




import argparse
import logging
import os
import re

import tensorflow as tf

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


__A   :  str					=      logging.getLogger(__name__)

__A   :  Tuple					=      tf.data.AUTOTUNE

def 						snake_case__       (					)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				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=5_12,  help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py",  )

		parser.add_argument(
		    "--mlm_probability",  type=_lowerCamelCase,  default=0.1_5,  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."				)

		__lowercase				:		Optional[Any]												=				parser.parse_args()
		return args

def 						snake_case__       (					_lowerCamelCase				)			->Union[str, Any]:






		"""simple docstring"""


		try:
				if args.tpu_name:
						__lowercase				:		Tuple												=				tf.distribute.cluster_resolver.TPUClusterResolver(
						    args.tpu_name,  zone=args.tpu_zone,  project=args.gcp_project				)
				else:
						__lowercase				:		Tuple												=				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 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		__lowercase				:		Tuple												=				0
		for file in file_list:
				__lowercase				:		Any												=				file.split("/"				)[-1]
				__lowercase				:		Optional[int]												=				re.search(R"-\d+-(\d+)\.tfrecord",  _lowerCamelCase				).group(1				)
				__lowercase				:		List[Any]												=				int(_lowerCamelCase				)
				num_samples += sample_count

		return num_samples

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=None				)			->int:






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				count_samples(_lowerCamelCase				)
		__lowercase				:		Dict												=				tf.data.Dataset.from_tensor_slices(_lowerCamelCase				)
		if shuffle:
				__lowercase				:		Union[str, Any]												=				dataset.shuffle(len(_lowerCamelCase				)				)
		__lowercase				:		Optional[Any]												=				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
		__lowercase				:		Optional[Any]												=				dataset.apply(tf.data.experimental.assert_cardinality(_lowerCamelCase				)				)
		__lowercase				:		List[Any]												=				dataset.map(_lowerCamelCase,  num_parallel_calls=_lowerCamelCase				)
		if shuffle:
				assert shuffle_buffer_size is not None
				__lowercase				:		Any												=				dataset.shuffle(args.shuffle_buffer_size				)
		__lowercase				:		Any												=				dataset.batch(_lowerCamelCase,  drop_remainder=_lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				dataset.map(_lowerCamelCase,  num_parallel_calls=_lowerCamelCase				)
		__lowercase				:		int												=				dataset.prefetch(_lowerCamelCase				)
		return dataset

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		if not args.no_tpu:
				__lowercase				:		Any												=				initialize_tpu(_lowerCamelCase				)
				__lowercase				:		Union[str, Any]												=				tf.distribute.TPUStrategy(_lowerCamelCase				)
		else:
				__lowercase				:		int												=				tf.distribute.OneDeviceStrategy(device="/gpu:0"				)

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

		__lowercase				:		Union[str, Any]												=				AutoTokenizer.from_pretrained(args.tokenizer				)
		__lowercase				:		Union[str, Any]												=				AutoConfig.from_pretrained(args.pretrained_model_config				)
		__lowercase				:		Union[str, Any]												=				tokenizer.vocab_size

		__lowercase				:		Optional[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}.'				)
		__lowercase				:		Tuple												=				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}.'				)

		__lowercase				:		Union[str, Any]												=				count_samples(_lowerCamelCase				)

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

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

		def mask_with_collator(_lowerCamelCase				):
				# TF really needs an isin() function
				__lowercase				:		List[str]												=				(
				    ~tf.cast(batch["attention_mask"],  tf.bool				)
				    | (batch["input_ids"] == tokenizer.cls_token_id)
				    | (batch["input_ids"] == tokenizer.sep_token_id)
				)
				__lowercase				:		Optional[int]												=				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

		__lowercase				:		Dict												=				args.per_replica_batch_size * strategy.num_replicas_in_sync

		__lowercase				:		Optional[Any]												=				prepare_dataset(
		    _lowerCamelCase,  decode_fn=_lowerCamelCase,  mask_fn=_lowerCamelCase,  batch_size=_lowerCamelCase,  shuffle=_lowerCamelCase,  shuffle_buffer_size=args.shuffle_buffer_size,  )

		__lowercase				:		List[str]												=				prepare_dataset(
		    _lowerCamelCase,  decode_fn=_lowerCamelCase,  mask_fn=_lowerCamelCase,  batch_size=_lowerCamelCase,  shuffle=_lowerCamelCase,  )

		__lowercase				:		int												=				[]
		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__":
						__A   :  str					=      parse_args()
						main(args)






"""simple docstring"""







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )			->list[float]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Dict												=				coefficient_matrix.shape
		__lowercase						,__lowercase				:		Tuple												=				constant_matrix.shape

		if rowsa != colsa:
				__lowercase				:		str												=				F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if colsa != 1:
				__lowercase				:		Dict												=				F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if rowsa != rowsa:
				__lowercase				:		Optional[Any]												=				(
				    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
				    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if len(_lowerCamelCase				) != rowsa:
				__lowercase				:		Dict												=				(
				    "Number of initial values must be equal to number of rows in coefficient "
				    F'matrix but received {len(_lowerCamelCase				)} and {rowsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if iterations <= 0:
				raise ValueError("Iterations must be at least 1"				)

		__lowercase				:		NDArray[floataa]												=				np.concatenate(
		    (coefficient_matrix, constant_matrix),  axis=1				)

		__lowercase						,__lowercase				:		str												=				table.shape

		strictly_diagonally_dominant(_lowerCamelCase				)

		# Iterates the whole matrix for given number of times
		for _ in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				[]
				for row in range(_lowerCamelCase				):
						__lowercase				:		Optional[Any]												=				0
						for col in range(_lowerCamelCase				):
								if col == row:
										__lowercase				:		Tuple												=				table[row][col]
								elif col == cols - 1:
										__lowercase				:		Tuple												=				table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						__lowercase				:		List[Any]												=				(temp + val) / denom
						new_val.append(_lowerCamelCase				)
				__lowercase				:		Dict												=				new_val

		return [float(_lowerCamelCase				) for i in new_val]

def 						snake_case__       (					_lowerCamelCase				)			->bool:






		"""simple docstring"""


		__lowercase						,__lowercase				:		int												=				table.shape

		__lowercase				:		Optional[int]												=				True

		for i in range(0,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				0
				for j in range(0,  cols - 1				):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError("Coefficient matrix is not strictly diagonally dominant"				)

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()
