





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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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       :     Tuple   =     16
__lowerCamelCase       :     Optional[int]   =     32



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase = 16     )						-> List[str]:
 UpperCamelCase	:					Tuple       	=   AutoTokenizer.from_pretrained("bert-base-cased"     )
 UpperCamelCase	:					Tuple       	=   load_dataset("glue"							,   "mrpc"     )

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

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

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

 # Instantiate dataloaders.
 UpperCamelCase	:					List[Any]       	=   DataLoader(
     tokenized_datasets["train"]							,   shuffle=_lowerCAmelCase							,   collate_fn=_lowerCAmelCase							,   batch_size=_lowerCAmelCase     )
 UpperCamelCase	:					int       	=   DataLoader(
     tokenized_datasets["validation"]							,   shuffle=_lowerCAmelCase							,   collate_fn=_lowerCAmelCase							,   batch_size=_lowerCAmelCase     )

 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       :     Any   =     mocked_dataloaders  # noqa: F811



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 # For testing only
 if os.environ.get("TESTING_MOCKED_DATALOADERS"							,   _lowerCAmelCase     ) == "1":
  UpperCamelCase	:					str       	=   2
 # New Code #
 UpperCamelCase	:					Any       	=   int(args.gradient_accumulation_steps     )
 UpperCamelCase	:					int       	=   int(args.local_sgd_steps     )
 # Initialize accelerator
 UpperCamelCase	:					Union[str, Any]       	=   Accelerator(
     cpu=args.cpu							,   mixed_precision=args.mixed_precision							,   gradient_accumulation_steps=_lowerCAmelCase     )
 if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
  raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 UpperCamelCase	:					Union[str, Any]       	=   config["lr"]
 UpperCamelCase	:					str       	=   int(config["num_epochs"]     )
 UpperCamelCase	:					List[str]       	=   int(config["seed"]     )
 UpperCamelCase	:					Any       	=   int(config["batch_size"]     )

 UpperCamelCase	:					Optional[int]       	=   evaluate.load("glue"							,   "mrpc"     )

 set_seed(_lowerCAmelCase     )
 UpperCamelCase	,    UpperCamelCase	:					Any       	=   get_dataloaders(_lowerCAmelCase							,   _lowerCAmelCase     )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase	:					Tuple       	=   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).
 UpperCamelCase	:					List[str]       	=   model.to(accelerator.device     )

 # Instantiate optimizer
 UpperCamelCase	:					Union[str, Any]       	=   AdamW(params=model.parameters()							,   lr=_lowerCAmelCase     )

 # Instantiate scheduler
 UpperCamelCase	:					Optional[Any]       	=   get_linear_schedule_with_warmup(
     optimizer=_lowerCAmelCase							,   num_warmup_steps=100							,   num_training_steps=(len(_lowerCAmelCase     ) * num_epochs)							,   )

 # 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.
 UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   accelerator.prepare(
     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )

 # Now we train the model
 for epoch in range(_lowerCAmelCase     ):
  model.train()
  with LocalSGD(
      accelerator=_lowerCAmelCase							,   model=_lowerCAmelCase							,   local_sgd_steps=_lowerCAmelCase							,   enabled=local_sgd_steps is not None     ) as local_sgd:
   for step, batch in enumerate(_lowerCAmelCase     ):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device     )
    # New code #
    # We use the new `accumulate` context manager to perform gradient accumulation
    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
    with accelerator.accumulate(_lowerCAmelCase     ):
     UpperCamelCase	:					Union[str, Any]       	=   model(**_lowerCAmelCase     )
     UpperCamelCase	:					List[Any]       	=   output.loss
     accelerator.backward(_lowerCAmelCase     )
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # LocalSGD-specific line
     local_sgd.step()

  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():
    UpperCamelCase	:					Union[str, Any]       	=   model(**_lowerCAmelCase     )
   UpperCamelCase	:					Optional[Any]       	=   outputs.logits.argmax(dim=-1     )
   UpperCamelCase	,    UpperCamelCase	:					str       	=   accelerator.gather_for_metrics((predictions, batch["labels"])     )
   metric.add_batch(
       predictions=_lowerCAmelCase							,   references=_lowerCAmelCase							,   )

  UpperCamelCase	:					int       	=   metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""							,   _lowerCAmelCase     )



def 		A_				(     )						-> int:
 UpperCamelCase	:					Optional[Any]       	=   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."							,   )
 # New Code #
 parser.add_argument(
     "--gradient_accumulation_steps"							,   type=_lowerCAmelCase							,   default=1							,   help="The number of minibatches to be ran before gradients are accumulated."							,   )
 parser.add_argument(
     "--local_sgd_steps"							,   type=_lowerCAmelCase							,   default=8							,   help="Number of local SGD steps or None to disable local SGD"     )
 parser.add_argument("--cpu"							,   action="store_true"							,   help="If passed, will train on the CPU."     )
 UpperCamelCase	:					Union[str, Any]       	=   parser.parse_args()
 UpperCamelCase	:					Union[str, Any]       	=   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(_lowerCAmelCase							,   _lowerCAmelCase     )


if __name__ == "__main__":
    main()







from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       super().__init__()
       self.register_modules(unet=A_       ,							scheduler=A_   )





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					Any       	=   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
        raise ValueError(
            F"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
            F""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
        ) * down_scale_factor
        logger.info(
            F"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
            F""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
            " process."   )
       UpperCamelCase	:					Any       	=   int(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   next(iter(self.unet.parameters()   )   ).dtype
       UpperCamelCase	:					Optional[int]       	=   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(A_       ,							A_   ) and len(A_   ) != batch_size:
        raise ValueError(
            F"""You have passed a list of generators of length {len(A_   )}, but requested an effective batch"""
            F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

       UpperCamelCase	:					Dict       	=   audio[:, :, :original_sample_size]

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
