



"""simple docstring"""







import argparse
import logging
import os
import re

import tensorflow as tf

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


__A   :  Dict					=      logging.getLogger(__name__)

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

def 						snake_case__       (					)			->str:






		"""simple docstring"""


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

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

		__lowercase				:		List[str]												=				parser.parse_args()
		return args

def 						snake_case__       (					_lowerCamelCase				)			->List[str]:






		"""simple docstring"""


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






		"""simple docstring"""


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

		return num_samples

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase=None				)			->Union[str, Any]:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		if not args.no_tpu:
				__lowercase				:		Optional[int]												=				initialize_tpu(_UpperCamelCase				)
				__lowercase				:		Optional[Any]												=				tf.distribute.TPUStrategy(_UpperCamelCase				)
		else:
				__lowercase				:		List[Any]												=				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				:		Optional[Any]												=				AutoConfig.from_pretrained(args.pretrained_model_config				)
		__lowercase				:		List[Any]												=				tokenizer.vocab_size

		__lowercase				:		List[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				:		Any												=				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				:		Optional[int]												=				count_samples(_UpperCamelCase				)

		__lowercase				:		Union[str, Any]												=				num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
		__lowercase				:		int												=				steps_per_epoch * args.num_epochs

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

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

		__lowercase				:		str												=				args.per_replica_batch_size * strategy.num_replicas_in_sync

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

		__lowercase				:		Optional[int]												=				prepare_dataset(
		    _UpperCamelCase,  decode_fn=_UpperCamelCase,  mask_fn=_UpperCamelCase,  batch_size=_UpperCamelCase,  shuffle=_UpperCamelCase,  )

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






"""simple docstring"""







from __future__ import annotations

import os
from collections.abc import Mapping

__A   :  str					=      tuple[int, int]




class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	set[int]    ,   lowercase__					:	Mapping[EdgeT, int]						):
						__lowercase				:		set[int]												=				vertices
						__lowercase				:		dict[EdgeT, int]												=				{
						    (min(lowercase__						), max(lowercase__						)): weight for edge, weight in edges.items()
						}



				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	EdgeT    ,   lowercase__					:	int						):
						self.vertices.add(edge[0]						)
						self.vertices.add(edge[1]						)
						__lowercase				:		Union[str, Any]												=				weight



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Graph												=				Graph({min(self.vertices						)}    ,   {}						)
						__lowercase				:		EdgeT
						__lowercase				:		int
						__lowercase				:		EdgeT
						__lowercase				:		int

						while len(subgraph.vertices						) < len(self.vertices						):
								__lowercase				:		int												=				max(self.edges.values()						) + 1
								for edge, weight in self.edges.items():
										if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
												if weight < min_weight:
														__lowercase				:		Optional[Any]												=				edge
														__lowercase				:		List[str]												=				weight

								subgraph.add_edge(lowercase__    ,   lowercase__						)

						return subgraph





def 						snake_case__       (					_lowerCamelCase = "p107_network.txt"				)			->int:






		"""simple docstring"""


		__lowercase				:		str												=				os.path.abspath(os.path.dirname(_lowerCamelCase				)				)
		__lowercase				:		str												=				os.path.join(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		dict[EdgeT, int]												=				{}
		__lowercase				:		list[str]
		__lowercase				:		int
		__lowercase				:		int

		with open(_lowerCamelCase				) as f:
				__lowercase				:		List[str]												=				f.read().strip().split("\n"				)

		__lowercase				:		Any												=				[line.split(","				) for line in data]

		for edgea in range(1,  len(_lowerCamelCase				)				):
				for edgea in range(_lowerCamelCase				):
						if adjaceny_matrix[edgea][edgea] != "-":
								__lowercase				:		Dict												=				int(adjaceny_matrix[edgea][edgea]				)

		__lowercase				:		Graph												=				Graph(set(range(len(_lowerCamelCase				)				)				),  _lowerCamelCase				)

		__lowercase				:		Graph												=				graph.prims_algorithm()

		__lowercase				:		int												=				sum(graph.edges.values()				)
		__lowercase				:		int												=				sum(subgraph.edges.values()				)

		return initial_total - optimal_total


if __name__ == "__main__":
						print(F"""{solution() = }""")
