


import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()







class 					snake_case__(				unittest.TestCase				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Any       =				"""hf-internal-testing/tiny-stable-diffusion-pipe"""
						SCREAMING_SNAKE_CASE__   :	str       =				1
						SCREAMING_SNAKE_CASE__   :	Dict       =				77
						SCREAMING_SNAKE_CASE__   :	Optional[Any]       =				32
						SCREAMING_SNAKE_CASE__   :	List[Any]       =				4
						SCREAMING_SNAKE_CASE__   :	str       =				64
						SCREAMING_SNAKE_CASE__   :	List[str]       =				torch.manual_seed(0				)




						def     lowercase_			(					self ,    __lowercase=0.99_99				)       ->     int:
										lowerCAmelCase_			:   List[str]     					=					UNetaDConditionModel.from_pretrained(self.model_id ,    subfolder='''unet'''				)
										lowerCAmelCase_			:   Union[str, Any]     					=					unet.to(__UpperCamelCase				)
										lowerCAmelCase_			:   int     					=					EMAModel(unet.parameters() ,    decay=__UpperCamelCase ,    model_cls=__UpperCamelCase ,    model_config=unet.config				)
										return unet, ema_unet




						def     lowercase_			(					self				)       ->     Optional[int]:
										lowerCAmelCase_			:   Dict     					=					torch.randn(
										    self.batch_size ,    self.num_in_channels ,    self.latent_height ,    self.latent_width ,    generator=self.generator				).to(__UpperCamelCase				)
										lowerCAmelCase_			:   Optional[Any]     					=					torch.randint(0 ,    1_0_0_0 ,    size=(self.batch_size,) ,    generator=self.generator				).to(__UpperCamelCase				)
										lowerCAmelCase_			:   Tuple     					=					torch.randn(
										    self.batch_size ,    self.prompt_length ,    self.text_encoder_hidden_dim ,    generator=self.generator				).to(__UpperCamelCase				)
										return noisy_latents, timesteps, encoder_hidden_states




						def     lowercase_			(					self ,    __lowercase				)       ->     Any:
										lowerCAmelCase_			:   Optional[int]     					=					{}
										for k, param in unet.state_dict().items():
														lowerCAmelCase_			:   Optional[int]     					=					torch.randn_like(__UpperCamelCase				) + (param * torch.randn_like(__UpperCamelCase				))
														updated_state_dict.update({k: updated_param}				)
										unet.load_state_dict(__UpperCamelCase				)
										return unet




						def     lowercase_			(					self				)       ->     List[Any]:
										lowerCAmelCase_					,	lowerCAmelCase_			:   int     					=					self.get_models()
										# Take the first (hypothetical) EMA step.
										ema_unet.step(unet.parameters()				)
										assert ema_unet.optimization_step == 1

										# Take two more.
										for _ in range(2				):
														ema_unet.step(unet.parameters()				)
										assert ema_unet.optimization_step == 3




						def     lowercase_			(					self				)       ->     Any:
										lowerCAmelCase_					,	lowerCAmelCase_			:   Tuple     					=					self.get_models()
										# Since the `unet` is not being updated (i.e., backprop'd)
										# there won't be any difference between the `params` of `unet`
										# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
										ema_unet.step(unet.parameters()				)
										lowerCAmelCase_			:   List[str]     					=					list(unet.parameters()				)
										for s_param, param in zip(ema_unet.shadow_params ,    __UpperCamelCase				):
														assert torch.allclose(__UpperCamelCase ,    __UpperCamelCase				)

										# The above holds true even if we call `ema.step()` multiple times since
										# `unet` params are still not being updated.
										for _ in range(4				):
														ema_unet.step(unet.parameters()				)
										for s_param, param in zip(ema_unet.shadow_params ,    __UpperCamelCase				):
														assert torch.allclose(__UpperCamelCase ,    __UpperCamelCase				)




						def     lowercase_			(					self				)       ->     Dict:
										lowerCAmelCase_					,	lowerCAmelCase_			:   Optional[int]     					=					self.get_models()
										# Here we simulate the parameter updates for `unet`. Since there might
										# be some parameters which are initialized to zero we take extra care to
										# initialize their values to something non-zero before the multiplication.
										lowerCAmelCase_			:   Optional[int]     					=					self.simulate_backprop(__UpperCamelCase				)

										# Take the EMA step.
										ema_unet.step(unet_pseudo_updated_step_one.parameters()				)

										# Now the EMA'd parameters won't be equal to the original model parameters.
										lowerCAmelCase_			:   Optional[Any]     					=					list(unet_pseudo_updated_step_one.parameters()				)
										for s_param, param in zip(ema_unet.shadow_params ,    __UpperCamelCase				):
														assert ~torch.allclose(__UpperCamelCase ,    __UpperCamelCase				)

										# Ensure this is the case when we take multiple EMA steps.
										for _ in range(4				):
														ema_unet.step(unet.parameters()				)
										for s_param, param in zip(ema_unet.shadow_params ,    __UpperCamelCase				):
														assert ~torch.allclose(__UpperCamelCase ,    __UpperCamelCase				)




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										lowerCAmelCase_					,	lowerCAmelCase_			:   int     					=					self.get_models()

										# First backprop + EMA
										lowerCAmelCase_			:   Tuple     					=					self.simulate_backprop(__UpperCamelCase				)
										ema_unet.step(unet_step_one.parameters()				)
										lowerCAmelCase_			:   Optional[int]     					=					ema_unet.shadow_params

										# Second backprop + EMA
										lowerCAmelCase_			:   Optional[int]     					=					self.simulate_backprop(__UpperCamelCase				)
										ema_unet.step(unet_step_two.parameters()				)
										lowerCAmelCase_			:   Union[str, Any]     					=					ema_unet.shadow_params

										for step_one, step_two in zip(__UpperCamelCase ,    __UpperCamelCase				):
														assert ~torch.allclose(__UpperCamelCase ,    __UpperCamelCase				)




						def     lowercase_			(					self				)       ->     Tuple:
										lowerCAmelCase_					,	lowerCAmelCase_			:   Optional[int]     					=					self.get_models(decay=0.0				)
										lowerCAmelCase_			:   Dict     					=					self.simulate_backprop(__UpperCamelCase				)
										ema_unet.step(unet_step_one.parameters()				)
										lowerCAmelCase_			:   Dict     					=					ema_unet.shadow_params

										lowerCAmelCase_			:   List[Any]     					=					self.simulate_backprop(__UpperCamelCase				)
										ema_unet.step(unet_step_two.parameters()				)
										lowerCAmelCase_			:   Union[str, Any]     					=					ema_unet.shadow_params

										for step_one, step_two in zip(__UpperCamelCase ,    __UpperCamelCase				):
														assert torch.allclose(__UpperCamelCase ,    __UpperCamelCase				)




						@skip_mps
						def     lowercase_			(					self				)       ->     Dict:
										lowerCAmelCase_					,	lowerCAmelCase_			:   Any     					=					self.get_models()
										lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_			:   Optional[int]     					=					self.get_dummy_inputs()

										with tempfile.TemporaryDirectory() as tmpdir:
														ema_unet.save_pretrained(__UpperCamelCase				)
														lowerCAmelCase_			:   int     					=					UNetaDConditionModel.from_pretrained(__UpperCamelCase ,    model_cls=__UpperCamelCase				)
														lowerCAmelCase_			:   str     					=					loaded_unet.to(unet.device				)

										# Since no EMA step has been performed the outputs should match.
										lowerCAmelCase_			:   Tuple     					=					unet(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase				).sample
										lowerCAmelCase_			:   Dict     					=					loaded_unet(__UpperCamelCase ,    __UpperCamelCase ,    __UpperCamelCase				).sample

										assert torch.allclose(__UpperCamelCase ,    __UpperCamelCase ,    atol=1e-4				)

import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   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
      _A							=       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
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''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'])



