
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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


__magic_name__           =					16
__magic_name__           =					32





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    # Initialize accelerator
    if args.with_tracking:
        snake_case__  =			Accelerator(
            cpu=args.cpu      ,						mixed_precision=args.mixed_precision      ,						log_with="all"      ,						project_dir=args.project_dir							)
    else:
        snake_case__  =			Accelerator(cpu=args.cpu      ,						mixed_precision=args.mixed_precision							)

    if hasattr(args.checkpointing_steps      ,						"isdigit"							):
        if args.checkpointing_steps == "epoch":
            snake_case__  =			args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            snake_case__  =			int(args.checkpointing_steps							)
        else:
            raise ValueError(
                F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""							)
    else:
        snake_case__  =			None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case__  =			config["lr"]
    snake_case__  =			int(config["num_epochs"]							)
    snake_case__  =			int(config["seed"]							)
    snake_case__  =			int(config["batch_size"]							)

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        snake_case__  =			os.path.split(__lowerCAmelCase							)[-1].split("."							)[0]
        accelerator.init_trackers(__lowerCAmelCase      ,						__lowerCAmelCase							)

    snake_case__  =			AutoTokenizer.from_pretrained("bert-base-cased"							)
    snake_case__  =			load_dataset("glue"      ,						"mrpc"							)
    snake_case__  =			evaluate.load("glue"      ,						"mrpc"							)

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

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

    def collate_fn(__lowerCAmelCase							):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case__  =			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":
            snake_case__  =			16
        elif accelerator.mixed_precision != "no":
            snake_case__  =			8
        else:
            snake_case__  =			None

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

    # Instantiate dataloaders.
    snake_case__  =			DataLoader(
        tokenized_datasets["train"]      ,						shuffle=__lowerCAmelCase      ,						collate_fn=__lowerCAmelCase      ,						batch_size=__lowerCAmelCase							)
    snake_case__  =			DataLoader(
        tokenized_datasets["validation"]      ,						shuffle=__lowerCAmelCase      ,						collate_fn=__lowerCAmelCase      ,						batch_size=__lowerCAmelCase							)

    set_seed(__lowerCAmelCase							)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case__  =			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).
    snake_case__  =			model.to(accelerator.device							)

    # Instantiate optimizer
    snake_case__  =			AdamW(params=model.parameters()      ,						lr=__lowerCAmelCase							)

    # Instantiate scheduler
    snake_case__  =			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.
    snake_case__  ,   snake_case__  ,   snake_case__  ,   snake_case__  ,   snake_case__  =			accelerator.prepare(
        __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)

    # We need to keep track of how many total steps we have iterated over
    snake_case__  =			0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case__  =			0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""							)
            accelerator.load_state(args.resume_from_checkpoint							)
            snake_case__  =			os.path.basename(args.resume_from_checkpoint							)
        else:
            # Get the most recent checkpoint
            snake_case__  =			[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
            dirs.sort(key=os.path.getctime							)
            snake_case__  =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        snake_case__  =			os.path.splitext(__lowerCAmelCase							)[0]

        if "epoch" in training_difference:
            snake_case__  =			int(training_difference.replace("epoch_"      ,						""							)							) + 1
            snake_case__  =			None
        else:
            snake_case__  =			int(training_difference.replace("step_"      ,						""							)							)
            snake_case__  =			resume_step // len(__lowerCAmelCase							)
            resume_step -= starting_epoch * len(__lowerCAmelCase							)

    # Now we train the model
    for epoch in range(__lowerCAmelCase      ,						__lowerCAmelCase							):
        model.train()
        if args.with_tracking:
            snake_case__  =			0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            snake_case__  =			accelerator.skip_first_batches(__lowerCAmelCase      ,						__lowerCAmelCase							)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            snake_case__  =			train_dataloader
        for step, batch in enumerate(__lowerCAmelCase							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            snake_case__  =			model(**__lowerCAmelCase							)
            snake_case__  =			outputs.loss
            snake_case__  =			loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(__lowerCAmelCase							)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

            if isinstance(__lowerCAmelCase      ,						__lowerCAmelCase							):
                snake_case__  =			F"""step_{overall_step}"""
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        snake_case__  =			os.path.join(args.output_dir      ,						__lowerCAmelCase							)
                    accelerator.save_state(__lowerCAmelCase							)

        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():
                snake_case__  =			model(**__lowerCAmelCase							)
            snake_case__  =			outputs.logits.argmax(dim=-1							)
            snake_case__  ,   snake_case__  =			accelerator.gather_for_metrics((predictions, batch["labels"])							)
            metric.add_batch(
                predictions=__lowerCAmelCase      ,						references=__lowerCAmelCase      ,						)

        snake_case__  =			metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""      ,						__lowerCAmelCase							)
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": eval_metric["accuracy"],
                    "f1": eval_metric["f1"],
                    "train_loss": total_loss.item() / len(__lowerCAmelCase							),
                    "epoch": epoch,
                }      ,						step=__lowerCAmelCase      ,						)

        if checkpointing_steps == "epoch":
            snake_case__  =			F"""epoch_{epoch}"""
            if args.output_dir is not None:
                snake_case__  =			os.path.join(args.output_dir      ,						__lowerCAmelCase							)
            accelerator.save_state(__lowerCAmelCase							)

    if args.with_tracking:
        accelerator.end_training()





def 		SCREAMING_SNAKE_CASE__      ( ):
    snake_case__  =			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."							)
    parser.add_argument(
        "--checkpointing_steps"      ,						type=__lowerCAmelCase      ,						default=__lowerCAmelCase      ,						help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."      ,						)
    parser.add_argument(
        "--resume_from_checkpoint"      ,						type=__lowerCAmelCase      ,						default=__lowerCAmelCase      ,						help="If the training should continue from a checkpoint folder."      ,						)
    parser.add_argument(
        "--with_tracking"      ,						action="store_true"      ,						help="Whether to load in all available experiment trackers from the environment and use them for logging."      ,						)
    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(
        "--project_dir"      ,						type=__lowerCAmelCase      ,						default="logs"      ,						help="Location on where to store experiment tracking logs` and relevent project information"      ,						)
    snake_case__  =			parser.parse_args()
    snake_case__  =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(__lowerCAmelCase      ,						__lowerCAmelCase							)


if __name__ == "__main__":
     main()





from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    seed(1							)
    return [gauss(__lowerCAmelCase      ,						__lowerCAmelCase							) for _ in range(__lowerCAmelCase							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return [k for k in range(__lowerCAmelCase							) for _ in range(instance_count[k]							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return sum(__lowerCAmelCase							) / instance_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return instance_count / total_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(__lowerCAmelCase							)							):
        # for loop iterates over number of elements in inner layer of items
        for j in range(len(items[i]							)							):
            # appending squared differences to 'squared_diff' list
            squared_diff.append((items[i][j] - means[i]) ** 2							)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
    snake_case__  =			len(__lowerCAmelCase							)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]
    # for loop iterates over number of elements in list
    for i in range(len(__lowerCAmelCase							)							):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i]							)							):
            snake_case__  =			[]  # to store all discriminant values of each item as a list
            # for loop iterates over number of classes we have in our dataset
            for k in range(len(__lowerCAmelCase							)							):
                # appending values of discriminants for each class to 'temp' list
                temp.append(
                    x_items[i][j] * (means[k] / variance)
                    - (means[k] ** 2 / (2 * variance))
                    + log(probabilities[k]							)							)
            # appending discriminant values of each item to 'results' list
            results.append(__lowerCAmelCase							)

    return [result.index(max(__lowerCAmelCase							)							) for result in results]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			sum(1 for i, j in zip(__lowerCAmelCase      ,						__lowerCAmelCase							) if i == j							)
    # percentage of accuracy equals to number of correct predictions divided by number
    # of all data and multiplied by 100
    return (correct / len(__lowerCAmelCase							)) * 100


__magic_name__           =					TypeVar('''num''')





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = lambda __lowerCAmelCase							: True      ,						__lowerCAmelCase = None      ,						):
    while True:
        try:
            snake_case__  =			input_type(input(__lowerCAmelCase							).strip() or default							)
            if condition(__lowerCAmelCase							):
                return user_input
            else:
                print(F"""{user_input}: {err_msg}"""							)
                continue
        except ValueError:
            print(
                F"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""							)





def 		SCREAMING_SNAKE_CASE__      ( ):
    while True:
        print(" Linear Discriminant Analysis ".center(50      ,						"*"							)							)
        print("*" * 50      ,						"\n"							)
        print("First of all we should specify the number of classes that"							)
        print("we want to generate as training dataset"							)
        # Trying to get number of classes
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x > 0      ,						input_msg="Enter the number of classes (Data Groupings): "      ,						err_msg="Number of classes should be positive!"      ,						)

        print("-" * 100							)

        # Trying to get the value of standard deviation
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x >= 0      ,						input_msg=(
                "Enter the value of standard deviation"
                "(Default value is 1.0 for all classes): "
            )      ,						err_msg="Standard deviation should not be negative!"      ,						default="1.0"      ,						)

        print("-" * 100							)

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        snake_case__  =			[]  # An empty list to store instance counts of classes in dataset
        for i in range(__lowerCAmelCase							):
            snake_case__  =			valid_input(
                input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x > 0      ,						input_msg=(F"""Enter The number of instances for class_{i+1}: """)      ,						err_msg="Number of instances should be positive!"      ,						)
            counts.append(__lowerCAmelCase							)
        print("-" * 100							)

        # An empty list to store values of user-entered means of classes
        snake_case__  =			[]
        for a in range(__lowerCAmelCase							):
            snake_case__  =			valid_input(
                input_type=__lowerCAmelCase      ,						input_msg=(F"""Enter the value of mean for class_{a+1}: """)      ,						err_msg="This is an invalid value."      ,						)
            user_means.append(__lowerCAmelCase							)
        print("-" * 100							)

        print("Standard deviation: "      ,						__lowerCAmelCase							)
        # print out the number of instances in classes in separated line
        for i, count in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Number of instances in class_{i} is: {count}"""							)
        print("-" * 100							)

        # print out mean values of classes separated line
        for i, user_mean in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Mean of class_{i} is: {user_mean}"""							)
        print("-" * 100							)

        # Generating training dataset drawn from gaussian distribution
        snake_case__  =			[
            gaussian_distribution(user_means[j]      ,						__lowerCAmelCase      ,						counts[j]							)
            for j in range(__lowerCAmelCase							)
        ]
        print("Generated Normal Distribution: \n"      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Generating Ys to detecting corresponding classes
        snake_case__  =			y_generator(__lowerCAmelCase      ,						__lowerCAmelCase							)
        print("Generated Corresponding Ys: \n"      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Calculating the value of actual mean for each class
        snake_case__  =			[calculate_mean(counts[k]      ,						x[k]							) for k in range(__lowerCAmelCase							)]
        # for loop iterates over number of elements in 'actual_means' list and print
        # out them in separated line
        for i, actual_mean in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Actual(Real) mean of class_{i} is: {actual_mean}"""							)
        print("-" * 100							)

        # Calculating the value of probabilities for each class
        snake_case__  =			[
            calculate_probabilities(counts[i]      ,						sum(__lowerCAmelCase							)							) for i in range(__lowerCAmelCase							)
        ]

        # for loop iterates over number of elements in 'probabilities' list and print
        # out them in separated line
        for i, probability in enumerate(__lowerCAmelCase      ,						1							):
            print(F"""Probability of class_{i} is: {probability}"""							)
        print("-" * 100							)

        # Calculating the values of variance for each class
        snake_case__  =			calculate_variance(__lowerCAmelCase      ,						__lowerCAmelCase      ,						sum(__lowerCAmelCase							)							)
        print("Variance: "      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        snake_case__  =			predict_y_values(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Calculating Accuracy of the model
        print(F"""Accuracy: {accuracy(__lowerCAmelCase      ,						__lowerCAmelCase							)}"""							)
        print("-" * 100							)
        print(" DONE ".center(100      ,						"+"							)							)

        if input("Press any key to restart or 'q' for quit: "							).strip().lower() == "q":
            print("\n" + "GoodBye!".center(100      ,						"-"							) + "\n"							)
            break
        system("cls" if name == "nt" else "clear"							)  # noqa: S605


if __name__ == "__main__":
     main()



