






import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


lowerCamelCase       :       Any        =				16
lowerCamelCase       :       Optional[int]        =				32
def 					snake_case_						(   lowerCAmelCase_				:  Accelerator				,				lowerCAmelCase_				:  DatasetDict				,				lowerCAmelCase_				:  List[int]				,				lowerCAmelCase_				:  List[int]				,				lowerCAmelCase_				:  int = 16					):
							__lowercase					:							int			=   AutoTokenizer.from_pretrained("""bert-base-cased"""					)
							__lowercase					:							Tuple			=   DatasetDict(
							    {
							        """train""": dataset["""train"""].select(lowerCAmelCase_					),
							        """validation""": dataset["""train"""].select(lowerCAmelCase_					),
							        """test""": dataset["""validation"""],
							    }					)

							def tokenize_function(lowerCAmelCase_				:  Union[str, Any]					):
														# max_length=None => use the model max length (it's actually the default)
														__lowercase					:							int			=   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():
														__lowercase					:							Optional[int]			=   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
							__lowercase					:							Tuple			=   tokenized_datasets.rename_column("""label"""				,				"""labels"""					)

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

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

							# Instantiate dataloaders.
							__lowercase					:							int			=   DataLoader(
							    tokenized_datasets["""train"""]				,				shuffle=lowerCAmelCase_				,				collate_fn=lowerCAmelCase_				,				batch_size=lowerCAmelCase_					)
							__lowercase					:							int			=   DataLoader(
							    tokenized_datasets["""validation"""]				,				shuffle=lowerCAmelCase_				,				collate_fn=lowerCAmelCase_				,				batch_size=lowerCAmelCase_					)

							__lowercase					:							List[Any]			=   DataLoader(
							    tokenized_datasets["""test"""]				,				shuffle=lowerCAmelCase_				,				collate_fn=lowerCAmelCase_				,				batch_size=lowerCAmelCase_					)

							return train_dataloader, eval_dataloader, test_dataloader
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  Union[str, Any]					):
							# New Code #
							__lowercase					:							Tuple			=   []
							# Download the dataset
							__lowercase					:							Dict			=   load_dataset("""glue"""				,				"""mrpc"""					)
							# Create our splits
							__lowercase					:							List[str]			=   StratifiedKFold(n_splits=int(args.num_folds					)					)
							# Initialize accelerator
							__lowercase					:							Tuple			=   Accelerator(cpu=args.cpu				,				mixed_precision=args.mixed_precision					)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowercase					:							List[str]			=   config["""lr"""]
							__lowercase					:							Optional[int]			=   int(config["""num_epochs"""]					)
							__lowercase					:							Union[str, Any]			=   int(config["""seed"""]					)
							__lowercase					:							Any			=   int(config["""batch_size"""]					)

							__lowercase					:							Dict			=   evaluate.load("""glue"""				,				"""mrpc"""					)

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

							set_seed(lowerCAmelCase_					)

							# New Code #
							# Create our folds:
							__lowercase					:							Optional[int]			=   kfold.split(np.zeros(datasets["""train"""].num_rows					)				,				datasets["""train"""]["""label"""]					)
							__lowercase					:							str			=   []
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(lowerCAmelCase_					):
														__lowercase	,	__lowercase	,	__lowercase					:							Any			=   get_fold_dataloaders(
														    lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_				,				)
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														__lowercase					:							Any			=   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).
														__lowercase					:							str			=   model.to(accelerator.device					)

														# Instantiate optimizer
														__lowercase					:							int			=   AdamW(params=model.parameters()				,				lr=lowerCAmelCase_					)

														# Instantiate scheduler
														__lowercase					:							str			=   get_linear_schedule_with_warmup(
														    optimizer=lowerCAmelCase_				,				num_warmup_steps=100				,				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.
														__lowercase	,	__lowercase	,	__lowercase	,	__lowercase	,	__lowercase					:							str			=   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					)
																												__lowercase					:							List[Any]			=   model(**lowerCAmelCase_					)
																												__lowercase					:							Dict			=   outputs.loss
																												__lowercase					:							str			=   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():
																																			__lowercase					:							str			=   model(**lowerCAmelCase_					)
																												__lowercase					:							Dict			=   outputs.logits.argmax(dim=-1					)
																												__lowercase	,	__lowercase					:							int			=   accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
																												metric.add_batch(
																												    predictions=lowerCAmelCase_				,				references=lowerCAmelCase_				,				)

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

														# New Code #
														# We also run predictions on the test set at the very end
														__lowercase					:							List[Any]			=   []
														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():
																												__lowercase					:							int			=   model(**lowerCAmelCase_					)
																					__lowercase					:							Tuple			=   outputs.logits
																					__lowercase	,	__lowercase					:							Union[str, Any]			=   accelerator.gather_for_metrics((predictions, batch["""labels"""])					)
																					fold_predictions.append(predictions.cpu()					)
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu()					)
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(lowerCAmelCase_				,				dim=0					)					)
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							__lowercase					:							List[str]			=   torch.cat(lowerCAmelCase_				,				dim=0					)
							__lowercase					:							Optional[int]			=   torch.stack(lowerCAmelCase_				,				dim=0					).sum(dim=0					).div(int(args.num_folds					)					).argmax(dim=-1					)
							__lowercase					:							Any			=   metric.compute(predictions=lowerCAmelCase_				,				references=lowerCAmelCase_					)
							accelerator.print("""Average test metrics from all folds:"""				,				lowerCAmelCase_					)
def 					snake_case_						(   ):
							__lowercase					:							Tuple			=   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."""					)
							# New Code #
							parser.add_argument("""--num_folds"""				,				type=lowerCAmelCase_				,				default=3				,				help="""The number of splits to perform across the dataset"""					)
							__lowercase					:							int			=   parser.parse_args()
							__lowercase					:							Tuple			=   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(lowerCAmelCase_				,				lowerCAmelCase_					)


if __name__ == "__main__":
	main()







import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 					snake_case_						(   lowerCAmelCase_				:  int = 8					):
							__lowercase					:							str			=   ascii_letters + digits + punctuation
							return "".join(secrets.choice(lowerCAmelCase_					) for _ in range(lowerCAmelCase_					)					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int					):
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   i // 3
							__lowercase					:							int			=   i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							__lowercase					:							str			=   (
							    chars_incl
							    + random(lowerCAmelCase_				,				quotient + remainder					)
							    + random(lowerCAmelCase_				,				lowerCAmelCase_					)
							    + random(lowerCAmelCase_				,				lowerCAmelCase_					)
							)
							__lowercase					:							int			=   list(lowerCAmelCase_					)
							shuffle(lowerCAmelCase_					)
							return "".join(lowerCAmelCase_					)

							# random is a generalised function for letters, characters and numbers
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int					):
							return "".join(secrets.choice(lowerCAmelCase_					) for _ in range(lowerCAmelCase_					)					)
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  int					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  Union[str, Any]				,				lowerCAmelCase_				:  List[Any]					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Optional[int]					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int = 8					):

							if len(lowerCAmelCase_					) < min_length:
														# Your Password must be at least 8 characters long
														return False

							__lowercase					:							Tuple			=   any(char in ascii_uppercase for char in password					)
							__lowercase					:							Union[str, Any]			=   any(char in ascii_lowercase for char in password					)
							__lowercase					:							Dict			=   any(char in digits for char in password					)
							__lowercase					:							Tuple			=   any(char in punctuation for char in password					)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters
def 					snake_case_						(   ):
							__lowercase					:							Union[str, Any]			=   int(input("""Please indicate the max length of your password: """					).strip()					)
							__lowercase					:							List[str]			=   input(
							    """Please indicate the characters that must be in your password: """					).strip()
							print("""Password generated:"""				,				password_generator(lowerCAmelCase_					)					)
							print(
							    """Alternative Password generated:"""				,				alternative_password_generator(lowerCAmelCase_				,				lowerCAmelCase_					)				,				)
							print("""[If you are thinking of using this passsword, You better save it.]"""					)


if __name__ == "__main__":
	main()