





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,
# specifically showcasing the experiment tracking capability,
# 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
#
########################################################################

UpperCAmelCase_					:   Any 							=						16
UpperCAmelCase_					:   Any 							=						32






def 							_lowerCAmelCase (  _a   :					Accelerator					,   _a   :					int = 16			)	->			Optional[int]:
     lowerCAmelCase_					:       Tuple    =		AutoTokenizer.from_pretrained("""bert-base-cased"""			)
     lowerCAmelCase_					:       List[Any]    =		load_dataset("""glue"""					,   """mrpc"""			)

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

     def collate_fn(_a   :					Optional[Any]			):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowerCAmelCase_					:       Optional[int]    =		1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               lowerCAmelCase_					:       Optional[int]    =		16
          elif accelerator.mixed_precision != "no":
               lowerCAmelCase_					:       List[Any]    =		8
          else:
               lowerCAmelCase_					:       Tuple    =		None

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

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

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      UpperCAmelCase_					:   Tuple 							=						mocked_dataloaders  # noqa: F811






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					List[Any]			)	->			List[str]:
     # For testing only
     if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,   _a			) == "1":
          lowerCAmelCase_					:       List[Any]    =		2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          lowerCAmelCase_					:       Dict    =		Accelerator(
              cpu=args.cpu					,   mixed_precision=args.mixed_precision					,   log_with="""all"""					,   project_dir=args.project_dir			)
     else:
          lowerCAmelCase_					:       Optional[int]    =		Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision			)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase_					:       Optional[int]    =		config["""lr"""]
     lowerCAmelCase_					:       Optional[Any]    =		int(config["""num_epochs"""]			)
     lowerCAmelCase_					:       Optional[Any]    =		int(config["""seed"""]			)
     lowerCAmelCase_					:       List[str]    =		int(config["""batch_size"""]			)
     set_seed(_a			)

     lowerCAmelCase_   ,  lowerCAmelCase_					:       Any    =		get_dataloaders(_a					,   _a			)
     lowerCAmelCase_					:       Any    =		evaluate.load("""glue"""					,   """mrpc"""			)

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

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase_					:       Optional[int]    =		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).
     lowerCAmelCase_					:       Any    =		model.to(accelerator.device			)

     # Instantiate optimizer
     lowerCAmelCase_					:       Optional[Any]    =		AdamW(params=model.parameters()					,   lr=_a			)

     # Instantiate scheduler
     lowerCAmelCase_					:       Optional[Any]    =		get_linear_schedule_with_warmup(
         optimizer=_a					,   num_warmup_steps=1_00					,   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.
     lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		accelerator.prepare(
         _a					,   _a					,   _a					,   _a					,   _a			)

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          lowerCAmelCase_					:       Union[str, Any]    =		os.path.split(_a			)[-1].split("""."""			)[0]
          accelerator.init_trackers(_a					,   _a			)

     # Now we train the model
     for epoch in range(_a			):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               lowerCAmelCase_					:       Optional[int]    =		0
          for step, batch in enumerate(_a			):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device			)
               lowerCAmelCase_					:       Union[str, Any]    =		model(**_a			)
               lowerCAmelCase_					:       Union[str, Any]    =		outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               lowerCAmelCase_					:       Optional[int]    =		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` (the default).
               batch.to(accelerator.device			)
               with torch.no_grad():
                    lowerCAmelCase_					:       str    =		model(**_a			)
               lowerCAmelCase_					:       Optional[Any]    =		outputs.logits.argmax(dim=-1			)
               lowerCAmelCase_   ,  lowerCAmelCase_					:       Optional[Any]    =		accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
               metric.add_batch(
                   predictions=_a					,   references=_a					,   )

          lowerCAmelCase_					:       List[Any]    =		metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'epoch {epoch}:'					,   _a			)

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       """accuracy""": eval_metric["""accuracy"""],
                       """f1""": eval_metric["""f1"""],
                       """train_loss""": total_loss.item() / len(_a			),
                       """epoch""": epoch,
                   }					,   step=_a					,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()






def 							_lowerCAmelCase (  )	->			int:
     lowerCAmelCase_					:       Union[str, Any]    =		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."""			)
     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(
         """--project_dir"""					,   type=_a					,   default="""logs"""					,   help="""Location on where to store experiment tracking logs` and relevent project information"""					,   )
     lowerCAmelCase_					:       List[str]    =		parser.parse_args()
     lowerCAmelCase_					:       str    =		{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
     training_function(_a					,   _a			)


if __name__ == "__main__":
      main()







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
