
"""simple docstring"""




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase__				:   int   							=			logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCAmelCase__				:   Union[str, Any]   							=			'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'



class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		None:



											"""simple docstring"""




											super().__init__()

											self.register_modules(
											    unet=SCREAMING_SNAKE_CASE__       ,	scheduler=SCREAMING_SNAKE_CASE__       ,	)

											SCREAMING_SNAKE_CASE__						:	Tuple									=       None



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__=0   ) ->		Any:



											"""simple docstring"""




											if is_accelerate_available() and is_accelerate_version(""">="""       ,	"""0.14.0"""   ):
																		from accelerate import cpu_offload
											else:
																		raise ImportError("""`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"""   )

											SCREAMING_SNAKE_CASE__						:	int									=       torch.device(F'''cuda:{gpu_id}'''   )

											if self.device.type != "cpu":
																		self.to("""cpu"""       ,	silence_dtype_warnings=SCREAMING_SNAKE_CASE__   )
																		torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											for cpu_offloaded_model in [self.unet]:
																		cpu_offload(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

											if self.safety_checker is not None:
																		cpu_offload(self.safety_checker       ,	execution_device=SCREAMING_SNAKE_CASE__       ,	offload_buffers=SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__=0   ) ->		Union[str, Any]:



											"""simple docstring"""




											if is_accelerate_available() and is_accelerate_version(""">="""       ,	"""0.17.0.dev0"""   ):
																		from accelerate import cpu_offload_with_hook
											else:
																		raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""   )

											SCREAMING_SNAKE_CASE__						:	Any									=       torch.device(F'''cuda:{gpu_id}'''   )

											if self.device.type != "cpu":
																		self.to("""cpu"""       ,	silence_dtype_warnings=SCREAMING_SNAKE_CASE__   )
																		torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											SCREAMING_SNAKE_CASE__						:	Tuple									=       None
											for cpu_offloaded_model in [self.unet]:
																		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Optional[int]									=       cpu_offload_with_hook(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	prev_module_hook=SCREAMING_SNAKE_CASE__   )

											if self.safety_checker is not None:
																		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	int									=       cpu_offload_with_hook(self.safety_checker       ,	SCREAMING_SNAKE_CASE__       ,	prev_module_hook=SCREAMING_SNAKE_CASE__   )

											# We'll offload the last model manually.
											SCREAMING_SNAKE_CASE__						:	List[str]									=       hook



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




											if not hasattr(self.unet       ,	"""_hf_hook"""   ):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(SCREAMING_SNAKE_CASE__       ,	"""_hf_hook"""   )
																		    and hasattr(module._hf_hook       ,	"""execution_device"""   )
																		    and module._hf_hook.execution_device is not None
																		):
																									return torch.device(module._hf_hook.execution_device   )
											return self.device



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       (batch_size, num_channels, height, width)
											if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
																		raise ValueError(
																		    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, but requested an effective batch'''
																		    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

											if latents is None:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       randn_tensor(SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__       ,	device=SCREAMING_SNAKE_CASE__       ,	dtype=SCREAMING_SNAKE_CASE__   )
											else:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       latents.to(device=SCREAMING_SNAKE_CASE__       ,	dtype=SCREAMING_SNAKE_CASE__   )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE__						:	Any									=       latents * self.scheduler.init_noise_sigma
											return latents



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = "pil"   ) ->		Any:



											"""simple docstring"""




											if output_type not in ["pt", "np", "pil"]:
																		raise ValueError(
																		    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''   )

											# Equivalent to diffusers.VaeImageProcessor.denormalize
											SCREAMING_SNAKE_CASE__						:	Dict									=       (sample / 2 + 0.5).clamp(0       ,	1   )
											if output_type == "pt":
																		return sample

											# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       sample.cpu().permute(0       ,	2       ,	3       ,	1   ).numpy()
											if output_type == "np":
																		return sample

											# Output_type must be 'pil'
											SCREAMING_SNAKE_CASE__						:	List[str]									=       self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )
											return sample



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Any:



											"""simple docstring"""




											if self.unet.config.num_class_embeds is not None:
																		if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																									SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(SCREAMING_SNAKE_CASE__       ,	dtype=torch.int   )
																		elif isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																									assert batch_size == 1, "Batch size must be 1 if classes is an int"
																									SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.tensor([class_labels]       ,	dtype=torch.int   )
																		elif class_labels is None:
																									# Randomly generate batch_size class labels
																									# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.randint(0       ,	self.unet.config.num_class_embeds       ,	size=(batch_size,)   )
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       class_labels.to(SCREAMING_SNAKE_CASE__   )
											else:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       None
											return class_labels



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Tuple:



											"""simple docstring"""




											if num_inference_steps is None and timesteps is None:
																		raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""   )

											if num_inference_steps is not None and timesteps is not None:
																		logger.warning(
																		    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
																		    """ `timesteps` will be used over `num_inference_steps`."""   )

											if latents is not None:
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       (batch_size, 3, img_size, img_size)
																		if latents.shape != expected_shape:
																									raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''   )

											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) or callback_steps <= 0)
											):
																		raise ValueError(
																		    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																		    F''' {type(SCREAMING_SNAKE_CASE__   )}.'''   )



				@torch.no_grad()
				@replace_example_docstring(SCREAMING_SNAKE_CASE__   )
				def __call__(self       ,	SCREAMING_SNAKE_CASE__ = 1       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = 1       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = "pil"       ,	SCREAMING_SNAKE_CASE__ = True       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = 1       ,	) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       self.unet.config.sample_size
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self._execution_device

											# 1. Check inputs
											self.check_inputs(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

											# 2. Prepare image latents
											# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.prepare_latents(
											    batch_size=SCREAMING_SNAKE_CASE__       ,	num_channels=self.unet.config.in_channels       ,	height=SCREAMING_SNAKE_CASE__       ,	width=SCREAMING_SNAKE_CASE__       ,	dtype=self.unet.dtype       ,	device=SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__       ,	latents=SCREAMING_SNAKE_CASE__       ,	)

											# 3. Handle class_labels for class-conditional models
											SCREAMING_SNAKE_CASE__						:	int									=       self.prepare_class_labels(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	class_labels=SCREAMING_SNAKE_CASE__   )

											# 4. Prepare timesteps
											if timesteps is not None:
																		self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__       ,	device=SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       self.scheduler.timesteps
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(SCREAMING_SNAKE_CASE__   )
											else:
																		self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	Any									=       self.scheduler.timesteps

											# 5. Denoising loop
											# Multistep sampling: implements Algorithm 1 in the paper
											with self.progress_bar(total=SCREAMING_SNAKE_CASE__   ) as progress_bar:
																		for i, t in enumerate(SCREAMING_SNAKE_CASE__   ):
																									SCREAMING_SNAKE_CASE__						:	Tuple									=       self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.unet(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	class_labels=SCREAMING_SNAKE_CASE__       ,	return_dict=SCREAMING_SNAKE_CASE__   )[0]

																									SCREAMING_SNAKE_CASE__						:	Any									=       self.scheduler.step(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__   )[0]

																									# call the callback, if provided
																									progress_bar.update()
																									if callback is not None and i % callback_steps == 0:
																																callback(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

        # 6. Post-process image sample
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.postprocess_image(SCREAMING_SNAKE_CASE__       ,	output_type=SCREAMING_SNAKE_CASE__   )

											# Offload last model to CPU
											if hasattr(self       ,	"""final_offload_hook"""   ) and self.final_offload_hook is not None:
																		self.final_offload_hook.offload()

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )






"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

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

					# Define train args/config, run train function
					UpperCAmelCase__				:   Dict   							=			argparse.Namespace(cpu=False, mixed_precision='fp16')
					UpperCAmelCase__				:   Any   							=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
					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'])




