

'''simple docstring'''







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

__lowerCamelCase									=     16
__lowerCamelCase									=     32
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__ = 16					)      ->      int:
       A_						       =   AutoTokenizer.from_pretrained("""bert-base-cased"""					)
       A_						       =   load_dataset("""glue""",							"""mrpc"""					)

       def tokenize_function(UpperCAmelCase__					):
              # max_length=None => use the model max length (it's actually the default)
              A_						       =   tokenizer(examples["""sentence1"""],							examples["""sentence2"""],							truncation=UpperCAmelCase__,							max_length=UpperCAmelCase__					)
              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(
                  UpperCAmelCase__,							batched=UpperCAmelCase__,							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(UpperCAmelCase__					):
              # 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(
                  UpperCAmelCase__,							padding="""longest""",							max_length=UpperCAmelCase__,							pad_to_multiple_of=UpperCAmelCase__,							return_tensors="""pt""",							)

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

       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

      __lowerCamelCase									=     mocked_dataloaders  # noqa: F811
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS""",							UpperCAmelCase__					) == "1":
              A_						       =   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:
              A_						       =   Accelerator(
                  cpu=args.cpu,							mixed_precision=args.mixed_precision,							log_with="""all""",							project_dir=args.project_dir					)
       else:
              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"""]					)
       set_seed(UpperCAmelCase__					)

       A_       ,      A_						       =   get_dataloaders(UpperCAmelCase__,							UpperCAmelCase__					)
       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

       # 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=UpperCAmelCase__					)

       # 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=UpperCAmelCase__					)

       # Instantiate scheduler
       A_						       =   get_linear_schedule_with_warmup(
           optimizer=UpperCAmelCase__,							num_warmup_steps=1_00,							num_training_steps=(len(UpperCAmelCase__					) * 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(
           UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)

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

       # Now we train the model
       for epoch in range(UpperCAmelCase__					):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     A_						       =   0
              for step, batch in enumerate(UpperCAmelCase__					):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device					)
                     A_						       =   model(**UpperCAmelCase__					)
                     A_						       =   outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     A_						       =   loss / gradient_accumulation_steps
                     accelerator.backward(UpperCAmelCase__					)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

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

              # 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(UpperCAmelCase__					),
                             """epoch""": epoch,
                         },							step=UpperCAmelCase__,							)

    # 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    UpperCAmelCase__     (   )      ->      str:
       A_						       =   argparse.ArgumentParser(description="""Simple example of training script."""					)
       parser.add_argument(
           """--mixed_precision""",							type=UpperCAmelCase__,							default=UpperCAmelCase__,							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=UpperCAmelCase__,							default="""logs""",							help="""Location on where to store experiment tracking logs` and relevent project information""",							)
       A_						       =   parser.parse_args()
       A_						       =   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(UpperCAmelCase__,							UpperCAmelCase__					)


if __name__ == "__main__":
      main()



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        A__   (   unittest.TestCase      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           torch.manual_seed(0							)
           A_						       =   UNetaDModel(
               block_out_channels=(32, 64)		,					layers_per_block=2		,					sample_size=32		,					in_channels=3		,					out_channels=3		,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")		,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")		,					)
           return model







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""							).images

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""		,					return_dict=UpperCamelCase__							)[0]

           A_						       =   image[0, -3:, -3:, -1]
           A_						       =   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

           assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2


@slow
@require_torch
class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)
           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					output_type="""numpy"""							).images

           A_						       =   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]							)

           assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
