

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


_lowerCamelCase      :					Union[str, Any]					    =      16
_lowerCamelCase      :					Any					    =      32





def      A__					(			__A  :						Accelerator     ,				__A  :						int = 16			)					->Any:
      __A    =AutoTokenizer.from_pretrained('''bert-base-cased'''			)
      __A    =load_dataset('''glue'''     ,				'''mrpc'''			)

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

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

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

      # Instantiate dataloaders.
      __A    =DataLoader(
          tokenized_datasets['''train''']     ,				shuffle=__A     ,				collate_fn=__A     ,				batch_size=__A     ,				drop_last=__A			)
      __A    =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  :						Optional[Any]     ,				__A  :						List[str]			)					->str:
      # Initialize accelerator
      __A    =Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision			)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      __A    =config['''lr''']
      __A    =int(config['''num_epochs''']			)
      __A    =int(config['''seed''']			)
      __A    =int(config['''batch_size''']			)

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

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

      set_seed(__A			)
      __A    ,		__A    =get_dataloaders(__A     ,				__A			)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      __A    =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).
      __A    =model.to(accelerator.device			)
      # Instantiate optimizer
      __A    =AdamW(params=model.parameters()     ,				lr=__A			)

      # Instantiate scheduler
      __A    =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.

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

            __A    =metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''     ,				__A			)





def      A__					(			)					->Any:
      __A    =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.'''			)
      __A    =parser.parse_args()
      __A    ={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(__A     ,				__A			)


if __name__ == "__main__":
   main()





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCamelCase      :					Union[str, Any]					    =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCamelCase      :					str					    =      '''    \"""
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"""

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''





class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =tempfile.mkdtemp()
             os.makedirs(os.path.join(self.diffusers_dir ,   '''schedulers/'''     )     )
             __A    =self.diffusers_dir
             shutil.copy(
                 os.path.join(lowercase__ ,   '''src/diffusers/schedulers/scheduling_ddpm.py'''     ) ,   os.path.join(self.diffusers_dir ,   '''schedulers/scheduling_ddpm.py'''     ) ,   )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''src/diffusers'''
             shutil.rmtree(self.diffusers_dir     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=None     ):







             '''simple docstring'''



             __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
             if overwrite_result is not None:
                   __A    =comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
             __A    =black.Mode(target_versions={black.TargetVersion.PYaa} ,   line_length=1_1_9     )
             __A    =black.format_str(lowercase__ ,   mode=lowercase__     )
             __A    =os.path.join(self.diffusers_dir ,   '''new_code.py'''     )
             with open(lowercase__ ,   '''w''' ,   newline='''\n'''     ) as f:
                   f.write(lowercase__     )
             if overwrite_result is None:
                   self.assertTrue(len(check_copies.is_copy_consistent(lowercase__     )     ) == 0     )
             else:
                   check_copies.is_copy_consistent(f.name ,   overwrite=lowercase__     )
                   with open(lowercase__ ,   '''r'''     ) as f:
                         self.assertTrue(f.read() ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''     )
             self.assertEqual(lowercase__ ,   lowercase__     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   REFERENCE_CODE + '''\n''' ,   )

             # With no empty line at the end
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,   '''DDPMSchedulerOutput''' ,   lowercase__ ,   )

             # Copy consistency with rename
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )

             # Copy consistency with a really long name
             __A    ='''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
             self.check_copy_consistency(
                 f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' ,   f'''{long_class_name}SchedulerOutput''' ,   re.sub('''Bert''' ,   lowercase__ ,   lowercase__     ) ,   )

             # Copy consistency with overwrite
             self.check_copy_consistency(
                 '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,   '''TestSchedulerOutput''' ,   lowercase__ ,   overwrite_result=re.sub('''DDPM''' ,   '''Test''' ,   lowercase__     ) ,   )


