import argparse
import logging
import os
import re

import tensorflow as tf

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


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

__SCREAMING_SNAKE_CASE						:   Any         =			tf.data.AUTOTUNE
def  snake_case						()	->	int:

						'''simple docstring'''



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

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

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

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

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

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

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

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

						_snake_case						:					List[Any]    =   parser.parse_args()
						return args
def  snake_case						(__lowercase						)	->	Any:

						'''simple docstring'''



						try:
												if args.tpu_name:
																		_snake_case						:					Optional[Any]    =   tf.distribute.cluster_resolver.TPUClusterResolver(
																		    args.tpu_name ,      zone=args.tpu_zone ,      project=args.gcp_project						)
												else:
																		_snake_case						:					List[str]    =   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(__lowercase						)
						tf.tpu.experimental.initialize_tpu_system(__lowercase						)

						return tpu
def  snake_case						(__lowercase						)	->	Tuple:

						'''simple docstring'''



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

						return num_samples
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase=None						)	->	int:

						'''simple docstring'''



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

						'''simple docstring'''



						if not args.no_tpu:
												_snake_case						:					List[Any]    =   initialize_tpu(__lowercase						)
												_snake_case						:					Optional[int]    =   tf.distribute.TPUStrategy(__lowercase						)
						else:
												_snake_case						:					Optional[Any]    =   tf.distribute.OneDeviceStrategy(device="/gpu:0"						)

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

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

						_snake_case						:					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}."""						)
						_snake_case						:					Union[str, 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}."""						)

						_snake_case						:					Optional[int]    =   count_samples(__lowercase						)

						_snake_case						:					Dict    =   num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
						_snake_case						:					Optional[Any]    =   steps_per_epoch * args.num_epochs

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

						def decode_fn(__lowercase						):
												_snake_case						:					Optional[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(__lowercase ,      __lowercase						)

						# 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						:					Optional[int]    =   DataCollatorForLanguageModeling(
						    tokenizer=__lowercase ,      mlm_probability=args.mlm_probability ,      mlm=__lowercase ,      return_tensors="tf"						)

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

						_snake_case						:					Union[str, Any]    =   args.per_replica_batch_size * strategy.num_replicas_in_sync

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

						_snake_case						:					Any    =   prepare_dataset(
						    __lowercase ,      decode_fn=__lowercase ,      mask_fn=__lowercase ,      batch_size=__lowercase ,      shuffle=__lowercase ,      )

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

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

						model.save_pretrained(args.output_dir						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   int         =			parse_args()
				main(args)
from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

__SCREAMING_SNAKE_CASE						:   str         =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE						:   list[int]         =			[ord(letter) for letter in string.ascii_lowercase]
__SCREAMING_SNAKE_CASE						:   set[int]         =			{ord(char) for char in VALID_CHARS}

__SCREAMING_SNAKE_CASE						:   list[str]         =			["the", "be", "to", "of", "and", "in", "that", "have"]
def  snake_case						(__lowercase ,      __lowercase						)	->	str | None:

						'''simple docstring'''



						_snake_case						:					str    =   ""
						_snake_case						:					int
						_snake_case						:					int
						_snake_case						:					int

						for keychar, cipherchar in zip(cycle(__lowercase						) ,      __lowercase						):
												_snake_case						:					str    =   cipherchar ^ keychar
												if decodedchar not in VALID_INTS:
																		return None
												decoded += chr(__lowercase						)

						return decoded
def  snake_case						(__lowercase						)	->	list[str]:

						'''simple docstring'''



						_snake_case						:					list[str]    =   []
						for key in product(__lowercase ,      repeat=3						):
												_snake_case						:					Union[str, Any]    =   try_key(__lowercase ,      __lowercase						)
												if encoded is not None:
																		possibles.append(__lowercase						)
						return possibles
def  snake_case						(__lowercase ,      __lowercase						)	->	list[str]:

						'''simple docstring'''



						return [possible for possible in possibles if common_word in possible.lower()]
def  snake_case						(__lowercase = "p059_cipher.txt"						)	->	int:

						'''simple docstring'''



						_snake_case						:					list[int]
						_snake_case						:					list[str]
						_snake_case						:					str
						_snake_case						:					str
						_snake_case						:					str    =   Path(__lowercase						).parent.joinpath(__lowercase						).read_text(encoding="utf-8"						)

						_snake_case						:					Dict    =   [int(__lowercase						) for number in data.strip().split(","						)]

						_snake_case						:					Tuple    =   filter_valid_chars(__lowercase						)
						for common_word in COMMON_WORDS:
												_snake_case						:					Optional[int]    =   filter_common_word(__lowercase ,      __lowercase						)
												if len(__lowercase						) == 1:
																		break

						_snake_case						:					int    =   possibles[0]
						return sum(ord(__lowercase						) for char in decoded_text						)


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