


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

UpperCAmelCase							 =					"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""

def 			A_      (	__a     :				str ,		__a     :				int ,		__a     :				Dict=8		):

				"""simple docstring"""
				a__				      =		height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				a__				      =		width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  ):
											super().__init__()

											self.register_modules(
											    unet=__UpperCamelCase  ,  scheduler=__UpperCamelCase  ,  movq=__UpperCamelCase  ,  )
											a__				      =		2 ** (len(self.movq.config.block_out_channels		) - 1)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											if latents is None:
															a__				      =		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}'''		)
															a__				      =		latents.to(__UpperCamelCase		)

											a__				      =		latents * scheduler.init_noise_sigma
											return latents


							def _a      (							self  ,  a_=0		):
											if is_accelerate_available():
															from accelerate import cpu_offload
											else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""		)

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

											a__				      =		[
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																			cpu_offload(__UpperCamelCase  ,  __UpperCamelCase		)


							def _a      (							self  ,  a_=0		):
											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."""		)

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

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

											a__				      =		None
											for cpu_offloaded_model in [self.unet, self.movq]:
															a__      ,			a__				      =		cpu_offload_with_hook(__UpperCamelCase  ,  __UpperCamelCase  ,  prev_module_hook=__UpperCamelCase		)

											# We'll offload the last model manually.
											a__				      =		hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def _a      (							self		):
											if not hasattr(self.unet  ,  """_hf_hook"""		):
															return self.device
											for module in self.unet.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







							@torch.no_grad()
							@replace_example_docstring(__UpperCamelCase		)
							def __call__(							self  ,  a_  ,  a_  ,  a_  ,  a_ = 512  ,  a_ = 512  ,  a_ = 100  ,  a_ = 4.0  ,  a_ = 1  ,  a_ = None  ,  a_ = None  ,  a_ = "pil"  ,  a_ = True  ,  ):
											a__				      =		self._execution_device

											a__				      =		guidance_scale > 1.0

											if isinstance(__UpperCamelCase  ,  __UpperCamelCase		):
															a__				      =		torch.cat(__UpperCamelCase  ,  dim=0		)
											if isinstance(__UpperCamelCase  ,  __UpperCamelCase		):
															a__				      =		torch.cat(__UpperCamelCase  ,  dim=0		)
											if isinstance(__UpperCamelCase  ,  __UpperCamelCase		):
															a__				      =		torch.cat(__UpperCamelCase  ,  dim=0		)

											a__				      =		image_embeds.shape[0] * num_images_per_prompt

											if do_classifier_free_guidance:
															a__				      =		image_embeds.repeat_interleave(__UpperCamelCase  ,  dim=0		)
															a__				      =		negative_image_embeds.repeat_interleave(__UpperCamelCase  ,  dim=0		)
															a__				      =		hint.repeat_interleave(__UpperCamelCase  ,  dim=0		)

											a__				      =		torch.cat([negative_image_embeds, image_embeds]  ,  dim=0		).to(dtype=self.unet.dtype  ,  device=__UpperCamelCase		)
											a__				      =		torch.cat([hint, hint]  ,  dim=0		).to(dtype=self.unet.dtype  ,  device=__UpperCamelCase		)
											self.scheduler.set_timesteps(__UpperCamelCase  ,  device=__UpperCamelCase		)
											a__				      =		self.scheduler.timesteps

											a__				      =		self.movq.config.latent_channels

											a__      ,			a__				      =		downscale_height_and_width(__UpperCamelCase  ,  __UpperCamelCase  ,  self.movq_scale_factor		)

											# create initial latent
											a__				      =		self.prepare_latents(
											    (batch_size, num_channels_latents, height, width)  ,  image_embeds.dtype  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  self.scheduler  ,  )

											for i, t in enumerate(self.progress_bar(__UpperCamelCase		)		):
															# expand the latents if we are doing classifier free guidance
															a__				      =		torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

															a__				      =		{"""image_embeds""": image_embeds, """hint""": hint}
															a__				      =		self.unet(
															    sample=__UpperCamelCase  ,  timestep=__UpperCamelCase  ,  encoder_hidden_states=__UpperCamelCase  ,  added_cond_kwargs=__UpperCamelCase  ,  return_dict=__UpperCamelCase  ,  )[0]

															if do_classifier_free_guidance:
																			a__      ,			a__				      =		noise_pred.split(latents.shape[1]  ,  dim=1		)
																			a__      ,			a__				      =		noise_pred.chunk(2		)
																			a__      ,			a__				      =		variance_pred.chunk(2		)
																			a__				      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			a__				      =		torch.cat([noise_pred, variance_pred_text]  ,  dim=1		)

															if not (
															    hasattr(self.scheduler.config  ,  """variance_type"""		)
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																			a__      ,			a__				      =		noise_pred.split(latents.shape[1]  ,  dim=1		)

															# compute the previous noisy sample x_t -> x_t-1
															a__				      =		self.scheduler.step(
															    __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  generator=__UpperCamelCase  ,  )[0]
											# post-processing
											a__				      =		self.movq.decode(__UpperCamelCase  ,  force_not_quantize=__UpperCamelCase		)["""sample"""]

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

											if output_type in ["np", "pil"]:
															a__				      =		image * 0.5 + 0.5
															a__				      =		image.clamp(0  ,  1		)
															a__				      =		image.cpu().permute(0  ,  2  ,  3  ,  1		).float().numpy()

											if output_type == "pil":
															a__				      =		self.numpy_to_pil(__UpperCamelCase		)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=__UpperCamelCase		)






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 			A_      (	__a     :				Accelerator ,		__a     :				int = 16		):

				"""simple docstring"""
				a__				      =		AutoTokenizer.from_pretrained("""bert-base-cased"""		)
				a__				      =		load_dataset("""glue""" ,		"""mrpc"""		)

				def tokenize_function(__a     :				int		):
								# max_length=None => use the model max length (it's actually the default)
								a__				      =		tokenizer(examples["""sentence1"""] ,		examples["""sentence2"""] ,		truncation=__a ,		max_length=__a		)
								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__				      =		datasets.map(
								    __a ,		batched=__a ,		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__				      =		tokenized_datasets.rename_column("""label""" ,		"""labels"""		)

				def collate_fn(__a     :				Optional[int]		):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								a__				      =		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__				      =		16
								elif accelerator.mixed_precision != "no":
												a__				      =		8
								else:
												a__				      =		None

								return tokenizer.pad(
								    __a ,		padding="""longest""" ,		max_length=__a ,		pad_to_multiple_of=__a ,		return_tensors="""pt""" ,		)

				# Instantiate dataloaders.
				a__				      =		DataLoader(
				    tokenized_datasets["""train"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)
				a__				      =		DataLoader(
				    tokenized_datasets["""validation"""] ,		shuffle=__a ,		collate_fn=__a ,		batch_size=__a		)

				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 			A_      (	__a     :				List[Any] ,		__a     :				Tuple		):

				"""simple docstring"""
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,		__a		) == "1":
								a__				      =		2
				# New Code #
				a__				      =		int(args.gradient_accumulation_steps		)
				a__				      =		int(args.local_sgd_steps		)
				# Initialize accelerator
				a__				      =		Accelerator(
				    cpu=args.cpu ,		mixed_precision=args.mixed_precision ,		gradient_accumulation_steps=__a		)
				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
				a__				      =		config["""lr"""]
				a__				      =		int(config["""num_epochs"""]		)
				a__				      =		int(config["""seed"""]		)
				a__				      =		int(config["""batch_size"""]		)

				a__				      =		evaluate.load("""glue""" ,		"""mrpc"""		)

				set_seed(__a		)
				a__      ,			a__				      =		get_dataloaders(__a ,		__a		)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				a__				      =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,		return_dict=__a		)

				# 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__				      =		model.to(accelerator.device		)

				# Instantiate optimizer
				a__				      =		AdamW(params=model.parameters() ,		lr=__a		)

				# Instantiate scheduler
				a__				      =		get_linear_schedule_with_warmup(
				    optimizer=__a ,		num_warmup_steps=100 ,		num_training_steps=(len(__a		) * 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.
				a__      ,			a__      ,			a__      ,			a__      ,			a__				      =		accelerator.prepare(
				    __a ,		__a ,		__a ,		__a ,		__a		)

				# Now we train the model
				for epoch in range(__a		):
								model.train()
								with LocalSGD(
								    accelerator=__a ,		model=__a ,		local_sgd_steps=__a ,		enabled=local_sgd_steps is not None		) as local_sgd:
												for step, batch in enumerate(__a		):
																# 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(__a		):
																				a__				      =		model(**__a		)
																				a__				      =		output.loss
																				accelerator.backward(__a		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								model.eval()
								for step, batch in enumerate(__a		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												with torch.no_grad():
																a__				      =		model(**__a		)
												a__				      =		outputs.logits.argmax(dim=-1		)
												a__      ,			a__				      =		accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
												metric.add_batch(
												    predictions=__a ,		references=__a ,		)

								a__				      =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:''' ,		__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser(description="""Simple example of training script."""		)
				parser.add_argument(
				    """--mixed_precision""" ,		type=__a ,		default=__a ,		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=__a ,		default=1 ,		help="""The number of minibatches to be ran before gradients are accumulated.""" ,		)
				parser.add_argument(
				    """--local_sgd_steps""" ,		type=__a ,		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."""		)
				a__				      =		parser.parse_args()
				a__				      =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(__a ,		__a		)


if __name__ == "__main__":
	main()

