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
#
########################################################################


UpperCAmelCase_           =				1_6
UpperCAmelCase_           =				3_2




def    lowerCAmelCase_  (		lowercase:				Accelerator	,       lowercase:				int = 16		)     ->					Union[str, Any]:







 '''simple docstring'''
 _UpperCamelCase:  List[Any]    						=			AutoTokenizer.from_pretrained('''bert-base-cased'''		)
 _UpperCamelCase:  Dict    						=			load_dataset('''glue'''	,       '''mrpc'''		)

 def tokenize_function(lowercase:				Any		):
  # max_length=None => use the model max length (it's actually the default)
  _UpperCamelCase:  Tuple    						=			tokenizer(examples['''sentence1''']	,       examples['''sentence2''']	,       truncation=lowercase	,       max_length=lowercase		)
  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:  List[str]    						=			datasets.map(
      lowercase	,       batched=lowercase	,       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:  Union[str, Any]    						=			tokenized_datasets.rename_column('''label'''	,       '''labels'''		)

 def collate_fn(lowercase:				Tuple		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  _UpperCamelCase:  str    						=			128 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:  Union[str, Any]    						=			16
  elif accelerator.mixed_precision != "no":
   _UpperCamelCase:  Dict    						=			8
  else:
   _UpperCamelCase:  Optional[int]    						=			None

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

 # Instantiate dataloaders.
 _UpperCamelCase:  Optional[Any]    						=			DataLoader(
     tokenized_datasets['''train''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=lowercase	,       drop_last=lowercase		)
 _UpperCamelCase:  Optional[Any]    						=			DataLoader(
     tokenized_datasets['''validation''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=lowercase	,       drop_last=(accelerator.mixed_precision == '''fp8''')	,       )

 return train_dataloader, eval_dataloader




def    lowerCAmelCase_  (		lowercase:				List[Any]	,       lowercase:				List[Any]		)     ->					Tuple:







 '''simple docstring'''
 # Initialize accelerator
 _UpperCamelCase:  Union[str, Any]    						=			Accelerator(cpu=args.cpu	,       mixed_precision=args.mixed_precision		)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:  int    						=			config['''lr''']
 _UpperCamelCase:  List[str]    						=			int(config['''num_epochs''']		)
 _UpperCamelCase:  Tuple    						=			int(config['''seed''']		)
 _UpperCamelCase:  Union[str, Any]    						=			int(config['''batch_size''']		)

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

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

 set_seed(lowercase		)
 _UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			get_dataloaders(lowercase	,       lowercase		)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  Any    						=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,       return_dict=lowercase		)

 # 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:  Dict    						=			model.to(accelerator.device		)
 # Instantiate optimizer
 _UpperCamelCase:  Tuple    						=			AdamW(params=model.parameters()	,       lr=lowercase		)

 # Instantiate scheduler
 _UpperCamelCase:  Optional[Any]    						=			get_linear_schedule_with_warmup(
     optimizer=lowercase	,       num_warmup_steps=100	,       num_training_steps=(len(lowercase		) * 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						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase						,				_UpperCamelCase:  Optional[Any]    						=			accelerator.prepare(
     lowercase	,       lowercase	,       lowercase	,       lowercase	,       lowercase		)

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

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

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




def    lowerCAmelCase_  (		)     ->					List[str]:







 '''simple docstring'''
 _UpperCamelCase:  Union[str, Any]    						=			argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument(
     '''--mixed_precision'''	,       type=lowercase	,       default=lowercase	,       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:  List[str]    						=			parser.parse_args()
 _UpperCamelCase:  Optional[Any]    						=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()
from __future__ import annotations




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				list[str] | None = None	,       lowercase:				dict[str, float] | None = None	,       lowercase:				bool = False	,       )     ->					tuple[int, float, str]:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			cipher_alphabet or [chr(lowercase		) for i in range(97	,       123		)]

 # If the argument is None or the user provided an empty dictionary
 if not frequencies_dict:
  # Frequencies of letters in the english language (how much they show up)
  _UpperCamelCase:  Any    						=			{
      '''a''': 0.08497,
      '''b''': 0.01492,
      '''c''': 0.02202,
      '''d''': 0.04253,
      '''e''': 0.11162,
      '''f''': 0.02228,
      '''g''': 0.02015,
      '''h''': 0.06094,
      '''i''': 0.07546,
      '''j''': 0.00153,
      '''k''': 0.01292,
      '''l''': 0.04025,
      '''m''': 0.02406,
      '''n''': 0.06749,
      '''o''': 0.07507,
      '''p''': 0.01929,
      '''q''': 0.00095,
      '''r''': 0.07587,
      '''s''': 0.06327,
      '''t''': 0.09356,
      '''u''': 0.02758,
      '''v''': 0.00978,
      '''w''': 0.02560,
      '''x''': 0.00150,
      '''y''': 0.01994,
      '''z''': 0.00077,
  }
 else:
  # Custom frequencies dictionary
  _UpperCamelCase:  str    						=			frequencies_dict

 if not case_sensitive:
  _UpperCamelCase:  Union[str, Any]    						=			ciphertext.lower()

 # Chi squared statistic values
 _UpperCamelCase:  dict[int, tuple[float, str]]    						=			{}

 # cycle through all of the shifts
 for shift in range(len(lowercase		)		):
  _UpperCamelCase:  Tuple    						=			''''''

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    _UpperCamelCase:  Optional[int]    						=			(alphabet_letters.index(letter.lower()		) - shift) % len(
        lowercase		)
    decrypted_with_shift += (
        alphabet_letters[new_key].upper()
        if case_sensitive and letter.isupper()
        else alphabet_letters[new_key]
    )
   except ValueError:
    # Append the character if it isn't in the alphabet
    decrypted_with_shift += letter

  _UpperCamelCase:  Optional[int]    						=			0.0

  # Loop through each letter in the decoded message with the shift
  for letter in decrypted_with_shift:
   if case_sensitive:
    _UpperCamelCase:  Optional[int]    						=			letter.lower()
    if letter in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  Any    						=			decrypted_with_shift.lower().count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  int    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  int    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value
   else:
    if letter.lower() in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  List[Any]    						=			decrypted_with_shift.count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  Union[str, Any]    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  List[str]    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
  _UpperCamelCase:  List[Any]    						=			(
      chi_squared_statistic,
      decrypted_with_shift,
  )

 # Get the most likely cipher by finding the cipher with the smallest chi squared
 # statistic
 def chi_squared_statistic_values_sorting_key(lowercase:				int		) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 _UpperCamelCase:  int    						=			min(
     lowercase	,       key=lowercase	,       )

 # Get all the data from the most likely cipher (key, decoded message)
 (
     (
     _UpperCamelCase
 )						,				(
     _UpperCamelCase
 )						,				
 ):  str    						=			chi_squared_statistic_values[most_likely_cipher]

 # Return the data on the most likely shift
 return (
     most_likely_cipher,
     most_likely_cipher_chi_squared_value,
     decoded_most_likely_cipher,
 )