
"""simple docstring"""





import argparse
import os

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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_a														=		16
_a														=		32



def       lowerCamelCase__		(			__snake_case,     __snake_case = 16	)							->		Tuple:


					"""simple docstring"""





					_UpperCamelCase							 =  AutoTokenizer.from_pretrained('''bert-base-cased'''	)
					_UpperCamelCase							 =  load_dataset('''glue''',     '''mrpc'''	)

					def tokenize_function(__snake_case	):
										# max_length=None => use the model max length (it's actually the default)
										_UpperCamelCase							 =  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():
										_UpperCamelCase							 =  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
					_UpperCamelCase							 =  tokenized_datasets.rename_column('''label''',     '''labels'''	)

					def collate_fn(__snake_case	):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										_UpperCamelCase							 =  1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															_UpperCamelCase							 =  16
										elif accelerator.mixed_precision != "no":
															_UpperCamelCase							 =  8
										else:
															_UpperCamelCase							 =  None

										return tokenizer.pad(
										    lowerCAmelCase__,     padding='''longest''',     max_length=lowerCAmelCase__,     pad_to_multiple_of=lowerCAmelCase__,     return_tensors='''pt''',     )

					# Instantiate dataloaders.
					_UpperCamelCase							 =  DataLoader(
					    tokenized_datasets['''train'''],     shuffle=lowerCAmelCase__,     collate_fn=lowerCAmelCase__,     batch_size=lowerCAmelCase__	)
					_UpperCamelCase							 =  DataLoader(
					    tokenized_datasets['''validation'''],     shuffle=lowerCAmelCase__,     collate_fn=lowerCAmelCase__,     batch_size=lowerCAmelCase__	)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				_a														=		mocked_dataloaders  # noqa: F811



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Optional[int]:


					"""simple docstring"""





					if os.environ.get('''TESTING_MOCKED_DATALOADERS''',     lowerCAmelCase__	) == "1":
										_UpperCamelCase							 =  2
					# Initialize accelerator
					_UpperCamelCase							 =  Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision	)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_UpperCamelCase							 =  config['''lr''']
					_UpperCamelCase							 =  int(config['''num_epochs''']	)
					_UpperCamelCase							 =  int(config['''seed''']	)
					_UpperCamelCase							 =  int(config['''batch_size''']	)

					_UpperCamelCase							 =  evaluate.load('''glue''',     '''mrpc'''	)

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

					set_seed(lowerCAmelCase__	)
					_UpperCamelCase							 =  get_dataloaders(lowerCAmelCase__,     lowerCAmelCase__	)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_UpperCamelCase							 =  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).
					_UpperCamelCase							 =  model.to(accelerator.device	)

					# Instantiate optimizer
					_UpperCamelCase							 =  AdamW(params=model.parameters(),     lr=lowerCAmelCase__	)

					# Instantiate scheduler
					_UpperCamelCase							 =  get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase__,     num_warmup_steps=1_00,     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.
					_UpperCamelCase							 =  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	)
															_UpperCamelCase							 =  model(**lowerCAmelCase__	)
															_UpperCamelCase							 =  outputs.loss
															_UpperCamelCase							 =  loss / gradient_accumulation_steps
															accelerator.backward(lowerCAmelCase__	)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										_UpperCamelCase							 =  0
										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():
																				_UpperCamelCase							 =  model(**lowerCAmelCase__	)
															_UpperCamelCase							 =  outputs.logits.argmax(dim=-1	)
															_UpperCamelCase							 =  accelerator.gather((predictions, batch['''labels'''])	)
															# New Code #
															# First we check if it's a distributed system
															if accelerator.use_distributed:
																				# Then see if we're on the last batch of our eval dataloader
																				if step == len(lowerCAmelCase__	) - 1:
																									# Last batch needs to be truncated on distributed systems as it contains additional samples
																									_UpperCamelCase							 =  predictions[: len(eval_dataloader.dataset	) - samples_seen]
																									_UpperCamelCase							 =  references[: len(eval_dataloader.dataset	) - samples_seen]
																				else:
																									# Otherwise we add the number of samples seen
																									samples_seen += references.shape[0]
																									       # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
																									       # accelerator.gather_for_metrics((predictions, batch["labels"]))
															metric.add_batch(
															    predictions=lowerCAmelCase__,     references=lowerCAmelCase__,     )

										_UpperCamelCase							 =  metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:''',     lowerCAmelCase__	)



def       lowerCamelCase__		(			)							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  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.'''	)
					_UpperCamelCase							 =  parser.parse_args()
					_UpperCamelCase							 =  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCAmelCase__,     lowerCAmelCase__	)


if __name__ == "__main__":
				main()








import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__       ={
    '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,
}
lowercase__       ='ETAOINSHRDLCUMWFGYPBVKJXQZ'
lowercase__       ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  List[Any]						=  {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    __UpperCamelCase     (				lowerCAmelCase__							:				tuple  ):
 return x[0]




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  Optional[Any]						=  get_letter_count(lowerCAmelCase__  )
 __a :  dict[int, list[str]]						=  {
     freq: [] for letter, freq in letter_to_freq.items()
 }
 for letter in LETTERS:
  freq_to_letter[letter_to_freq[letter]].append(lowerCAmelCase__  )

 __a :  dict[int, str]						=  {}

 for freq in freq_to_letter:
  freq_to_letter[freq].sort(key=ETAOIN.find  ,   reverse=lowerCAmelCase__  )
  __a :  int						=  ''''''.join(freq_to_letter[freq]  )

 __a :  Any						=  list(freq_to_letter_str.items()  )
 freq_pairs.sort(key=lowerCAmelCase__  ,   reverse=lowerCAmelCase__  )

 __a :  list[str]						=  [freq_pair[1] for freq_pair in freq_pairs]

 return "".join(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 __a :  int						=  get_frequency_order(lowerCAmelCase__  )
 __a :  str						=  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()
