



'''simple docstring'''




import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


UpperCamelCase__			:      Optional[Any]        =					16
UpperCamelCase__			:      Dict        =					32


def 							lowerCAmelCase_  (      _lowerCamelCase:  Accelerator			, _lowerCamelCase:  int = 16			, _lowerCamelCase:  str = "bert-base-cased"   ):
		__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						AutoTokenizer.from_pretrained(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						str       			=						load_dataset("""glue"""			, """mrpc"""   )

		def tokenize_function(_lowerCamelCase:  Optional[Any]   ):
				# max_length=None => use the model max length (it's actually the default)
				__SCREAMING_SNAKE_CASE			:						Any       			=						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
		__SCREAMING_SNAKE_CASE			:						str       			=						datasets.map(
		    _lowerCamelCase			, batched=_lowerCamelCase			, remove_columns=["""idx""", """sentence1""", """sentence2"""]			, load_from_cache_file=_lowerCamelCase   )

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

		def collate_fn(_lowerCamelCase:  Optional[Any]   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(_lowerCamelCase			, padding="""max_length"""			, max_length=1_28			, return_tensors="""pt"""   )
				return tokenizer.pad(_lowerCamelCase			, padding="""longest"""			, return_tensors="""pt"""   )

		# Instantiate dataloaders.
		__SCREAMING_SNAKE_CASE			:						Dict       			=						DataLoader(
		    tokenized_datasets["""train"""]			, shuffle=_lowerCamelCase			, collate_fn=_lowerCamelCase			, batch_size=_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						List[str]       			=						DataLoader(
		    tokenized_datasets["""validation"""]			, shuffle=_lowerCamelCase			, collate_fn=_lowerCamelCase			, batch_size=_lowerCamelCase   )

		return train_dataloader, eval_dataloader


def 							lowerCAmelCase_  (      _lowerCamelCase:  Any			, _lowerCamelCase:  Optional[Any]			, _lowerCamelCase:  List[str]			, _lowerCamelCase:  Any   ):
		model.eval()
		__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						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():
						__SCREAMING_SNAKE_CASE			:						List[Any]       			=						model(**_lowerCamelCase   )
				__SCREAMING_SNAKE_CASE			:						List[Any]       			=						outputs.logits.argmax(dim=-1   )
				# It is slightly faster to call this once, than multiple times
				__SCREAMING_SNAKE_CASE			:						List[str]       			=						accelerator.gather(
				    (predictions, batch["""labels"""])   )  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(_lowerCamelCase   ) - 1:
								__SCREAMING_SNAKE_CASE			:						Dict       			=						predictions[: len(eval_dataloader.dataset   ) - samples_seen]
								__SCREAMING_SNAKE_CASE			:						int       			=						references[: len(eval_dataloader.dataset   ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=_lowerCamelCase			, references=_lowerCamelCase			, )

		__SCREAMING_SNAKE_CASE			:						Any       			=						metric.compute()
		return eval_metric["accuracy"]


def 							lowerCAmelCase_  (      _lowerCamelCase:  Optional[Any]			, _lowerCamelCase:  Tuple   ):
		__SCREAMING_SNAKE_CASE			:						int       			=						Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__SCREAMING_SNAKE_CASE			:						int       			=						config['''lr''']
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						int(config["""num_epochs"""]   )
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						int(config["""seed"""]   )
		__SCREAMING_SNAKE_CASE			:						List[str]       			=						int(config["""batch_size"""]   )
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						args.model_name_or_path

		set_seed(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						get_dataloaders(_lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase   )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__SCREAMING_SNAKE_CASE			:						Tuple       			=						AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase			, return_dict=_lowerCamelCase   )

		# Instantiate optimizer
		__SCREAMING_SNAKE_CASE			:						List[str]       			=						(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						optimizer_cls(params=model.parameters()			, lr=_lowerCamelCase   )

		if accelerator.state.deepspeed_plugin is not None:
				__SCREAMING_SNAKE_CASE			:						Any       			=						accelerator.state.deepspeed_plugin.deepspeed_config[
				    '''gradient_accumulation_steps'''
				]
		else:
				__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						1
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						(len(_lowerCamelCase   ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				__SCREAMING_SNAKE_CASE			:						List[str]       			=						get_linear_schedule_with_warmup(
				    optimizer=_lowerCamelCase			, num_warmup_steps=0			, num_training_steps=_lowerCamelCase			, )
		else:
				__SCREAMING_SNAKE_CASE			:						Tuple       			=						DummyScheduler(_lowerCamelCase			, total_num_steps=_lowerCamelCase			, warmup_num_steps=0   )

		# 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.
		__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						accelerator.prepare(
		    _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase   )

		# We need to keep track of how many total steps we have iterated over
		__SCREAMING_SNAKE_CASE			:						Tuple       			=						0
		# We also need to keep track of the stating epoch so files are named properly
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						0
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						evaluate.load("""glue"""			, """mrpc"""   )
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						num_epochs

		if args.partial_train_epoch is not None:
				__SCREAMING_SNAKE_CASE			:						Any       			=						args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint   )
				__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						args.resume_from_checkpoint.split("""epoch_"""   )[1]
				__SCREAMING_SNAKE_CASE			:						str       			=						''''''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				__SCREAMING_SNAKE_CASE			:						str       			=						int(_lowerCamelCase   ) + 1
				__SCREAMING_SNAKE_CASE			:						List[str]       			=						evaluation_loop(_lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase   )
				accelerator.print("""resumed checkpoint performance:"""			, _lowerCamelCase   )
				accelerator.print("""resumed checkpoint\'s scheduler\'s lr:"""			, lr_scheduler.get_lr()[0]   )
				accelerator.print("""resumed optimizers\'s lr:"""			, optimizer.param_groups[0]["""lr"""]   )
				with open(os.path.join(args.output_dir			, F"state_{starting_epoch-1}.json"   )			, """r"""   ) as f:
						__SCREAMING_SNAKE_CASE			:						Dict       			=						json.load(_lowerCamelCase   )
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

						 # Now we train the model
		__SCREAMING_SNAKE_CASE			:						Any       			=						{}
		for epoch in range(_lowerCamelCase			, _lowerCamelCase   ):
				model.train()
				for step, batch in enumerate(_lowerCamelCase   ):
						__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						model(**_lowerCamelCase   )
						__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						outputs.loss
						__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						loss / gradient_accumulation_steps
						accelerator.backward(_lowerCamelCase   )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				__SCREAMING_SNAKE_CASE			:						Any       			=						F"epoch_{epoch}"
				__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						os.path.join(args.output_dir			, _lowerCamelCase   )
				accelerator.save_state(_lowerCamelCase   )
				__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						evaluation_loop(_lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase			, _lowerCamelCase   )
				__SCREAMING_SNAKE_CASE			:						List[str]       			=						accuracy
				__SCREAMING_SNAKE_CASE			:						str       			=						lr_scheduler.get_lr()[0]
				__SCREAMING_SNAKE_CASE			:						List[Any]       			=						optimizer.param_groups[0]['''lr''']
				__SCREAMING_SNAKE_CASE			:						List[Any]       			=						epoch
				__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						overall_step
				accelerator.print(F"epoch {epoch}:"			, _lowerCamelCase   )

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			, F"state_{epoch}.json"   )			, """w"""   ) as f:
								json.dump(_lowerCamelCase			, _lowerCamelCase   )


def 							lowerCAmelCase_  (      ):
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""   )
		parser.add_argument(
		    """--model_name_or_path"""			, type=_lowerCamelCase			, default="""bert-base-cased"""			, help="""Path to pretrained model or model identifier from huggingface.co/models."""			, required=_lowerCamelCase			, )
		parser.add_argument(
		    """--output_dir"""			, type=_lowerCamelCase			, default="""."""			, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			, )
		parser.add_argument(
		    """--resume_from_checkpoint"""			, type=_lowerCamelCase			, default=_lowerCamelCase			, help="""If the training should continue from a checkpoint folder."""			, )
		parser.add_argument(
		    """--partial_train_epoch"""			, type=_lowerCamelCase			, default=_lowerCamelCase			, help="""If passed, the training will stop after this number of epochs."""			, )
		parser.add_argument(
		    """--num_epochs"""			, type=_lowerCamelCase			, default=2			, help="""Number of train epochs."""			, )
		__SCREAMING_SNAKE_CASE			:						List[str]       			=						parser.parse_args()
		__SCREAMING_SNAKE_CASE			:						str       			=						{'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

		training_function(_lowerCamelCase			, _lowerCamelCase   )


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,
 )