,				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=__A			,				help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'			,				)

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

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

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

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

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

	parser.add_argument(
	    '--mlm_probability'			,				type=__A			,				default=0.1_5			,				help='Fraction of tokens to mask during training.'			,				)

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

	_snake_case   						=		parser.parse_args()
	return args







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	try:
		if args.tpu_name:
			_snake_case   						=		tf.distribute.cluster_resolver.TPUClusterResolver(
			    args.tpu_name			,				zone=args.tpu_zone			,				project=args.gcp_project	)
		else:
			_snake_case   						=		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(__A	)
	tf.tpu.experimental.initialize_tpu_system(__A	)

	return tpu







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	int:
	_snake_case   						=		0
	for file in file_list:
		_snake_case   						=		file.split('/'	)[-1]
		_snake_case   						=		re.search(r'-\d+-(\d+)\.tfrecord'			,				__A	).group(1	)
		_snake_case   						=		int(__A	)
		num_samples += sample_count

	return num_samples







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







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	if not args.no_tpu:
		_snake_case   						=		initialize_tpu(__A	)
		_snake_case   						=		tf.distribute.TPUStrategy(__A	)
	else:
		_snake_case   						=		tf.distribute.OneDeviceStrategy(device='/gpu:0'	)

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

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

	_snake_case   						=		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   						=		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   						=		count_samples(__A	)

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

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

	def decode_fn(__A	):
		_snake_case   						=		{
		    '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(__A			,				__A	)

	# 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   						=		DataCollatorForLanguageModeling(
	    tokenizer=__A			,				mlm_probability=args.mlm_probability			,				mlm=__A			,				return_tensors='tf'	)

	def mask_with_collator(__A	):
		# TF really needs an isin() function
		_snake_case   						=		(
		    ~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   						=		data_collator.tf_mask_tokens(
		    batch['input_ids']			,				vocab_size=len(__A	)			,				mask_token_id=tokenizer.mask_token_id			,				special_tokens_mask=__A			,				)
		return batch

	_snake_case   						=		args.per_replica_batch_size * strategy.num_replicas_in_sync

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

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

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

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

	model.save_pretrained(args.output_dir	)


if __name__ == "__main__":
					lowercase    :							Any      =					parse_args()
					main(args)



'''simple docstring'''







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase    :							Optional[Any]      =					argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
lowercase    :							Tuple      =					parser.parse_args()


lowercase    :							Optional[int]      =					"cpu"
lowercase    :							Optional[Any]      =					"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

lowercase    :							Optional[int]      =					"path-to-your-trained-model"
lowercase    :							List[str]      =					StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					lowercase    :							str      =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase    :							Dict      =					pipe.to(device)

# to channels last
lowercase    :							Optional[Any]      =					pipe.unet.to(memory_format=torch.channels_last)
lowercase    :							int      =					pipe.vae.to(memory_format=torch.channels_last)
lowercase    :							Optional[Any]      =					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					lowercase    :							Optional[int]      =					pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowercase    :							Any      =					torch.randn(2, 4, 64, 64)
lowercase    :							Optional[int]      =					torch.rand(1) * 999
lowercase    :							Optional[Any]      =					torch.randn(2, 77, 768)
lowercase    :							Optional[Any]      =					(sample, timestep, encoder_hidden_status)
try:
					lowercase    :							List[Any]      =					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					lowercase    :							List[str]      =					ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase    :							Tuple      =					ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase    :							Optional[Any]      =					ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					lowercase    :							Tuple      =					ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase    :							List[str]      =					666
lowercase    :							Tuple      =					torch.Generator(device).manual_seed(seed)
lowercase    :							Union[str, Any]      =					{"generator": generator}
if args.steps is not None:
					lowercase    :							Dict      =					args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
					lowercase    :							List[str]      =					pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")

