


import argparse
import logging
import os
import re

import tensorflow as tf

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


__snake_case            =   logging.getLogger(__name__)

__snake_case            =   tf.data.AUTOTUNE






def 							_lowercase    (		):





			"""simple docstring"""




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

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

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

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

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

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

			UpperCamelCase         =					parser.parse_args()
			return args






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




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

			return tpu






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




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

			return num_samples






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		List[str]=None	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




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

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

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

			UpperCamelCase         =					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}.'	)
			UpperCamelCase         =					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}.'	)

			UpperCamelCase         =					count_samples(SCREAMING_SNAKE_CASE_	)

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

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

			def decode_fn(SCREAMING_SNAKE_CASE_   :		Optional[int]	):
						UpperCamelCase         =					{
						    """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(SCREAMING_SNAKE_CASE_       ,  SCREAMING_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.
			UpperCamelCase         =					DataCollatorForLanguageModeling(
			    tokenizer=SCREAMING_SNAKE_CASE_       ,  mlm_probability=args.mlm_probability       ,  mlm=SCREAMING_SNAKE_CASE_       ,  return_tensors="""tf"""	)

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

			UpperCamelCase         =					args.per_replica_batch_size * strategy.num_replicas_in_sync

			UpperCamelCase         =					prepare_dataset(
			    SCREAMING_SNAKE_CASE_       ,  decode_fn=SCREAMING_SNAKE_CASE_       ,  mask_fn=SCREAMING_SNAKE_CASE_       ,  batch_size=SCREAMING_SNAKE_CASE_       ,  shuffle=SCREAMING_SNAKE_CASE_       ,  shuffle_buffer_size=args.shuffle_buffer_size       ,  )

			UpperCamelCase         =					prepare_dataset(
			    SCREAMING_SNAKE_CASE_       ,  decode_fn=SCREAMING_SNAKE_CASE_       ,  mask_fn=SCREAMING_SNAKE_CASE_       ,  batch_size=SCREAMING_SNAKE_CASE_       ,  shuffle=SCREAMING_SNAKE_CASE_       ,  )

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

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

			model.save_pretrained(args.output_dir	)


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








import qiskit






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int = 2	):





			"""simple docstring"""




			UpperCamelCase         =					qubits

			# Using Aer's simulator
			UpperCamelCase         =					qiskit.Aer.get_backend("""aer_simulator"""	)

			# Creating a Quantum Circuit acting on the q register
			UpperCamelCase         =					qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# Adding a H gate on qubit 0 (now q0 in superposition)
			circuit.h(0	)

			for i in range(1       ,  SCREAMING_SNAKE_CASE_	):
						# Adding CX (CNOT) gate
						circuit.cx(i - 1       ,  SCREAMING_SNAKE_CASE_	)

			# Mapping the quantum measurement to the classical bits
			circuit.measure(list(range(SCREAMING_SNAKE_CASE_	)	)       ,  list(range(SCREAMING_SNAKE_CASE_	)	)	)

			# Now measuring any one qubit would affect other qubits to collapse
			# their super position and have same state as the measured one.

			# Executing the circuit on the simulator
			UpperCamelCase         =					qiskit.execute(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  shots=1_000	)

			return job.result().get_counts(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
			print(F'''Total count for various states are: {quantum_entanglement(3)}''')




