code: 
  
  
  '''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
  #
  # 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)
  #
  # 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			:		Optional[int] =			16
  __lowercase			:		Tuple =			32
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Dict     ):
       # Initialize accelerator
       if args.with_tracking:
            __a    : str      					=						Accelerator(
                cpu=args.cpu		,   mixed_precision=args.mixed_precision		,   log_with='all'		,   project_dir=args.project_dir     )
       else:
            __a    : str      					=						Accelerator(cpu=args.cpu		,   mixed_precision=args.mixed_precision     )
  
       if hasattr(args.checkpointing_steps		,   'isdigit'     ):
            if args.checkpointing_steps == "epoch":
                 __a    : List[str]      					=						args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                 __a    : Optional[int]      					=						int(args.checkpointing_steps     )
            else:
                 raise ValueError(
                     F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
       else:
            __a    : int      					=						None
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __a    : Any      					=						config['lr']
       __a    : str      					=						int(config['num_epochs']     )
       __a    : List[Any]      					=						int(config['seed']     )
       __a    : str      					=						int(config['batch_size']     )
  
       # We need to initialize the trackers we use, and also store our configuration
       if args.with_tracking:
            __a    : Any      					=						os.path.split(snake_case__     )[-1].split('.'     )[0]
            accelerator.init_trackers(snake_case__		,   snake_case__     )
  
       __a    : List[Any]      					=						AutoTokenizer.from_pretrained('bert-base-cased'     )
       __a    : Union[str, Any]      					=						load_dataset('glue'		,   'mrpc'     )
       __a    : Union[str, Any]      					=						evaluate.load('glue'		,   'mrpc'     )
  
       def tokenize_function(_SCREAMING_SNAKE_CASE   :     int     ):
            # max_length=None => use the model max length (it's actually the default)
            __a    : List[str]      					=						tokenizer(examples['sentence1']		,   examples['sentence2']		,   truncation=snake_case__		,   max_length=snake_case__     )
            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    : Union[str, Any]      					=						datasets.map(
                snake_case__		,   batched=snake_case__		,   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    : List[Any]      					=						tokenized_datasets.rename_column('label'		,   'labels'     )
  
       # If the batch size is too big we use gradient accumulation
       __a    : Dict      					=						1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            __a    : List[Any]      					=						batch_size // MAX_GPU_BATCH_SIZE
            __a    : Any      					=						MAX_GPU_BATCH_SIZE
  
       def collate_fn(_SCREAMING_SNAKE_CASE   :     Union[str, Any]     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            __a    : Union[str, Any]      					=						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":
                 __a    : Union[str, Any]      					=						16
            elif accelerator.mixed_precision != "no":
                 __a    : str      					=						8
            else:
                 __a    : Optional[int]      					=						None
  
            return tokenizer.pad(
                snake_case__		,   padding='longest'		,   max_length=snake_case__		,   pad_to_multiple_of=snake_case__		,   return_tensors='pt'		,   )
  
       # Instantiate dataloaders.
       __a    : Optional[int]      					=						DataLoader(
           tokenized_datasets['train']		,   shuffle=snake_case__		,   collate_fn=snake_case__		,   batch_size=snake_case__     )
       __a    : str      					=						DataLoader(
           tokenized_datasets['validation']		,   shuffle=snake_case__		,   collate_fn=snake_case__		,   batch_size=snake_case__     )
  
       set_seed(snake_case__     )
  
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       __a    : Optional[int]      					=						AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,   return_dict=snake_case__     )
  
       # 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    : Union[str, Any]      					=						model.to(accelerator.device     )
  
       # Instantiate optimizer
       __a    : Optional[Any]      					=						AdamW(params=model.parameters()		,   lr=snake_case__     )
  
       # Instantiate scheduler
       __a    : List[Any]      					=						get_linear_schedule_with_warmup(
           optimizer=snake_case__		,   num_warmup_steps=100		,   num_training_steps=(len(snake_case__     ) * 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    : Optional[int]      					=						accelerator.prepare(
           snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__     )
  
       # We need to keep track of how many total steps we have iterated over
       __a    : List[Any]      					=						0
       # We also need to keep track of the stating epoch so files are named properly
       __a    : int      					=						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     )
                 __a    : List[Any]      					=						os.path.basename(args.resume_from_checkpoint     )
            else:
                 # Get the most recent checkpoint
                 __a    : List[Any]      					=						[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
                 dirs.sort(key=os.path.getctime     )
                 __a    : Tuple      					=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            __a    : str      					=						os.path.splitext(snake_case__     )[0]
  
            if "epoch" in training_difference:
                 __a    : Union[str, Any]      					=						int(training_difference.replace('epoch_'		,   ''     )     ) + 1
                 __a    : int      					=						None
            else:
                 __a    : List[Any]      					=						int(training_difference.replace('step_'		,   ''     )     )
                 __a    : Dict      					=						resume_step // len(snake_case__     )
                 resume_step -= starting_epoch * len(snake_case__     )
  
      # Now we train the model
       for epoch in range(snake_case__		,   snake_case__     ):
            model.train()
            if args.with_tracking:
                 __a    : Union[str, Any]      					=						0
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                 # We need to skip steps until we reach the resumed step
                 __a    : Optional[int]      					=						accelerator.skip_first_batches(snake_case__		,   snake_case__     )
                 overall_step += resume_step
            else:
                 # After the first iteration though, we need to go back to the original dataloader
                 __a    : Tuple      					=						train_dataloader
            for step, batch in enumerate(snake_case__     ):
                 # We could avoid this line since we set the accelerator with `device_placement=True`.
                 batch.to(accelerator.device     )
                 __a    : Optional[Any]      					=						model(**snake_case__     )
                 __a    : Any      					=						outputs.loss
                 __a    : Tuple      					=						loss / gradient_accumulation_steps
                 # We keep track of the loss at each epoch
                 if args.with_tracking:
                      total_loss += loss.detach().float()
                 accelerator.backward(snake_case__     )
                 if step % gradient_accumulation_steps == 0:
                      optimizer.step()
                      lr_scheduler.step()
                      optimizer.zero_grad()
  
                 overall_step += 1
  
                 if isinstance(snake_case__		,   snake_case__     ):
                      __a    : Any      					=						F"""step_{overall_step}"""
                      if overall_step % checkpointing_steps == 0:
                           if args.output_dir is not None:
                                __a    : List[Any]      					=						os.path.join(args.output_dir		,   snake_case__     )
                           accelerator.save_state(snake_case__     )
  
            model.eval()
            for step, batch in enumerate(snake_case__     ):
                 # We could avoid this line since we set the accelerator with `device_placement=True`.
                 batch.to(accelerator.device     )
                 with torch.no_grad():
                      __a    : Optional[int]      					=						model(**snake_case__     )
                 __a    : int      					=						outputs.logits.argmax(dim=-1     )
                 __a					,       __a    : int      					=						accelerator.gather_for_metrics((predictions, batch['labels'])     )
                 metric.add_batch(
                     predictions=snake_case__		,   references=snake_case__		,   )
  
            __a    : Union[str, Any]      					=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""		,   snake_case__     )
            if args.with_tracking:
                 accelerator.log(
                     {
                         'accuracy': eval_metric['accuracy'],
                         'f1': eval_metric['f1'],
                         'train_loss': total_loss.item() / len(snake_case__     ),
                         'epoch': epoch,
                     }		,   step=snake_case__		,   )
  
            if checkpointing_steps == "epoch":
                 __a    : Union[str, Any]      					=						F"""epoch_{epoch}"""
                 if args.output_dir is not None:
                      __a    : Any      					=						os.path.join(args.output_dir		,   snake_case__     )
                 accelerator.save_state(snake_case__     )
  
       if args.with_tracking:
            accelerator.end_training()
  
  
  
  
  
  
  def 						lowerCamelCase							():
       __a    : Dict      					=						argparse.ArgumentParser(description='Simple example of training script.'     )
       parser.add_argument(
           '--mixed_precision'		,   type=snake_case__		,   default=snake_case__		,   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=snake_case__		,   default=snake_case__		,   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=snake_case__		,   default=snake_case__		,   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=snake_case__		,   default='.'		,   help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,   )
       parser.add_argument(
           '--project_dir'		,   type=snake_case__		,   default='logs'		,   help='Location on where to store experiment tracking logs` and relevent project information'		,   )
       __a    : Optional[int]      					=						parser.parse_args()
       __a    : List[Any]      					=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(snake_case__		,   snake_case__     )
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import unittest
  
  import torch
  
  from diffusers import UNetaDModel
  from diffusers.utils import floats_tensor, slow, torch_device
  
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						4
  						__a    : str      					=						14
  						__a    : int      					=						16
  
  						__a    : List[str]      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_output()
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Union[str, Any]      					=						{
  						    'block_out_channels': (32, 64, 128, 256),
  						    'in_channels': 14,
  						    'out_channels': 14,
  						    'time_embedding_type': 'positional',
  						    'use_timestep_embedding': True,
  						    'flip_sin_to_cos': False,
  						    'freq_shift': 1.0,
  						    'out_block_type': 'OutConv1DBlock',
  						    'mid_block_type': 'MidResTemporalBlock1D',
  						    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
  						    'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
  						    'act_fn': 'mish',
  						}
  						__a    : str      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='unet'							)
  						self.assertIsNotNone(__a							)
  						self.assertEqual(len(loading_info['missing_keys']							)		,       0							)
  
  						model.to(__a							)
  						__a    : Optional[int]      					=						model(**self.dummy_input							)
  
  						assert image is not None, "Make sure output is not None"
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'		,       subfolder='unet'							)
  						torch.manual_seed(0							)
  						if torch.cuda.is_available():
  											torch.cuda.manual_seed_all(0							)
  
  						__a    : str      					=						model.config.in_channels
  						__a    : int      					=						16
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Tuple      					=						model(__a		,       __a							).sample.permute(0		,       2		,       1							)
  
  						__a    : int      					=						output[0, -3:, -3:].flatten()
  						# fmt: off
  						__a    : List[str]      					=						torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348]							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Tuple      					=						'harmonai/maestro-150k'
  						__a    : Any      					=						UNetaDModel.from_pretrained(__a		,       subfolder='unet'							)
  						model.to(__a							)
  
  						__a    : Optional[Any]      					=						6_5536
  						__a    : str      					=						torch.sin(torch.arange(__a							)[None, None, :].repeat(1		,       2		,       1							)							).to(__a							)
  						__a    : int      					=						torch.tensor([1]							).to(__a							)
  
  						with torch.no_grad():
  											__a    : Optional[int]      					=						model(__a		,       __a							).sample
  
  						__a    : Union[str, Any]      					=						output.abs().sum()
  						__a    : Dict      					=						output.abs().max()
  
  						assert (output_sum - 224.0896).abs() < 0.5
  						assert (output_max - 0.0607).abs() < 4E-4
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					UNetaDModel
  	A_			    =					"sample"
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						4
  						__a    : str      					=						14
  						__a    : Optional[int]      					=						16
  
  						__a    : str      					=						floats_tensor((batch_size, num_features, seq_len)							).to(__a							)
  						__a    : Optional[Any]      					=						torch.tensor([10] * batch_size							).to(__a							)
  
  						return {"sample": noise, "timestep": time_step}
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 16)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (4, 14, 1)
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_determinism()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_outputs_equivalence()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_from_save_pretrained_variant()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().test_model_from_pretrained()
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[Any]      					=						self.prepare_init_args_and_inputs_for_common()
  						__a    : int      					=						self.model_class(**__a							)
  						model.to(__a							)
  						model.eval()
  
  						with torch.no_grad():
  											__a    : Optional[Any]      					=						model(**__a							)
  
  											if isinstance(__a		,       __a							):
  																__a    : Any      					=						output.sample
  
  						self.assertIsNotNone(__a							)
  						__a    : Tuple      					=						torch.Size((inputs_dict['sample'].shape[0], 1)							)
  						self.assertEqual(output.shape		,       __a		,       'Input and output shapes do not match'							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : int      					=						{
  						    'in_channels': 14,
  						    'out_channels': 14,
  						    'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
  						    'up_block_types': [],
  						    'out_block_type': 'ValueFunction',
  						    'mid_block_type': 'ValueFunctionMidBlock1D',
  						    'block_out_channels': [32, 64, 128, 256],
  						    'layers_per_block': 1,
  						    'downsample_each_block': True,
  						    'use_timestep_embedding': True,
  						    'freq_shift': 1.0,
  						    'flip_sin_to_cos': False,
  						    'time_embedding_type': 'positional',
  						    'act_fn': 'mish',
  						}
  						__a    : Union[str, Any]      					=						self.dummy_input
  						return init_dict, inputs_dict
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='value_function'							)
  						self.assertIsNotNone(__a							)
  						self.assertEqual(len(vf_loading_info['missing_keys']							)		,       0							)
  
  						value_function.to(__a							)
  						__a    : Optional[Any]      					=						value_function(**self.dummy_input							)
  
  						assert image is not None, "Make sure output is not None"
  
  
  
  
  	@unittest.skipIf(torch_device == 'mps'		,       'mish op not supported in MPS'							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Dict      					=						UNetaDModel.from_pretrained(
  						    'bglick13/hopper-medium-v2-value-function-hor32'		,       output_loading_info=__a		,       subfolder='value_function'							)
  						torch.manual_seed(0							)
  						if torch.cuda.is_available():
  											torch.cuda.manual_seed_all(0							)
  
  						__a    : str      					=						value_function.config.in_channels
  						__a    : Dict      					=						14
  						__a    : List[str]      					=						torch.randn((1, seq_len, num_features)							).permute(
  						    0		,       2		,       1							)  # match original, we can update values and remove
  						__a    : List[Any]      					=						torch.full((num_features,)		,       0							)
  
  						with torch.no_grad():
  											__a    : Any      					=						value_function(__a		,       __a							).sample
  
  						# fmt: off
  						__a    : str      					=						torch.tensor([165.25] * seq_len							)
  						# fmt: on
  						self.assertTrue(torch.allclose(__a		,       __a		,       rtol=1E-3							)							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  