


'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				StableUnCLIPImgaImgPipeline
				_snake_case						       =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS
				_snake_case						       =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_snake_case						       =				frozenset(
				    [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				_snake_case						       =				frozenset([])

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Any   					=		3_2
					lowercase						:		Tuple   					=		embedder_hidden_size

					# image encoding components

					lowercase						:		List[Any]   					=		CLIPImageProcessor(crop_size=3_2							,      size=3_2							)

					torch.manual_seed(0							)
					lowercase						:		int   					=		CLIPVisionModelWithProjection(
					    CLIPVisionConfig(
					        hidden_size=a_							,      projection_dim=a_							,      num_hidden_layers=5							,      num_attention_heads=4							,      image_size=3_2							,      intermediate_size=3_7							,      patch_size=1							,      )							)

					# regular denoising components

					torch.manual_seed(0							)
					lowercase						:		Dict   					=		StableUnCLIPImageNormalizer(embedding_dim=a_							)
					lowercase						:		Dict   					=		DDPMScheduler(beta_schedule="squaredcos_cap_v2"							)

					torch.manual_seed(0							)
					lowercase						:		List[Any]   					=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

					torch.manual_seed(0							)
					lowercase						:		int   					=		CLIPTextModel(
					    CLIPTextConfig(
					        bos_token_id=0							,      eos_token_id=2							,      hidden_size=a_							,      projection_dim=3_2							,      intermediate_size=3_7							,      layer_norm_eps=1e-0_5							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )							)

					torch.manual_seed(0							)
					lowercase						:		Union[str, Any]   					=		UNetaDConditionModel(
					    sample_size=3_2							,      in_channels=4							,      out_channels=4							,      down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")							,      up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")							,      block_out_channels=(3_2, 6_4)							,      attention_head_dim=(2, 4)							,      class_embed_type="projection"							,      projection_class_embeddings_input_dim=embedder_projection_dim * 2							,      cross_attention_dim=a_							,      layers_per_block=1							,      upcast_attention=a_							,      use_linear_projection=a_							,      )

					torch.manual_seed(0							)
					lowercase						:		Tuple   					=		DDIMScheduler(
					    beta_schedule="scaled_linear"							,      beta_start=0.0_00_85							,      beta_end=0.0_12							,      prediction_type="v_prediction"							,      set_alpha_to_one=a_							,      steps_offset=1							,      )

					torch.manual_seed(0							)
					lowercase						:		Optional[int]   					=		AutoencoderKL()

					lowercase						:		Any   					=		{
					    # image encoding components
					    "feature_extractor": feature_extractor,
					    "image_encoder": image_encoder.eval(),
					    # image noising components
					    "image_normalizer": image_normalizer.eval(),
					    "image_noising_scheduler": image_noising_scheduler,
					    # regular denoising components
					    "tokenizer": tokenizer,
					    "text_encoder": text_encoder.eval(),
					    "unet": unet.eval(),
					    "scheduler": scheduler,
					    "vae": vae.eval(),
					}

					return components

				def 	a__		(     self							,      a_							,      a_=0							,      a_=True							)    ->  List[str]:
					if str(a_							).startswith("mps"							):
						lowercase						:		Tuple   					=		torch.manual_seed(a_							)
					else:
						lowercase						:		Optional[int]   					=		torch.Generator(device=a_							).manual_seed(a_							)

					lowercase						:		Optional[Any]   					=		floats_tensor((1, 3, 3_2, 3_2)							,      rng=random.Random(a_							)							).to(a_							)

					if pil_image:
						lowercase						:		int   					=		input_image * 0.5 + 0.5
						lowercase						:		Tuple   					=		input_image.clamp(0							,      1							)
						lowercase						:		Any   					=		input_image.cpu().permute(0							,      2							,      3							,      1							).float().numpy()
						lowercase						:		Tuple   					=		DiffusionPipeline.numpy_to_pil(a_							)[0]

					return {
					    "prompt": "An anime racoon running a marathon",
					    "image": input_image,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "output_type": "np",
					}

				@skip_mps
				def 	a__		(     self							)    ->  Any:
					lowercase						:		Union[str, Any]   					=		"cpu"  # ensure determinism for the device-dependent torch.Generator
					lowercase						:		Union[str, Any]   					=		self.get_dummy_components()
					lowercase						:		str   					=		StableUnCLIPImgaImgPipeline(**a_							)
					lowercase						:		Optional[Any]   					=		sd_pipe.to(a_							)
					sd_pipe.set_progress_bar_config(disable=a_							)

					lowercase						:		Any   					=		self.get_dummy_inputs(a_							)
					inputs.update({"image_embeds": None}							)
					lowercase						:		Any   					=		sd_pipe(**a_							).images
					lowercase						:		List[Any]   					=		image[0, -3:, -3:, -1]

					assert image.shape == (1, 3_2, 3_2, 3)
					lowercase						:		Optional[Any]   					=		np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78]							)

					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Optional[Any]   					=		torch_device in ["cpu", "mps"]

					self._test_attention_slicing_forward_pass(test_max_difference=a_							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		Dict   					=		torch_device in ["cpu", "mps"]

					self._test_inference_batch_single_identical(test_max_difference=a_							)


				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()							,      reason="XFormers attention is only available with CUDA and `xformers` installed"							,      )
				def 	a__		(     self							)    ->  int:
					self._test_xformers_attention_forwardGenerator_pass(test_max_difference=a_							)




@slow
@require_torch_gpu
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def 	a__		(     self							)    ->  List[str]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Union[str, Any]   					=		load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

					lowercase						:		str   					=		load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"							)

					lowercase						:		Optional[int]   					=		StableUnCLIPImgaImgPipeline.from_pretrained(
					    "fusing/stable-unclip-2-1-l-img2img"							,      torch_dtype=torch.floataa							)
					pipe.to(a_							)
					pipe.set_progress_bar_config(disable=a_							)
					# stable unclip will oom when integration tests are run on a V100,
					# so turn on memory savings
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					lowercase						:		int   					=		torch.Generator(device="cpu"							).manual_seed(0							)
					lowercase						:		Optional[int]   					=		pipe(a_							,      "anime turle"							,      generator=a_							,      output_type="np"							)

					lowercase						:		Optional[int]   					=		output.images[0]

					assert image.shape == (7_6_8, 7_6_8, 3)

					assert_mean_pixel_difference(a_							,      a_							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		List[Any]   					=		load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

					lowercase						:		Union[str, Any]   					=		load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"							)

					lowercase						:		Dict   					=		StableUnCLIPImgaImgPipeline.from_pretrained(
					    "fusing/stable-unclip-2-1-h-img2img"							,      torch_dtype=torch.floataa							)
					pipe.to(a_							)
					pipe.set_progress_bar_config(disable=a_							)
					# stable unclip will oom when integration tests are run on a V100,
					# so turn on memory savings
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					lowercase						:		Union[str, Any]   					=		torch.Generator(device="cpu"							).manual_seed(0							)
					lowercase						:		str   					=		pipe(a_							,      "anime turle"							,      generator=a_							,      output_type="np"							)

					lowercase						:		Optional[int]   					=		output.images[0]

					assert image.shape == (7_6_8, 7_6_8, 3)

					assert_mean_pixel_difference(a_							,      a_							)


				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Union[str, Any]   					=		load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"							)

					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					lowercase						:		Any   					=		StableUnCLIPImgaImgPipeline.from_pretrained(
					    "fusing/stable-unclip-2-1-h-img2img"							,      torch_dtype=torch.floataa							)
					lowercase						:		Tuple   					=		pipe.to(a_							)
					pipe.set_progress_bar_config(disable=a_							)
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					lowercase						:		Any   					=		pipe(
					    a_							,      "anime turtle"							,      num_inference_steps=2							,      output_type="np"							,      )

					lowercase						:		Union[str, Any]   					=		torch.cuda.max_memory_allocated()
					# make sure that less than 7 GB is allocated
					assert mem_bytes < 7 * 1_0**9






'''simple docstring'''
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
#
########################################################################


lowerCAmelCase   : Tuple             =				1_6
lowerCAmelCase   : Optional[int]             =				3_2




def 	_A  (       A							,A = 1_6							)  ->				Union[str, Any]:
	lowercase						:		List[Any]   					=		AutoTokenizer.from_pretrained("bert-base-cased"							)
	lowercase						:		Any   					=		load_dataset("glue"							,"mrpc"							)

	def tokenize_function(A							):
		# max_length=None => use the model max length (it's actually the default)
		lowercase						:		List[str]   					=		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():
		lowercase						:		List[str]   					=		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
	lowercase						:		Optional[int]   					=		tokenized_datasets.rename_column("label"							,"labels"							)

	def collate_fn(A							):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		lowercase						:		Tuple   					=		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			lowercase						:		Any   					=		1_6
		elif accelerator.mixed_precision != "no":
			lowercase						:		List[Any]   					=		8
		else:
			lowercase						:		List[Any]   					=		None

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

	# Instantiate dataloaders.
	lowercase						:		Any   					=		DataLoader(
	    tokenized_datasets["train"]							,shuffle=A							,collate_fn=A							,batch_size=A							)
	lowercase						:		List[str]   					=		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

					lowerCAmelCase   : List[Any]             =				mocked_dataloaders  # noqa: F811




def 	_A  (       A							,A							)  ->				str:
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"							,A							) == "1":
		lowercase						:		Optional[Any]   					=		2
	# New Code #
	lowercase						:		List[str]   					=		int(args.gradient_accumulation_steps							)
	lowercase						:		Union[str, Any]   					=		int(args.local_sgd_steps							)
	# Initialize accelerator
	lowercase						:		Union[str, Any]   					=		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
	lowercase						:		Dict   					=		config["lr"]
	lowercase						:		List[Any]   					=		int(config["num_epochs"]							)
	lowercase						:		int   					=		int(config["seed"]							)
	lowercase						:		Dict   					=		int(config["batch_size"]							)

	lowercase						:		List[str]   					=		evaluate.load("glue"							,"mrpc"							)

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

	# Instantiate optimizer
	lowercase						:		Tuple   					=		AdamW(params=model.parameters()							,lr=A							)

	# Instantiate scheduler
	lowercase						:		Union[str, Any]   					=		get_linear_schedule_with_warmup(
	    optimizer=A							,num_warmup_steps=1_0_0							,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.
	lowercase					,		lowercase					,		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		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							):
					lowercase						:		Any   					=		model(**A							)
					lowercase						:		Dict   					=		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():
				lowercase						:		Dict   					=		model(**A							)
			lowercase						:		Tuple   					=		outputs.logits.argmax(dim=-1							)
			lowercase					,		lowercase						:		str   					=		accelerator.gather_for_metrics((predictions, batch["labels"])							)
			metric.add_batch(
			    predictions=A							,references=A							,)

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




def 	_A  (       )  ->				Dict:
	lowercase						:		Optional[int]   					=		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."							)
	lowercase						:		Optional[Any]   					=		parser.parse_args()
	lowercase						:		Union[str, Any]   					=		{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
	training_function(A							,A							)


if __name__ == "__main__":
					main()


