






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


_a    :   List[Any]        =			16
_a    :   Tuple        =			32


def 				snake_case__							(							UpperCAmelCase							:  Accelerator    ,			UpperCAmelCase							:  int = 1_6       ):
    lowerCAmelCase__   :Tuple      							=				AutoTokenizer.from_pretrained("bert-base-cased"       )
    lowerCAmelCase__   :Any      							=				load_dataset("glue"    ,			"mrpc"       )

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

    def collate_fn(UpperCAmelCase							:  Any       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase__   :Optional[int]      							=				1_2_8 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__   :int      							=				1_6
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__   :Optional[int]      							=				8
        else:
            lowerCAmelCase__   :List[Any]      							=				None

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

    # Instantiate dataloaders.
    lowerCAmelCase__   :Dict      							=				DataLoader(
        tokenized_datasets["train"]    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase       )
    lowerCAmelCase__   :Optional[Any]      							=				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

 _a    :   List[Any]        =			mocked_dataloaders  # noqa: F811


def 				snake_case__							(							UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  str       ):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,			UpperCAmelCase       ) == "1":
        lowerCAmelCase__   :Tuple      							=				2
    # New Code #
    lowerCAmelCase__   :Tuple      							=				int(args.gradient_accumulation_steps       )
    lowerCAmelCase__   :str      							=				int(args.local_sgd_steps       )
    # Initialize accelerator
    lowerCAmelCase__   :Optional[Any]      							=				Accelerator(
        cpu=args.cpu    ,			mixed_precision=args.mixed_precision    ,			gradient_accumulation_steps=UpperCAmelCase       )
    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
    lowerCAmelCase__   :Union[str, Any]      							=				config["lr"]
    lowerCAmelCase__   :int      							=				int(config["num_epochs"]       )
    lowerCAmelCase__   :List[Any]      							=				int(config["seed"]       )
    lowerCAmelCase__   :int      							=				int(config["batch_size"]       )

    lowerCAmelCase__   :Tuple      							=				evaluate.load("glue"    ,			"mrpc"       )

    set_seed(UpperCAmelCase       )
    lowerCAmelCase__							,lowerCAmelCase__   :Any      							=				get_dataloaders(UpperCAmelCase    ,			UpperCAmelCase       )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCAmelCase__   :Dict      							=				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).
    lowerCAmelCase__   :Any      							=				model.to(accelerator.device       )

    # Instantiate optimizer
    lowerCAmelCase__   :Optional[int]      							=				AdamW(params=model.parameters()    ,			lr=UpperCAmelCase       )

    # Instantiate scheduler
    lowerCAmelCase__   :List[Any]      							=				get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase    ,			num_warmup_steps=1_0_0    ,			num_training_steps=(len(UpperCAmelCase       ) * 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.
    lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :Tuple      							=				accelerator.prepare(
        UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )

    # Now we train the model
    for epoch in range(UpperCAmelCase       ):
        model.train()
        with LocalSGD(
            accelerator=UpperCAmelCase    ,			model=UpperCAmelCase    ,			local_sgd_steps=UpperCAmelCase    ,			enabled=local_sgd_steps is not None       ) as local_sgd:
            for step, batch in enumerate(UpperCAmelCase       ):
                # 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(UpperCAmelCase       ):
                    lowerCAmelCase__   :List[str]      							=				model(**UpperCAmelCase       )
                    lowerCAmelCase__   :List[Any]      							=				output.loss
                    accelerator.backward(UpperCAmelCase       )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

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

        lowerCAmelCase__   :Optional[int]      							=				metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''    ,			UpperCAmelCase       )


def 				snake_case__							(							):
    lowerCAmelCase__   :Union[str, Any]      							=				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."    ,			)
    # New Code #
    parser.add_argument(
        "--gradient_accumulation_steps"    ,			type=UpperCAmelCase    ,			default=1    ,			help="The number of minibatches to be ran before gradients are accumulated."    ,			)
    parser.add_argument(
        "--local_sgd_steps"    ,			type=UpperCAmelCase    ,			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."       )
    lowerCAmelCase__   :Tuple      							=				parser.parse_args()
    lowerCAmelCase__   :Any      							=				{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(UpperCAmelCase    ,			UpperCAmelCase       )


if __name__ == "__main__":
 main()











from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    ['''vqvae''']
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__()
          self.register_modules(unet=_lowerCAmelCase    ,	scheduler=_lowerCAmelCase    ,	mel=_lowerCAmelCase    ,	vqvae=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 50 if isinstance(self.scheduler    ,	_lowerCAmelCase		) else 1_000
      @torch.no_grad()
      def __call__(   self    ,	_lowerCAmelCase = 1    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase=True    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				steps or self.get_default_steps()
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size		) == int:
              lowerCAmelCase__   :Dict      							=				(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
              lowerCAmelCase__   :Optional[int]      							=				randn_tensor(
                  (
                      batch_size,
                      self.unet.config.in_channels,
                      self.unet.config.sample_size[0],
                      self.unet.config.sample_size[1],
                  )    ,	generator=_lowerCAmelCase    ,	device=self.device    ,	)
          lowerCAmelCase__   :Union[str, Any]      							=				noise
          lowerCAmelCase__   :Any      							=				None

          if audio_file is not None or raw_audio is not None:
              self.mel.load_audio(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				self.mel.audio_slice_to_image(_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				np.frombuffer(input_image.tobytes()    ,	dtype="uint8"		).reshape(
                  (input_image.height, input_image.width)		)
              lowerCAmelCase__   :Tuple      							=				(input_image / 255) * 2 - 1
              lowerCAmelCase__   :Any      							=				torch.tensor(input_image[np.newaxis, :, :]    ,	dtype=torch.float		).to(self.device		)

              if self.vqvae is not None:
                  lowerCAmelCase__   :str      							=				self.vqvae.encode(torch.unsqueeze(_lowerCAmelCase    ,	0		)		).latent_dist.sample(
                      generator=_lowerCAmelCase		)[0]
                  lowerCAmelCase__   :Dict      							=				self.vqvae.config.scaling_factor * input_images

              if start_step > 0:
                  lowerCAmelCase__   :Dict      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	self.scheduler.timesteps[start_step - 1]		)

              lowerCAmelCase__   :Optional[Any]      							=				(
                  self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
              )
              lowerCAmelCase__   :Dict      							=				int(mask_start_secs * pixels_per_second		)
              lowerCAmelCase__   :Tuple      							=				int(mask_end_secs * pixels_per_second		)
              lowerCAmelCase__   :str      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	torch.tensor(self.scheduler.timesteps[start_step:]		)		)

          for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
              if isinstance(self.unet    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Optional[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              else:
                  lowerCAmelCase__   :Dict      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]

              if isinstance(self.scheduler    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Any      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	eta=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]
              else:
                  lowerCAmelCase__   :List[str]      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]

              if mask is not None:
                  if mask_start > 0:
                      lowerCAmelCase__   :List[Any]      							=				mask[:, step, :, :mask_start]
                  if mask_end > 0:
                      lowerCAmelCase__   :Optional[Any]      							=				mask[:, step, :, -mask_end:]

          if self.vqvae is not None:
              # 0.18215 was scaling factor used in training to ensure unit variance
              lowerCAmelCase__   :Any      							=				1 / self.vqvae.config.scaling_factor * images
              lowerCAmelCase__   :List[Any]      							=				self.vqvae.decode(_lowerCAmelCase		)["sample"]

          lowerCAmelCase__   :Dict      							=				(images / 2 + 0.5).clamp(0    ,	1		)
          lowerCAmelCase__   :Union[str, Any]      							=				images.cpu().permute(0    ,	2    ,	3    ,	1		).numpy()
          lowerCAmelCase__   :Optional[int]      							=				(images * 255).round().astype("uint8"		)
          lowerCAmelCase__   :Optional[int]      							=				list(
              (Image.fromarray(_[:, :, 0]		) for _ in images)
              if images.shape[3] == 1
              else (Image.fromarray(_lowerCAmelCase    ,	mode="RGB"		).convert("L"		) for _ in images)		)

          lowerCAmelCase__   :Optional[Any]      							=				[self.mel.image_to_audio(_lowerCAmelCase		) for _ in images]
          if not return_dict:
              return images, (self.mel.get_sample_rate(), audios)

          return BaseOutput(**AudioPipelineOutput(np.array(_lowerCAmelCase		)[:, np.newaxis, :]		)    ,	**ImagePipelineOutput(_lowerCAmelCase		)		)
      @torch.no_grad()
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = 50		):



          '''simple docstring'''
          assert isinstance(self.scheduler    ,	_lowerCAmelCase		)
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				np.array(
              [np.frombuffer(image.tobytes()    ,	dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
          lowerCAmelCase__   :Dict      							=				(sample / 255) * 2 - 1
          lowerCAmelCase__   :Optional[Any]      							=				torch.Tensor(_lowerCAmelCase		).to(self.device		)

          for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,	(0,)		)		):
              lowerCAmelCase__   :List[Any]      							=				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
              lowerCAmelCase__   :Any      							=				self.scheduler.alphas_cumprod[t]
              lowerCAmelCase__   :List[Any]      							=				(
                  self.scheduler.alphas_cumprod[prev_timestep]
                  if prev_timestep >= 0
                  else self.scheduler.final_alpha_cumprod
              )
              lowerCAmelCase__   :List[str]      							=				1 - alpha_prod_t
              lowerCAmelCase__   :List[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              lowerCAmelCase__   :int      							=				(1 - alpha_prod_t_prev) ** 0.5 * model_output
              lowerCAmelCase__   :List[Any]      							=				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
              lowerCAmelCase__   :Union[str, Any]      							=				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

          return sample



      @staticmethod
      def 			snake_case_      (   _lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				acos(torch.dot(torch.flatten(_lowerCAmelCase		)    ,	torch.flatten(_lowerCAmelCase		)		) / torch.norm(_lowerCAmelCase		) / torch.norm(_lowerCAmelCase		)		)
          return sin((1 - alpha) * theta		) * xa / sin(_lowerCAmelCase		) + sin(alpha * theta		) * xa / sin(_lowerCAmelCase		)



