





'''simple docstring'''

import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowercase__		:    Dict =       16
lowercase__		:    Any =       32







def       a__       (       lowercase      :  str   )	->	Dict:





     """simple docstring"""

     return int(x / 2**20   )
class        __lowerCAmelCase  :






       """simple docstring"""





       def __enter__(							self     :	Optional[int]  )					->  Tuple:

            '''simple docstring'''




            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
            _UpperCamelCase       					= torch.cuda.memory_allocated()
            _UpperCamelCase       					= psutil.Process()

            _UpperCamelCase       					= self.cpu_mem_used()
            _UpperCamelCase       					= True
            _UpperCamelCase       					= threading.Thread(target=self.peak_monitor_func  )
            _UpperCamelCase       					= True
            peak_monitor_thread.start()
            return self





       def snake_case__		(							self     :	int  )					->  Dict:

            '''simple docstring'''




            return self.process.memory_info().rss





       def snake_case__		(							self     :	Optional[int]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase       					= -1

            while True:
                 _UpperCamelCase       					= max(self.cpu_mem_used() ,				self.cpu_peak  )

                 # can't sleep or will not catch the peak right (this comment is here on purpose)
                 # time.sleep(0.001) # 1msec

                 if not self.peak_monitoring:
                      break







       def __exit__(							self     :	Optional[int] ,				*lowerCAmelCase__     :	Any  )					->  str:

            '''simple docstring'''




            _UpperCamelCase       					= False

            gc.collect()
            torch.cuda.empty_cache()
            _UpperCamelCase       					= torch.cuda.memory_allocated()
            _UpperCamelCase       					= torch.cuda.max_memory_allocated()
            _UpperCamelCase       					= bamb(self.end - self.begin  )
            _UpperCamelCase       					= bamb(self.peak - self.begin  )

            _UpperCamelCase       					= self.cpu_mem_used()
            _UpperCamelCase       					= bamb(self.cpu_end - self.cpu_begin  )
            _UpperCamelCase       					= bamb(self.cpu_peak - self.cpu_begin  )
            # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

   lowercase__		:    int =       mocked_dataloaders  # noqa: F811







def       a__       (       lowercase      :  Union[str, Any],      lowercase      :  Optional[Any]   )	->	List[str]:





     """simple docstring"""

     if os.environ.get('''TESTING_MOCKED_DATALOADERS''',      lowercase   ) == "1":
          _UpperCamelCase       					= 2

     # New Code #
     # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
     _UpperCamelCase       					= FullyShardedDataParallelPlugin(
         state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase,      ranka_only=lowercase   ),      optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase,      ranka_only=lowercase   ),      )

     # Initialize accelerator
     if args.with_tracking:
          _UpperCamelCase       					= Accelerator(
              cpu=args.cpu,      mixed_precision=args.mixed_precision,      log_with='''wandb''',      project_dir=args.logging_dir,      fsdp_plugin=lowercase,      )
     else:
          _UpperCamelCase       					= Accelerator(fsdp_plugin=lowercase   )
     accelerator.print(accelerator.distributed_type   )

     if hasattr(args.checkpointing_steps,      '''isdigit'''   ):
          if args.checkpointing_steps == "epoch":
               _UpperCamelCase       					= args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               _UpperCamelCase       					= int(args.checkpointing_steps   )
          else:
               raise ValueError(
                   F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
     else:
          _UpperCamelCase       					= None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _UpperCamelCase       					= config['''lr''']
     _UpperCamelCase       					= int(config['''num_epochs''']   )
     _UpperCamelCase       					= int(config['''seed''']   )
     _UpperCamelCase       					= int(config['''batch_size''']   )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          _UpperCamelCase       					= vars(lowercase   )
          accelerator.init_trackers('''fsdp_glue_no_trainer''',      lowercase   )

     _UpperCamelCase       					= AutoTokenizer.from_pretrained(args.model_name_or_path   )
     _UpperCamelCase       					= load_dataset('''glue''',      '''mrpc'''   )
     _UpperCamelCase       					= evaluate.load('''glue''',      '''mrpc'''   )

     def tokenize_function(lowercase      :  str   ):
          # max_length=None => use the model max length (it's actually the default)
          _UpperCamelCase       					= tokenizer(examples['''sentence1'''],      examples['''sentence2'''],      truncation=lowercase,      max_length=lowercase   )
          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       					= datasets.map(
              lowercase,      batched=lowercase,      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       					= tokenized_datasets.rename_column('''label''',      '''labels'''   )

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

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

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

     # Instantiate dataloaders.
     _UpperCamelCase       					= DataLoader(
         tokenized_datasets['''train'''],      shuffle=lowercase,      collate_fn=lowercase,      batch_size=lowercase   )
     _UpperCamelCase       					= DataLoader(
         tokenized_datasets['''validation'''],      shuffle=lowercase,      collate_fn=lowercase,      batch_size=lowercase   )

     set_seed(lowercase   )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     _UpperCamelCase       					= AutoModelForSequenceClassification.from_pretrained(
         args.model_name_or_path,      return_dict=lowercase,      low_cpu_mem_usage=lowercase   )

     # New Code #
     # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
     _UpperCamelCase       					= accelerator.prepare(lowercase   )
     accelerator.print(lowercase   )

     # Instantiate optimizer
     # New Code #
     # For FSDP feature, at present it doesn't support multiple parameter groups,
     # so we need to create a single parameter group for the whole model
     _UpperCamelCase       					= torch.optim.AdamW(params=model.parameters(),      lr=lowercase,      weight_decay=2e-4   )

     # Instantiate scheduler
     _UpperCamelCase       					= get_linear_schedule_with_warmup(
         optimizer=lowercase,      num_warmup_steps=10,      num_training_steps=(len(lowercase   ) * num_epochs) // gradient_accumulation_steps,      )

     # New Code #
     # For FSDP feature, prepare everything except the model as we have already prepared the model
     # before creating the optimizer
     # 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       					= accelerator.prepare(
         lowercase,      lowercase,      lowercase,      lowercase   )

     _UpperCamelCase       					= 0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""   )
               accelerator.load_state(args.resume_from_checkpoint   )
               _UpperCamelCase       					= os.path.basename(args.resume_from_checkpoint   )
          else:
               # Get the most recent checkpoint
               _UpperCamelCase       					= [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
               dirs.sort(key=os.path.getctime   )
               _UpperCamelCase       					= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          _UpperCamelCase       					= os.path.splitext(lowercase   )[0]

          if "epoch" in training_difference:
               num_epochs -= int(training_difference.replace('''epoch_''',      ''''''   )   )
               _UpperCamelCase       					= None
          else:
               _UpperCamelCase       					= int(training_difference.replace('''step_''',      ''''''   )   )
               num_epochs -= resume_step // len(lowercase   )
               # If resuming by step, we also need to know exactly how far into the DataLoader we went
               _UpperCamelCase       					= (num_epochs * len(lowercase   )) - resume_step

    # Now we train the model
     for epoch in range(lowercase   ):
          # New Code #
          # context manager to track the peak memory usage during the training epoch
          with TorchTracemalloc() as tracemalloc:
               model.train()
               if args.with_tracking:
                    _UpperCamelCase       					= 0
               for step, batch in enumerate(lowercase   ):
                    # We need to skip steps until we reach the resumed step
                    if args.resume_from_checkpoint and epoch == 0:
                         if resume_step is not None and step < resume_step:
                              pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device   )
                    _UpperCamelCase       					= model(**lowercase   )
                    _UpperCamelCase       					= outputs.loss
                    _UpperCamelCase       					= loss / gradient_accumulation_steps
                    # We keep track of the loss at each epoch
                    if args.with_tracking:
                         total_loss += loss.detach().float()
                    accelerator.backward(lowercase   )
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # accelerator.print(lr_scheduler.get_lr())

                    overall_step += 1

                    if isinstance(lowercase,      lowercase   ):
                         _UpperCamelCase       					= F"""step_{overall_step}"""
                         if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                   _UpperCamelCase       					= os.path.join(args.output_dir,      lowercase   )
                              accelerator.save_state(lowercase   )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin   )   )   )
          accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used   )   )
          accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked   )   )
          accelerator.print(
              '''Total Peak Memory consumed during the train (max): {}'''.format(
                  tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin   ),
                   },      step=lowercase,      )

          # New Code #
          # context manager to track the peak memory usage during the evaluation
          with TorchTracemalloc() as tracemalloc:
               model.eval()
               for step, batch in enumerate(lowercase   ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device   )
                    with torch.no_grad():
                         _UpperCamelCase       					= model(**lowercase   )
                    _UpperCamelCase       					= outputs.logits.argmax(dim=-1   )
                    _UpperCamelCase					,       _UpperCamelCase       					= accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
                    metric.add_batch(
                        predictions=lowercase,      references=lowercase,      )

               _UpperCamelCase       					= metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F"""epoch {epoch}:""",      lowercase   )
               if args.with_tracking:
                    accelerator.log(
                        {
                            '''accuracy''': eval_metric['''accuracy'''],
                            '''f1''': eval_metric['''f1'''],
                            '''train_loss''': total_loss.item() / len(lowercase   ),
                        },      step=lowercase,      )

               if checkpointing_steps == "epoch":
                    _UpperCamelCase       					= F"""epoch_{epoch}"""
                    if args.output_dir is not None:
                         _UpperCamelCase       					= os.path.join(args.output_dir,      lowercase   )
                    accelerator.save_state(lowercase   )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin   )   )   )
          accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used   )   )
          accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked   )   )
          accelerator.print(
              '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin   ),
                   },      step=lowercase,      )

     if args.with_tracking:
          accelerator.end_training()







def       a__       (       )	->	Optional[int]:





     """simple docstring"""

     _UpperCamelCase       					= argparse.ArgumentParser(description='''Simple example of training script.'''   )
     parser.add_argument(
         '''--mixed_precision''',      type=lowercase,      default=lowercase,      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(
         '''--checkpointing_steps''',      type=lowercase,      default=lowercase,      help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.''',      )
     parser.add_argument(
         '''--resume_from_checkpoint''',      type=lowercase,      default=lowercase,      help='''If the training should continue from a checkpoint folder.''',      )
     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(
         '''--output_dir''',      type=lowercase,      default='''.''',      help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''',      )
     parser.add_argument(
         '''--logging_dir''',      type=lowercase,      default='''logs''',      help='''Location on where to store experiment tracking logs`''',      )
     parser.add_argument(
         '''--model_name_or_path''',      type=lowercase,      help='''Path to pretrained model or model identifier from huggingface.co/models.''',      required=lowercase,      )
     _UpperCamelCase       					= parser.parse_args()
     _UpperCamelCase       					= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(lowercase,      lowercase   )


if __name__ == "__main__":
   main()










'''simple docstring'''

from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .consistency_models import ConsistencyModelPipeline
   from .dance_diffusion import DanceDiffusionPipeline
   from .ddim import DDIMPipeline
   from .ddpm import DDPMPipeline
   from .dit import DiTPipeline
   from .latent_diffusion import LDMSuperResolutionPipeline
   from .latent_diffusion_uncond import LDMPipeline
   from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
   from .pndm import PNDMPipeline
   from .repaint import RePaintPipeline
   from .score_sde_ve import ScoreSdeVePipeline
   from .stochastic_karras_ve import KarrasVePipeline

try:
   if not (is_torch_available() and is_librosa_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
   from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
   if not (is_torch_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
   from .audioldm import AudioLDMPipeline
   from .controlnet import (
       StableDiffusionControlNetImgaImgPipeline,
       StableDiffusionControlNetInpaintPipeline,
       StableDiffusionControlNetPipeline,
   )
   from .deepfloyd_if import (
       IFImgaImgPipeline,
       IFImgaImgSuperResolutionPipeline,
       IFInpaintingPipeline,
       IFInpaintingSuperResolutionPipeline,
       IFPipeline,
       IFSuperResolutionPipeline,
   )
   from .kandinsky import (
       KandinskyImgaImgPipeline,
       KandinskyInpaintPipeline,
       KandinskyPipeline,
       KandinskyPriorPipeline,
   )
   from .kandinskya_a import (
       KandinskyVaaControlnetImgaImgPipeline,
       KandinskyVaaControlnetPipeline,
       KandinskyVaaImgaImgPipeline,
       KandinskyVaaInpaintPipeline,
       KandinskyVaaPipeline,
       KandinskyVaaPriorEmbaEmbPipeline,
       KandinskyVaaPriorPipeline,
   )
   from .latent_diffusion import LDMTextToImagePipeline
   from .paint_by_example import PaintByExamplePipeline
   from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
   from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
   from .stable_diffusion import (
       CycleDiffusionPipeline,
       StableDiffusionAttendAndExcitePipeline,
       StableDiffusionDepthaImgPipeline,
       StableDiffusionDiffEditPipeline,
       StableDiffusionImageVariationPipeline,
       StableDiffusionImgaImgPipeline,
       StableDiffusionInpaintPipeline,
       StableDiffusionInpaintPipelineLegacy,
       StableDiffusionInstructPixaPixPipeline,
       StableDiffusionLatentUpscalePipeline,
       StableDiffusionLDMaDPipeline,
       StableDiffusionModelEditingPipeline,
       StableDiffusionPanoramaPipeline,
       StableDiffusionParadigmsPipeline,
       StableDiffusionPipeline,
       StableDiffusionPixaPixZeroPipeline,
       StableDiffusionSAGPipeline,
       StableDiffusionUpscalePipeline,
       StableUnCLIPImgaImgPipeline,
       StableUnCLIPPipeline,
   )
   from .stable_diffusion_safe import StableDiffusionPipelineSafe
   from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
   from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
   from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
   from .versatile_diffusion import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
   from .vq_diffusion import VQDiffusionPipeline


try:
   if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
   from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
   if not is_onnx_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_onnx_objects import *  # noqa F403
else:
   from .onnx_utils import OnnxRuntimeModel

try:
   if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
   from .stable_diffusion import (
       OnnxStableDiffusionImgaImgPipeline,
       OnnxStableDiffusionInpaintPipeline,
       OnnxStableDiffusionInpaintPipelineLegacy,
       OnnxStableDiffusionPipeline,
       OnnxStableDiffusionUpscalePipeline,
       StableDiffusionOnnxPipeline,
   )

try:
   if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
   from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
   if not (is_flax_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
   from .controlnet import FlaxStableDiffusionControlNetPipeline
   from .stable_diffusion import (
       FlaxStableDiffusionImgaImgPipeline,
       FlaxStableDiffusionInpaintPipeline,
       FlaxStableDiffusionPipeline,
   )
try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline



