

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


lowercase_									=   1_6
lowercase_									=   3_2
def      a      (			A__	:   int      ,   A__	:   Optional[Any] = 16     )    ->      Tuple:





      """simple docstring"""



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

      def tokenize_function(A__	:   Dict     ):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase	       =tokenizer(examples['sentence1']      ,   examples['sentence2']      ,   truncation=A__      ,   max_length=A__     )
            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	       =datasets.map(
                A__      ,   batched=A__      ,   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	       =tokenized_datasets.rename_column('label'      ,   'labels'     )

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

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

      # Instantiate dataloaders.
      _lowercase	       =DataLoader(
          tokenized_datasets['train']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__      ,   drop_last=A__     )
      _lowercase	       =DataLoader(
          tokenized_datasets['validation']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__      ,   drop_last=(accelerator.mixed_precision == 'fp8')      ,   )

      return train_dataloader, eval_dataloader
def      a      (			A__	:   int      ,   A__	:   str     )    ->      int:





      """simple docstring"""



      _lowercase	       =Accelerator(cpu=args.cpu      ,   mixed_precision=args.mixed_precision     )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      _lowercase	       =config['lr']
      _lowercase	       =int(config['num_epochs']     )
      _lowercase	       =int(config['seed']     )
      _lowercase	       =int(config['batch_size']     )

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

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

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

      # 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	       =model.to(accelerator.device     )
      # Instantiate optimizer
      _lowercase	       =AdamW(params=model.parameters()      ,   lr=A__     )

      # Instantiate scheduler
      _lowercase	       =get_linear_schedule_with_warmup(
          optimizer=A__      ,   num_warmup_steps=100      ,   num_training_steps=(len(A__     ) * 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	       =accelerator.prepare(
          A__      ,   A__      ,   A__      ,   A__      ,   A__     )

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

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

            _lowercase	       =metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''      ,   A__     )
def      a      (			)    ->      Dict:





      """simple docstring"""



      _lowercase	       =argparse.ArgumentParser(description='Simple example of training script.'     )
      parser.add_argument(
          '--mixed_precision'      ,   type=A__      ,   default=A__      ,   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.'     )
      _lowercase	       =parser.parse_args()
      _lowercase	       ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
      training_function(A__      ,   A__     )


if __name__ == "__main__":
  main()









import colorsys

from PIL import Image  # type: ignore







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      float:
					__A	:						List[str]       =				x
					__A	:						str       =				y
					for step in range(a  ):  # noqa: B007
										__A	:						Union[str, Any]       =				a * a - b * b + x
										__A	:						Optional[int]       =				2 * a * b + y
										__A	:						List[str]       =				a_new

										# divergence happens for all complex number with an absolute value
										# greater than 4
										if a * a + b * b > 4:
															break
					return step / (max_step - 1)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return (2_55, 2_55, 2_55)







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      tuple:
					if distance == 1:
										return (0, 0, 0)
					else:
										return tuple(round(i * 2_55  ) for i in colorsys.hsv_to_rgb(a					,						1					,						1  )  )







def  _SCREAMING_SNAKE_CASE				(   a = 8_00					,						a = 6_00					,						a = -0.6					,						a = 0					,						a = 3.2					,						a = 50					,						a = True					,						)      ->      Image.Image:
					__A	:						str       =				Image.new('RGB'					,						(image_width, image_height)  )
					__A	:						Dict       =				img.load()

					# loop through the image-coordinates
					for image_x in range(a  ):
										for image_y in range(a  ):
															# determine the figure-coordinates based on the image-coordinates
															__A	:						Dict       =				figure_width / image_width * image_height
															__A	:						Union[str, Any]       =				figure_center_x + (image_x / image_width - 0.5) * figure_width
															__A	:						Optional[Any]       =				figure_center_y + (image_y / image_height - 0.5) * figure_height

															__A	:						Union[str, Any]       =				get_distance(a					,						a					,						a  )

															# color the corresponding pixel based on the selected coloring-function
															if use_distance_color_coding:
																				__A	:						Optional[Any]       =				get_color_coded_rgb(a  )
															else:
																				__A	:						Dict       =				get_black_and_white_rgb(a  )

					return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				UpperCAmelCase						:   str			  =     get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()




