






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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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)
#
# 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
#
########################################################################


__UpperCAmelCase								=						16
__UpperCAmelCase								=						32




def      lowercase__      (     __snake_case    :							Accelerator     ,       __snake_case    :							int = 16  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						AutoTokenizer.from_pretrained('bert-base-cased'  )
					UpperCAmelCase_    :      Any            =						load_dataset('glue'     ,       'mrpc'  )

					def tokenize_function(__snake_case    :							Tuple  ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCAmelCase_    :      Union[str, Any]            =						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():
										UpperCAmelCase_    :      List[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
					UpperCAmelCase_    :      List[str]            =						tokenized_datasets.rename_column('label'     ,       'labels'  )

					def collate_fn(__snake_case    :							Dict  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCAmelCase_    :      Optional[int]            =						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_    :      int            =						16
										elif accelerator.mixed_precision != "no":
															UpperCAmelCase_    :      Union[str, Any]            =						8
										else:
															UpperCAmelCase_    :      List[str]            =						None

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

					# Instantiate dataloaders.
					UpperCAmelCase_    :      int            =						DataLoader(
					    tokenized_datasets['train']     ,       shuffle=__snake_case     ,       collate_fn=__snake_case     ,       batch_size=__snake_case  )
					UpperCAmelCase_    :      Tuple            =						DataLoader(
					    tokenized_datasets['validation']     ,       shuffle=__snake_case     ,       collate_fn=__snake_case     ,       batch_size=__snake_case  )

					return train_dataloader, eval_dataloader


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

		__UpperCAmelCase								=						mocked_dataloaders  # noqa: F811




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,       __snake_case  ) == "1":
										UpperCAmelCase_    :      Optional[int]            =						2
					# New Code #
					UpperCAmelCase_    :      int            =						int(args.gradient_accumulation_steps  )
					UpperCAmelCase_    :      List[Any]            =						int(args.local_sgd_steps  )
					# Initialize accelerator
					UpperCAmelCase_    :      Dict            =						Accelerator(
					    cpu=args.cpu     ,       mixed_precision=args.mixed_precision     ,       gradient_accumulation_steps=__snake_case  )
					if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase_    :      List[str]            =						config['lr']
					UpperCAmelCase_    :      str            =						int(config['num_epochs']  )
					UpperCAmelCase_    :      str            =						int(config['seed']  )
					UpperCAmelCase_    :      str            =						int(config['batch_size']  )

					UpperCAmelCase_    :      List[str]            =						evaluate.load('glue'     ,       'mrpc'  )

					set_seed(__snake_case  )
					UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						get_dataloaders(__snake_case     ,       __snake_case  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCAmelCase_    :      Optional[Any]            =						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).
					UpperCAmelCase_    :      Any            =						model.to(accelerator.device  )

					# Instantiate optimizer
					UpperCAmelCase_    :      Optional[Any]            =						AdamW(params=model.parameters()     ,       lr=__snake_case  )

					# Instantiate scheduler
					UpperCAmelCase_    :      Tuple            =						get_linear_schedule_with_warmup(
					    optimizer=__snake_case     ,       num_warmup_steps=100     ,       num_training_steps=(len(__snake_case  ) * num_epochs)     ,       )

					# 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.
					UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						accelerator.prepare(
					    __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case     ,       __snake_case  )

					# Now we train the model
					for epoch in range(__snake_case  ):
										model.train()
										with LocalSGD(
										    accelerator=__snake_case     ,       model=__snake_case     ,       local_sgd_steps=__snake_case     ,       enabled=local_sgd_steps is not None  ) as local_sgd:
															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  )
																				# New code #
																				# We use the new `accumulate` context manager to perform gradient accumulation
																				# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																				with accelerator.accumulate(__snake_case  ):
																									UpperCAmelCase_    :      str            =						model(**__snake_case  )
																									UpperCAmelCase_    :      List[str]            =						output.loss
																									accelerator.backward(__snake_case  )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()
																									# LocalSGD-specific line
																									local_sgd.step()

										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():
																				UpperCAmelCase_    :      Any            =						model(**__snake_case  )
															UpperCAmelCase_    :      Union[str, Any]            =						outputs.logits.argmax(dim=-1  )
															UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						accelerator.gather_for_metrics((predictions, batch['labels'])  )
															metric.add_batch(
															    predictions=__snake_case     ,       references=__snake_case     ,       )

										UpperCAmelCase_    :      Tuple            =						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:"     ,       __snake_case  )




def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						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.'     ,       )
					# New Code #
					parser.add_argument(
					    '--gradient_accumulation_steps'     ,       type=__snake_case     ,       default=1     ,       help='The number of minibatches to be ran before gradients are accumulated.'     ,       )
					parser.add_argument(
					    '--local_sgd_steps'     ,       type=__snake_case     ,       default=8     ,       help='Number of local SGD steps or None to disable local SGD'  )
					parser.add_argument('--cpu'     ,       action='store_true'     ,       help='If passed, will train on the CPU.'  )
					UpperCAmelCase_    :      Dict            =						parser.parse_args()
					UpperCAmelCase_    :      List[Any]            =						{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(__snake_case     ,       __snake_case  )


if __name__ == "__main__":
		main()














from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

__UpperCAmelCase								=						'\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n        ```\n'



@dataclass
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[PIL.Image.Image, np.ndarray]





class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Any:
							super().__init__()

							self.register_modules(
							    prior=_UpperCamelCase				,		image_encoder=_UpperCamelCase				,		image_processor=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		renderer=_UpperCamelCase				,		)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							List[Any]:
							if latents is None:
												UpperCAmelCase_    :      str            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=_UpperCamelCase   )
							else:
												if latents.shape != shape:
																	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
												UpperCAmelCase_    :      Tuple            =						latents.to(_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						latents * scheduler.init_noise_sigma
							return latents



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Union[str, Any]:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'   )

							UpperCAmelCase_    :      int            =						torch.device(f"cuda:{gpu_id}"   )

							UpperCAmelCase_    :      int            =						[self.image_encoder, self.prior]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(_UpperCamelCase				,		_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							if self.device != torch.device('meta'   ) or not hasattr(self.image_encoder				,		'_hf_hook'   ):
												return self.device
							for module in self.image_encoder.modules():
												if (
												    hasattr(_UpperCamelCase				,		'_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 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							str:
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		torch.Tensor   ):
												UpperCAmelCase_    :      int            =						torch.cat(_UpperCamelCase				,		axis=0   ) if image[0].ndim == 4 else torch.stack(_UpperCamelCase				,		axis=0   )

							if not isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      Optional[int]            =						self.image_processor(_UpperCamelCase				,		return_tensors='pt'   ).pixel_values[0].unsqueeze(0   )

							UpperCAmelCase_    :      Tuple            =						image.to(dtype=self.image_encoder.dtype				,		device=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						self.image_encoder(_UpperCamelCase   )['last_hidden_state']
							UpperCAmelCase_    :      Union[str, Any]            =						image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

							UpperCAmelCase_    :      List[str]            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      Dict            =						torch.zeros_like(_UpperCamelCase   )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												UpperCAmelCase_    :      Optional[int]            =						torch.cat([negative_image_embeds, image_embeds]   )

							return image_embeds



		@torch.no_grad()
		@replace_example_docstring(_UpperCamelCase   )
		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_5				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 6_4				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							Union[str, Any]:

							if isinstance(_UpperCamelCase				,		PIL.Image.Image   ):
												UpperCAmelCase_    :      Tuple            =						1
							elif isinstance(_UpperCamelCase				,		torch.Tensor   ):
												UpperCAmelCase_    :      str            =						image.shape[0]
							elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and isinstance(image[0]				,		(torch.Tensor, PIL.Image.Image)   ):
												UpperCAmelCase_    :      Optional[int]            =						len(_UpperCamelCase   )
							else:
												raise ValueError(
												    f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_UpperCamelCase   )}"   )

							UpperCAmelCase_    :      Tuple            =						self._execution_device

							UpperCAmelCase_    :      str            =						batch_size * num_images_per_prompt

							UpperCAmelCase_    :      str            =						guidance_scale > 1.0
							UpperCAmelCase_    :      str            =						self._encode_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							# prior

							self.scheduler.set_timesteps(_UpperCamelCase				,		device=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.scheduler.timesteps

							UpperCAmelCase_    :      int            =						self.prior.config.num_embeddings
							UpperCAmelCase_    :      Any            =						self.prior.config.embedding_dim

							UpperCAmelCase_    :      List[str]            =						self.prepare_latents(
							    (batch_size, num_embeddings * embedding_dim)				,		image_embeds.dtype				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		self.scheduler				,		)

							# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
							UpperCAmelCase_    :      List[Any]            =						latents.reshape(latents.shape[0]				,		_UpperCamelCase				,		_UpperCamelCase   )

							for i, t in enumerate(self.progress_bar(_UpperCamelCase   )   ):
												# expand the latents if we are doing classifier free guidance
												UpperCAmelCase_    :      Tuple            =						torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
												UpperCAmelCase_    :      Optional[Any]            =						self.scheduler.scale_model_input(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      int            =						self.prior(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		proj_embedding=_UpperCamelCase				,		).predicted_image_embedding

												# remove the variance
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						noise_pred.split(
												    scaled_model_input.shape[2]				,		dim=2   )  # batch_size, num_embeddings, embedding_dim

												if do_classifier_free_guidance is not None:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_    :      List[Any]            =						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

												UpperCAmelCase_    :      List[str]            =						self.scheduler.step(
												    _UpperCamelCase				,		timestep=_UpperCamelCase				,		sample=_UpperCamelCase				,		).prev_sample

							if output_type == "latent":
												return ShapEPipelineOutput(images=_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						[]
							for i, latent in enumerate(_UpperCamelCase   ):
												print()
												UpperCAmelCase_    :      List[str]            =						self.renderer.decode(
												    latent[None, :]				,		_UpperCamelCase				,		size=_UpperCamelCase				,		ray_batch_size=4_0_9_6				,		n_coarse_samples=6_4				,		n_fine_samples=1_2_8				,		)

												images.append(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						torch.stack(_UpperCamelCase   )

							if output_type not in ["np", "pil"]:
												raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}"   )

							UpperCAmelCase_    :      Dict            =						images.cpu().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[str]            =						[self.numpy_to_pil(_UpperCamelCase   ) for image in images]

							# 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 (images,)

							return ShapEPipelineOutput(images=_UpperCamelCase   )






