




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Tuple     					=				(VQDiffusionScheduler,)







    def    _lowercase					(   self					,  **UpperCamelCase__					)					->				Optional[Any]:
           lowerCamelCase       :				str      			= {
               "num_vec_classes": 4097,
               "num_train_timesteps": 100,
           }

           config.update(**UpperCamelCase__					)
           return config







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Dict:
           lowerCamelCase       :				Tuple      			= 4
           lowerCamelCase       :				List[str]      			= 8
           lowerCamelCase       :				Optional[Any]      			= 8

           lowerCamelCase       :				Union[str, Any]      			= torch.randint(0					,  UpperCamelCase__					,  (batch_size, height * width)					)

           return sample







    @property
    def    _lowercase					(   self					)					->				Optional[Any]:
           assert False







    def    _lowercase					(   self					,  UpperCamelCase__					)					->				Union[str, Any]:
           def model(UpperCamelCase__					,  UpperCamelCase__					,  *UpperCamelCase__					):
                  lowerCamelCase			,				lowerCamelCase       :				Any      			= sample.shape
                  lowerCamelCase       :				Tuple      			= torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)					)
                  lowerCamelCase       :				Tuple      			= F.log_softmax(logits.double()					,  dim=1					).float()
                  return return_value

           return model







    def    _lowercase					(   self					)					->				str:
           for timesteps in [2, 5, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCamelCase__					)







    def    _lowercase					(   self					)					->				List[str]:
           for num_vec_classes in [5, 100, 1000, 4000]:
                  self.check_over_configs(num_vec_classes=UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Union[str, Any]:
           for t in [0, 50, 99]:
                  self.check_over_forward(time_step=UpperCamelCase__					)







    def    _lowercase					(   self					)					->				List[Any]:
           pass












import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 					A				(				*_SCREAMING_SNAKE_CASE   )     ->			Union[str, Any]:
       lowerCamelCase       :				List[str]      			= torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''   )
       with patch_environment(
           world_size=_SCREAMING_SNAKE_CASE			,master_addr="127.0.01"			,master_port="29500"			,mixed_precision=args[1].mixed_precision   ):
              lowerCamelCase       :				Union[str, Any]      			= PrepareForLaunch(_SCREAMING_SNAKE_CASE			,distributed_type="MULTI_GPU"   )
              torch.multiprocessing.start_processes(_SCREAMING_SNAKE_CASE			,args=_SCREAMING_SNAKE_CASE			,nprocs=_SCREAMING_SNAKE_CASE			,start_method="spawn"   )


if __name__ == "__main__":
       # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
       # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

       # on-demand GPU
       # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
       SCREAMING_SNAKE_CASE__	:				Tuple  =       rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
       gpu.up_if_not()

       # on-prem GPU
       # gpu = rh.cluster(
       #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
       #       )

       # Set up remote function
       SCREAMING_SNAKE_CASE__	:				int  =       [
           'pip:./',
           'transformers',
           'datasets',
           'evaluate',
           'tqdm',
           'scipy',
           'scikit-learn',
           'tensorboard',
           'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
       ]
       SCREAMING_SNAKE_CASE__	:				Dict  =       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

       # Define train args/config, run train function
       SCREAMING_SNAKE_CASE__	:				int  =       argparse.Namespace(cpu=False, mixed_precision='fp16')
       SCREAMING_SNAKE_CASE__	:				int  =       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       launch_train_gpu(config, train_args, stream_logs=True)

       # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
       # gpu.install_packages(reqs)
       # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






