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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=True       )->       int:



			"""simple docstring"""







			model.train()
			lowercase							=		model(UpperCAmelCase       )
			lowercase							=		F.mse_loss(UpperCAmelCase,		target.to(output.device       )       )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase=False       )->       str:



			"""simple docstring"""







			set_seed(42       )
			lowercase							=		RegressionModel()
			lowercase							=		deepcopy(UpperCAmelCase       )
			lowercase							=		RegressionDataset(length=80       )
			lowercase							=		DataLoader(UpperCAmelCase,		batch_size=16       )
			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(UpperCAmelCase,		lr_lambda=lambda UpperCAmelCase       : epoch**0.65       )
						lowercase							=		LambdaLR(UpperCAmelCase,		lr_lambda=lambda UpperCAmelCase       : epoch**0.65       )
			# Make a copy of `model`
			if sched:
						lowercase		,lowercase		,lowercase		,lowercase							=		accelerator.prepare(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
			else:
						lowercase		,lowercase							=		accelerator.prepare(UpperCAmelCase,		UpperCAmelCase       )
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader






def        __UpperCAmelCase    (       UpperCAmelCase       )->       int:



			"""simple docstring"""







			lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase       )
			# Use a single batch
			lowercase		,lowercase							=		next(iter(UpperCAmelCase       )       ).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(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(UpperCAmelCase       ):
												step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						else:
									# Sync grads
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						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(1337 + iteration       )
						lowercase							=		ddp_input[torch.randperm(len(UpperCAmelCase       )       )]






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase       )
			# Use a single batch
			lowercase		,lowercase							=		next(iter(UpperCAmelCase       )       ).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(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(UpperCAmelCase       ):
												step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						else:
									# Sync grads
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )

						# 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(1337 + iteration       )
						lowercase							=		ddp_input[torch.randperm(len(UpperCAmelCase       )       )]






def        __UpperCAmelCase    (       UpperCAmelCase=False,		UpperCAmelCase=False       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		Accelerator(
			    split_batches=UpperCAmelCase,		dispatch_batches=UpperCAmelCase,		gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase       )
			for iteration, batch in enumerate(UpperCAmelCase       ):
						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(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(UpperCAmelCase       ):
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )

						# 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(UpperCAmelCase       ) - 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(1337 + iteration       )
						lowercase							=		ddp_input[torch.randperm(len(UpperCAmelCase       )       )]
			GradientState._reset_state()






def        __UpperCAmelCase    (       UpperCAmelCase=False,		UpperCAmelCase=False       )->       int:



			"""simple docstring"""







			lowercase							=		Accelerator(
			    split_batches=UpperCAmelCase,		dispatch_batches=UpperCAmelCase,		gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		get_training_setup(UpperCAmelCase,		UpperCAmelCase       )
			for iteration, batch in enumerate(UpperCAmelCase       ):
						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(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase       )):
									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(UpperCAmelCase       ):
									step_model(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									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(UpperCAmelCase       ))
						if accelerator.num_processes > 1:
									check_model_parameters(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration       )
			GradientState._reset_state()






def        __UpperCAmelCase    (       )->       Any:



			"""simple docstring"""







			lowercase							=		Accelerator()

			lowercase							=		RegressionDataset(length=80       )
			lowercase							=		DataLoader(UpperCAmelCase,		batch_size=16       )
			lowercase							=		RegressionDataset(length=96       )
			lowercase							=		DataLoader(UpperCAmelCase,		batch_size=16       )
			lowercase		,lowercase							=		accelerator.prepare(UpperCAmelCase,		UpperCAmelCase       )
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(UpperCAmelCase       ):
						assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCAmelCase       )
						if iteration < len(UpperCAmelCase       ) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(UpperCAmelCase       ):
															assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCAmelCase       )
															if batch_num < len(UpperCAmelCase       ) - 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        __UpperCAmelCase    (       )->       Any:



			"""simple docstring"""







			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(UpperCAmelCase       )
			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(UpperCAmelCase       )
			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(UpperCAmelCase,		UpperCAmelCase       )

    # 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(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			main()


if __name__ == "__main__":
						main()






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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=__lowerCamelCase							,							schedule=jnp.array(__lowerCamelCase							,							dtype=jnp.floataa   )							,							timesteps=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

									if self.config.s_min <= sigma <= self.config.s_max:
												lowercase							=		min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1   )
									else:
												lowercase							=		0

									# sample eps ~ N(0, S_noise^2 * I)
									lowercase							=		random.split(__lowerCamelCase							,							num=1   )
									lowercase							=		self.config.s_noise * random.normal(key=__lowerCamelCase							,							shape=sample.shape   )
									lowercase							=		sigma + gamma * sigma
									lowercase							=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )



						def        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





