"""simple docstring"""



import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	16
SCREAMING_SNAKE_CASE__:List[str]			=	32



def 					_lowerCamelCase(					a ,  a = 1_6							):
							__a     				=  AutoTokenizer.from_pretrained("bert-base-cased"							)
							__a     				=  load_dataset("glue" ,  "mrpc"							)

							def tokenize_function(a							):
														# max_length=None => use the model max length (it's actually the default)
														__a     				=  tokenizer(examples["sentence1"] ,  examples["sentence2"] ,  truncation=lowerCamelCase__ ,  max_length=lowerCamelCase__							)
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														__a     				=  datasets.map(
														    lowerCamelCase__ ,  batched=lowerCamelCase__ ,  remove_columns=["idx", "sentence1", "sentence2"] ,  )

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							__a     				=  tokenized_datasets.rename_column("label" ,  "labels"							)

							def collate_fn(a							):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__a     				=  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					__a     				=  1_6
														elif accelerator.mixed_precision != "no":
																					__a     				=  8
														else:
																					__a     				=  None

														return tokenizer.pad(
														    lowerCamelCase__ ,  padding="longest" ,  max_length=lowerCamelCase__ ,  pad_to_multiple_of=lowerCamelCase__ ,  return_tensors="pt" ,  )

							# Instantiate dataloaders.
							__a     				=  DataLoader(
							    tokenized_datasets["train"] ,  shuffle=lowerCamelCase__ ,  collate_fn=lowerCamelCase__ ,  batch_size=lowerCamelCase__ ,  drop_last=lowerCamelCase__							)
							__a     				=  DataLoader(
							    tokenized_datasets["validation"] ,  shuffle=lowerCamelCase__ ,  collate_fn=lowerCamelCase__ ,  batch_size=lowerCamelCase__ ,  drop_last=(accelerator.mixed_precision == "fp8") ,  )

							return train_dataloader, eval_dataloader



def 					_lowerCamelCase(					a ,  a							):
							# Initialize accelerator
							__a     				=  Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision							)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__a     				=  config['''lr''']
							__a     				=  int(config["num_epochs"]							)
							__a     				=  int(config["seed"]							)
							__a     				=  int(config["batch_size"]							)

							__a     				=  evaluate.load("glue" ,  "mrpc"							)

							# If the batch size is too big we use gradient accumulation
							__a     				=  1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														__a     				=  batch_size // MAX_GPU_BATCH_SIZE
														__a     				=  MAX_GPU_BATCH_SIZE

							set_seed(lowerCamelCase__							)
							__a     				=  get_dataloaders(lowerCamelCase__ ,  lowerCamelCase__							)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__a     				=  AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,  return_dict=lowerCamelCase__							)

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							__a     				=  model.to(accelerator.device							)
							# Instantiate optimizer
							__a     				=  AdamW(params=model.parameters() ,  lr=lowerCamelCase__							)

							# Instantiate scheduler
							__a     				=  get_linear_schedule_with_warmup(
							    optimizer=lowerCamelCase__ ,  num_warmup_steps=1_0_0 ,  num_training_steps=(len(lowerCamelCase__							) * num_epochs) // gradient_accumulation_steps ,  )

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.

							__a     				=  accelerator.prepare(
							    lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__ ,  lowerCamelCase__							)

							# Now we train the model
							for epoch in range(lowerCamelCase__							):
														model.train()
														for step, batch in enumerate(lowerCamelCase__							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device							)
																					__a     				=  model(**lowerCamelCase__							)
																					__a     				=  outputs.loss
																					__a     				=  loss / gradient_accumulation_steps
																					accelerator.backward(lowerCamelCase__							)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														for step, batch in enumerate(lowerCamelCase__							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device							)
																					with torch.no_grad():
																												__a     				=  model(**lowerCamelCase__							)
																					__a     				=  outputs.logits.argmax(dim=-1							)
																					__a     				=  accelerator.gather_for_metrics((predictions, batch["labels"])							)
																					metric.add_batch(
																					    predictions=lowerCamelCase__ ,  references=lowerCamelCase__ ,  )

														__a     				=  metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}:" ,  lowerCamelCase__							)



def 					_lowerCamelCase(					):
							__a     				=  argparse.ArgumentParser(description="Simple example of training script."							)
							parser.add_argument(
							    "--mixed_precision" ,  type=lowerCamelCase__ ,  default=lowerCamelCase__ ,  choices=["no", "fp16", "bf16", "fp8"] ,  help="Whether to use mixed precision. Choose"
							    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
							    "and an Nvidia Ampere GPU." ,  )
							parser.add_argument("--cpu" ,  action="store_true" ,  help="If passed, will train on the CPU."							)
							__a     				=  parser.parse_args()
							__a     				=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
							training_function(lowerCamelCase__ ,  lowerCamelCase__							)


if __name__ == "__main__":
			main()

"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
SCREAMING_SNAKE_CASE__:Any			=	{
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
SCREAMING_SNAKE_CASE__:Optional[int]			=	"""ETAOINSHRDLCUMWFGYPBVKJXQZ"""
SCREAMING_SNAKE_CASE__:Tuple			=	"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def 					_lowerCamelCase(					a							):
       __a     				=  {letter: 0 for letter in string.ascii_uppercase}
       for letter in message.upper():
              if letter in LETTERS:
                     letter_count[letter] += 1

       return letter_count



def 					_lowerCamelCase(					a							):
       return x[0]



def 					_lowerCamelCase(					a							):
       __a     				=  get_letter_count(a							)
       __a     				=  {
           freq: [] for letter, freq in letter_to_freq.items()
       }
       for letter in LETTERS:
              freq_to_letter[letter_to_freq[letter]].append(a							)

       __a     				=  {}

       for freq in freq_to_letter:
              freq_to_letter[freq].sort(key=ETAOIN.find ,  reverse=a							)
              __a     				=  "".join(freq_to_letter[freq]							)

       __a     				=  list(freq_to_letter_str.items()							)
       freq_pairs.sort(key=a ,  reverse=a							)

       __a     				=  [freq_pair[1] for freq_pair in freq_pairs]

       return "".join(a							)



def 					_lowerCamelCase(					a							):
       __a     				=  get_frequency_order(a							)
       __a     				=  0
       for common_letter in ETAOIN[:6]:
              if common_letter in freq_order[:6]:
                     match_score += 1

       for uncommon_letter in ETAOIN[-6:]:
              if uncommon_letter in freq_order[-6:]:
                     match_score += 1

       return match_score


if __name__ == "__main__":
   import doctest

   doctest.testmod()
