

import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Any      							=      ConsistencyModelPipeline
						_snake_case			:     Union[str, Any]      							=      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
						_snake_case			:     Optional[Any]      							=      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

						# Override required_optional_params to remove num_images_per_prompt
						_snake_case			:     str      							=      frozenset(
						    [
						        """num_inference_steps""",
						        """generator""",
						        """latents""",
						        """output_type""",
						        """return_dict""",
						        """callback""",
						        """callback_steps""",
						    ]	)



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			UNetaDModel.from_pretrained(
							    "diffusers/consistency-models-test"							,      subfolder="test_unet"							,      )
							return unet



						@property
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			UNetaDModel.from_pretrained(
							    "diffusers/consistency-models-test"							,      subfolder="test_unet_class_cond"							,      )
							return unet



						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							List[str]=False	):






							'''simple docstring'''



							if class_cond:
								__lowercase		       =			self.dummy_cond_unet
							else:
								__lowercase		       =			self.dummy_uncond_unet

							# Default to CM multistep sampler
							__lowercase		       =			CMStochasticIterativeScheduler(
							    num_train_timesteps=40							,      sigma_min=0.002							,      sigma_max=80.0							,      )

							__lowercase		       =			{
							    "unet": unet,
							    "scheduler": scheduler,
							}

							return components



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							Optional[int]=0	):






							'''simple docstring'''



							if str(lowerCamelCase	).startswith("mps"	):
								__lowercase		       =			torch.manual_seed(lowerCamelCase	)
							else:
								__lowercase		       =			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)

							__lowercase		       =			{
							    "batch_size": 1,
							    "num_inference_steps": None,
							    "timesteps": [22, 0],
							    "generator": generator,
							    "output_type": "np",
							}

							return inputs



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			ConsistencyModelPipeline(**lowerCamelCase	)
							__lowercase		       =			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 32, 32, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]
							__lowercase		       =			np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]	)

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



						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components(class_cond=lowerCamelCase	)
							__lowercase		       =			ConsistencyModelPipeline(**lowerCamelCase	)
							__lowercase		       =			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			0
							__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 32, 32, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]
							__lowercase		       =			np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]	)

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



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components()
							__lowercase		       =			ConsistencyModelPipeline(**lowerCamelCase	)
							__lowercase		       =			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			1
							__lowercase		       =			None
							__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 32, 32, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]
							__lowercase		       =			np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]	)

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



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							__lowercase		       =			self.get_dummy_components(class_cond=lowerCamelCase	)
							__lowercase		       =			ConsistencyModelPipeline(**lowerCamelCase	)
							__lowercase		       =			pipe.to(lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_dummy_inputs(lowerCamelCase	)
							__lowercase		       =			1
							__lowercase		       =			None
							__lowercase		       =			0
							__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 32, 32, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]
							__lowercase		       =			np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]	)

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

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


						'''simple docstring'''





						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Tuple=0							,      lowerCamelCase	:							List[str]=False							,      lowerCamelCase	:							Tuple="cpu"							,      lowerCamelCase	:							Optional[Any]=torch.floataa							,      lowerCamelCase	:							Optional[int]=(1, 3, 64, 64)	):






							'''simple docstring'''



							__lowercase		       =			torch.manual_seed(lowerCamelCase	)

							__lowercase		       =			{
							    "num_inference_steps": None,
							    "timesteps": [22, 0],
							    "class_labels": 0,
							    "generator": generator,
							    "output_type": "np",
							}

							if get_fixed_latents:
								__lowercase		       =			self.get_fixed_latents(seed=lowerCamelCase							,      device=lowerCamelCase							,      dtype=lowerCamelCase							,      shape=lowerCamelCase	)
								__lowercase		       =			latents

							return inputs



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							Tuple=0							,      lowerCamelCase	:							Dict="cpu"							,      lowerCamelCase	:							Optional[int]=torch.floataa							,      lowerCamelCase	:							Dict=(1, 3, 64, 64)	):






							'''simple docstring'''



							if type(lowerCamelCase	) == str:
								__lowercase		       =			torch.device(lowerCamelCase	)
							__lowercase		       =			torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)
							__lowercase		       =			randn_tensor(lowerCamelCase							,      generator=lowerCamelCase							,      device=lowerCamelCase							,      dtype=lowerCamelCase	)
							return latents



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			UNetaDModel.from_pretrained("diffusers/consistency_models"							,      subfolder="diffusers_cd_imagenet64_l2"	)
							__lowercase		       =			CMStochasticIterativeScheduler(
							    num_train_timesteps=40							,      sigma_min=0.002							,      sigma_max=80.0							,      )
							__lowercase		       =			ConsistencyModelPipeline(unet=lowerCamelCase							,      scheduler=lowerCamelCase	)
							pipe.to(torch_device=lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_inputs()
							__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 64, 64, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]

							__lowercase		       =			np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 2e-2



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase		       =			UNetaDModel.from_pretrained("diffusers/consistency_models"							,      subfolder="diffusers_cd_imagenet64_l2"	)
							__lowercase		       =			CMStochasticIterativeScheduler(
							    num_train_timesteps=40							,      sigma_min=0.002							,      sigma_max=80.0							,      )
							__lowercase		       =			ConsistencyModelPipeline(unet=lowerCamelCase							,      scheduler=lowerCamelCase	)
							pipe.to(torch_device=lowerCamelCase	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_inputs()
							__lowercase		       =			1
							__lowercase		       =			None
							__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 64, 64, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]

							__lowercase		       =			np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 2e-2



						@require_torch_a
						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			UNetaDModel.from_pretrained("diffusers/consistency_models"							,      subfolder="diffusers_cd_imagenet64_l2"	)
							__lowercase		       =			CMStochasticIterativeScheduler(
							    num_train_timesteps=40							,      sigma_min=0.002							,      sigma_max=80.0							,      )
							__lowercase		       =			ConsistencyModelPipeline(unet=lowerCamelCase							,      scheduler=lowerCamelCase	)
							pipe.to(torch_device=lowerCamelCase							,      torch_dtype=torch.floataa	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_inputs(get_fixed_latents=lowerCamelCase							,      device=lowerCamelCase	)
							# Ensure usage of flash attention in torch 2.0
							with sdp_kernel(enable_flash=lowerCamelCase							,      enable_math=lowerCamelCase							,      enable_mem_efficient=lowerCamelCase	):
								__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 64, 64, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]

							__lowercase		       =			np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353]	)

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



						@require_torch_a
						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			UNetaDModel.from_pretrained("diffusers/consistency_models"							,      subfolder="diffusers_cd_imagenet64_l2"	)
							__lowercase		       =			CMStochasticIterativeScheduler(
							    num_train_timesteps=40							,      sigma_min=0.002							,      sigma_max=80.0							,      )
							__lowercase		       =			ConsistencyModelPipeline(unet=lowerCamelCase							,      scheduler=lowerCamelCase	)
							pipe.to(torch_device=lowerCamelCase							,      torch_dtype=torch.floataa	)
							pipe.set_progress_bar_config(disable=lowerCamelCase	)

							__lowercase		       =			self.get_inputs(get_fixed_latents=lowerCamelCase							,      device=lowerCamelCase	)
							__lowercase		       =			1
							__lowercase		       =			None
							# Ensure usage of flash attention in torch 2.0
							with sdp_kernel(enable_flash=lowerCamelCase							,      enable_math=lowerCamelCase							,      enable_mem_efficient=lowerCamelCase	):
								__lowercase		       =			pipe(**lowerCamelCase	).images
							assert image.shape == (1, 64, 64, 3)

							__lowercase		       =			image[0, -3:, -3:, -1]

							__lowercase		       =			np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095]	)

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








from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	for param, grad_param in zip(model_a.parameters()  ,	model_b.parameters()   ):
		if not param.requires_grad:
			continue
		if not did_step:
			# Grads should not be in sync
			assert (
			    torch.allclose(param.grad  ,	grad_param.grad   ) is False
			), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
		else:
			# Grads should be in sync
			assert (
			    torch.allclose(param.grad  ,	grad_param.grad   ) is True
			), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=True   ):
	model.train()
	__lowercase		       =			model(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			F.mse_loss(_SCREAMING_SNAKE_CASE  ,	target.to(output.device   )   )
	if not do_backward:
		loss /= accelerator.gradient_accumulation_steps
		loss.backward()
	else:
		accelerator.backward(_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	set_seed(4_2   )
	__lowercase		       =			RegressionModel()
	__lowercase		       =			deepcopy(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			RegressionDataset(length=8_0   )
	__lowercase		       =			DataLoader(_SCREAMING_SNAKE_CASE  ,	batch_size=1_6   )
	model.to(accelerator.device   )
	if sched:
		__lowercase		       =			AdamW(params=model.parameters()  ,	lr=1E-3   )
		__lowercase		       =			AdamW(params=ddp_model.parameters()  ,	lr=1E-3   )
		__lowercase		       =			LambdaLR(_SCREAMING_SNAKE_CASE  ,	lr_lambda=lambda _SCREAMING_SNAKE_CASE   : epoch**0.6_5   )
		__lowercase		       =			LambdaLR(_SCREAMING_SNAKE_CASE  ,	lr_lambda=lambda _SCREAMING_SNAKE_CASE   : epoch**0.6_5   )
	# Make a copy of `model`
	if sched:
		__lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase		       =			accelerator.prepare(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	else:
		__lowercase       ,    __lowercase		       =			accelerator.prepare(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	if sched:
		return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
	return model, ddp_model, dataloader



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	# Test when on a single CPU or GPU that the context manager does nothing
	__lowercase       ,    __lowercase       ,    __lowercase		       =			get_training_setup(_SCREAMING_SNAKE_CASE   )
	# Use a single batch
	__lowercase       ,    __lowercase		       =			next(iter(_SCREAMING_SNAKE_CASE   )   ).values()
	for iteration in range(3   ):
		# Gather the distributed inputs and targs for the base model
		__lowercase       ,    __lowercase		       =			accelerator.gather((ddp_input, ddp_target)   )
		__lowercase       ,    __lowercase		       =			input.to(accelerator.device   ), target.to(accelerator.device   )
		# Perform our initial ground truth step in non "DDP"
		step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		# Do "gradient accumulation" (noop)
		if iteration % 2 == 0:
			# Accumulate grads locally
			with accelerator.no_sync(_SCREAMING_SNAKE_CASE   ):
				step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		else:
			# Sync grads
			step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

		# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
		check_model_parameters(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		for param, ddp_param in zip(model.parameters()  ,	ddp_model.parameters()   ):
			if not param.requires_grad:
				continue
			assert torch.allclose(
			    param.grad  ,	ddp_param.grad   ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

		# Shuffle ddp_input on each iteration
		torch.manual_seed(1_3_3_7 + iteration   )
		__lowercase		       =			ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE   )   )]



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	# Test on distributed setup that context manager behaves properly
	__lowercase       ,    __lowercase       ,    __lowercase		       =			get_training_setup(_SCREAMING_SNAKE_CASE   )
	# Use a single batch
	__lowercase       ,    __lowercase		       =			next(iter(_SCREAMING_SNAKE_CASE   )   ).values()
	for iteration in range(3   ):
		# Gather the distributed inputs and targs for the base model
		__lowercase       ,    __lowercase		       =			accelerator.gather((ddp_input, ddp_target)   )
		__lowercase       ,    __lowercase		       =			input.to(accelerator.device   ), target.to(accelerator.device   )
		# Perform our initial ground truth step in non "DDP"
		step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		# Do "gradient accumulation" (noop)
		if iteration % 2 == 0:
			# Accumulate grads locally
			with accelerator.no_sync(_SCREAMING_SNAKE_CASE   ):
				step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		else:
			# Sync grads
			step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

		# DDP model and model should only be in sync when not (iteration % 2 == 0)
		for param, ddp_param in zip(model.parameters()  ,	ddp_model.parameters()   ):
			if not param.requires_grad:
				continue
			if iteration % 2 == 0:
				# Grads should not be in sync
				assert (
				    torch.allclose(param.grad  ,	ddp_param.grad   ) is False
				), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
			else:
				# Grads should be in sync
				assert (
				    torch.allclose(param.grad  ,	ddp_param.grad   ) is True
				), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
		torch.manual_seed(1_3_3_7 + iteration   )
		__lowercase		       =			ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE   )   )]



def    snake_case_				(    _SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			Accelerator(
	    split_batches=_SCREAMING_SNAKE_CASE  ,	dispatch_batches=_SCREAMING_SNAKE_CASE  ,	gradient_accumulation_steps=2   )
	# Test that context manager behaves properly
	__lowercase       ,    __lowercase       ,    __lowercase		       =			get_training_setup(_SCREAMING_SNAKE_CASE   )
	for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
		__lowercase       ,    __lowercase		       =			batch.values()
		# Gather the distributed inputs and targs for the base model
		__lowercase       ,    __lowercase		       =			accelerator.gather((ddp_input, ddp_target)   )
		__lowercase       ,    __lowercase		       =			input.to(accelerator.device   ), target.to(accelerator.device   )
		# Perform our initial ground truth step in non "DDP"
		step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		# Do "gradient accumulation" (noop)
		with accelerator.accumulate(_SCREAMING_SNAKE_CASE   ):
			step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

		# DDP model and model should only be in sync when not (iteration % 2 == 0)
		for param, ddp_param in zip(model.parameters()  ,	ddp_model.parameters()   ):
			if not param.requires_grad:
				continue
			if ((iteration + 1) % 2 == 0) or (iteration == len(_SCREAMING_SNAKE_CASE   ) - 1):
				# Grads should be in sync
				assert (
				    torch.allclose(param.grad  ,	ddp_param.grad   ) is True
				), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
			else:
				# Grads should not be in sync
				assert (
				    torch.allclose(param.grad  ,	ddp_param.grad   ) is False
				), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
		torch.manual_seed(1_3_3_7 + iteration   )
		__lowercase		       =			ddp_input[torch.randperm(len(_SCREAMING_SNAKE_CASE   )   )]
	GradientState._reset_state()



def    snake_case_				(    _SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			Accelerator(
	    split_batches=_SCREAMING_SNAKE_CASE  ,	dispatch_batches=_SCREAMING_SNAKE_CASE  ,	gradient_accumulation_steps=2   )
	# Test that context manager behaves properly
	__lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase		       =			get_training_setup(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	for iteration, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
		__lowercase       ,    __lowercase		       =			batch.values()
		# Gather the distributed inputs and targs for the base model
		__lowercase       ,    __lowercase		       =			accelerator.gather((ddp_input, ddp_target)   )
		__lowercase       ,    __lowercase		       =			input.to(accelerator.device   ), target.to(accelerator.device   )
		# Perform our initial ground truth step in non "DDP"
		model.train()
		ddp_model.train()
		step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		opt.step()

		if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE   )):
			if split_batches:
				sched.step()
			else:
				for _ in range(accelerator.num_processes   ):
					sched.step()
		opt.zero_grad()
		# Perform gradient accumulation under wrapper
		with accelerator.accumulate(_SCREAMING_SNAKE_CASE   ):
			step_model(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
			ddp_opt.step()
			ddp_sched.step()
			ddp_opt.zero_grad()

		# Learning rates should be the same
		assert (
		    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
		), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n"""
		__lowercase		       =			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_SCREAMING_SNAKE_CASE   ))
		if accelerator.num_processes > 1:
			check_model_parameters(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
		# Shuffle ddp_input on each iteration
		torch.manual_seed(1_3_3_7 + iteration   )
	GradientState._reset_state()



def    snake_case_				(    ):
	__lowercase		       =			Accelerator()

	__lowercase		       =			RegressionDataset(length=8_0   )
	__lowercase		       =			DataLoader(_SCREAMING_SNAKE_CASE  ,	batch_size=1_6   )
	__lowercase		       =			RegressionDataset(length=9_6   )
	__lowercase		       =			DataLoader(_SCREAMING_SNAKE_CASE  ,	batch_size=1_6   )
	__lowercase       ,    __lowercase		       =			accelerator.prepare(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	assert accelerator.gradient_state.active_dataloader is None
	for iteration, _ in enumerate(_SCREAMING_SNAKE_CASE   ):
		assert id(accelerator.gradient_state.active_dataloader   ) == id(_SCREAMING_SNAKE_CASE   )
		if iteration < len(_SCREAMING_SNAKE_CASE   ) - 1:
			assert not accelerator.gradient_state.end_of_dataloader
			if iteration == 1:
				for batch_num, _ in enumerate(_SCREAMING_SNAKE_CASE   ):
					assert id(accelerator.gradient_state.active_dataloader   ) == id(_SCREAMING_SNAKE_CASE   )
					if batch_num < len(_SCREAMING_SNAKE_CASE   ) - 1:
						assert not accelerator.gradient_state.end_of_dataloader
					else:
						assert accelerator.gradient_state.end_of_dataloader
		else:
			assert accelerator.gradient_state.end_of_dataloader
	assert accelerator.gradient_state.active_dataloader is None



def    snake_case_				(    ):
	__lowercase		       =			Accelerator()
	__lowercase		       =			accelerator.state
	if state.local_process_index == 0:
		print("**Test `accumulate` gradient accumulation with dataloader break**"   )
	test_dataloader_break()
	if state.distributed_type == DistributedType.NO:
		if state.local_process_index == 0:
			print("**Test NOOP `no_sync` context manager**"   )
		test_noop_sync(_SCREAMING_SNAKE_CASE   )
	if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
		if state.local_process_index == 0:
			print("**Test Distributed `no_sync` context manager**"   )
		test_distributed_sync(_SCREAMING_SNAKE_CASE   )
	if state.distributed_type == DistributedType.MULTI_GPU:
		for split_batch in [True, False]:
			for dispatch_batches in [True, False]:
				if state.local_process_index == 0:
					print(
					    "**Test `accumulate` gradient accumulation, "  ,	F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""  ,	)
				test_gradient_accumulation(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

    # Currently will break on torch 2.0 +, need to investigate why
	if is_torch_version("<"  ,	"2.0"   ) or state.distributed_type == DistributedType.NO:
		if state.local_process_index == 0:
			print(
			    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "  ,	"`split_batches=False`, `dispatch_batches=False`**"  ,	)
		test_gradient_accumulation_with_opt_and_scheduler()
		if state.distributed_type == DistributedType.MULTI_GPU:
			for split_batch in [True, False]:
				for dispatch_batches in [True, False]:
					if not split_batch and not dispatch_batches:
						continue
					if state.local_process_index == 0:
						print(
						    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "  ,	F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""  ,	)
					test_gradient_accumulation_with_opt_and_scheduler(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
	main()



