



"""simple docstring"""

import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import DecisionTransformerModel
	from transformers.models.decision_transformer.modeling_decision_transformer import (
	    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)




class SCREAMING_SNAKE_CASE__       :



						def __init__(self    ,  _lowercase    ,  _lowercase=13    ,  _lowercase=7    ,  _lowercase=6    ,  _lowercase=17    ,  _lowercase=23    ,  _lowercase=11    ,  _lowercase=True    ,  ):





													'''simple docstring'''

													__a		:		Dict						=							parent
													__a		:		Tuple						=							batch_size
													__a		:		Any						=							seq_length
													__a		:		Optional[int]						=							act_dim
													__a		:		Optional[Any]						=							state_dim
													__a		:		Optional[int]						=							hidden_size
													__a		:		Optional[Any]						=							max_length
													__a		:		List[Any]						=							is_training



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		str						=							floats_tensor((self.batch_size, self.seq_length, self.state_dim)				)
													__a		:		Any						=							floats_tensor((self.batch_size, self.seq_length, self.act_dim)				)
													__a		:		Optional[Any]						=							floats_tensor((self.batch_size, self.seq_length, 1)				)
													__a		:		Dict						=							floats_tensor((self.batch_size, self.seq_length, 1)				)
													__a		:		List[str]						=							ids_tensor((self.batch_size, self.seq_length)    ,  vocab_size=1000				)
													__a		:		Union[str, Any]						=							random_attention_mask((self.batch_size, self.seq_length)				)

													__a		:		Union[str, Any]						=							self.get_config()

													return (
													    config,
													    states,
													    actions,
													    rewards,
													    returns_to_go,
													    timesteps,
													    attention_mask,
													)



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													return DecisionTransformerConfig(
													    batch_size=self.batch_size    ,  seq_length=self.seq_length    ,  act_dim=self.act_dim    ,  state_dim=self.state_dim    ,  hidden_size=self.hidden_size    ,  max_length=self.max_length    ,  )



						def      lowerCAmelCase__(self    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  ):





													'''simple docstring'''

													__a		:		str						=							DecisionTransformerModel(config=__A				)
													model.to(__A				)
													model.eval()
													__a		:		Any						=							model(__A    ,  __A    ,  __A    ,  __A    ,  __A    ,  __A				)

													self.parent.assertEqual(result.state_preds.shape    ,  states.shape				)
													self.parent.assertEqual(result.action_preds.shape    ,  actions.shape				)
													self.parent.assertEqual(result.return_preds.shape    ,  returns_to_go.shape				)
													self.parent.assertEqual(
													    result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length * 3, self.hidden_size)				)  # seq length *3 as there are 3 modelities: states, returns and actions





						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		Optional[int]						=							self.prepare_config_and_inputs()
													(
													    (
													    __a    
													)				,  (
													    __a    
													)				,  (
													    __a    
													)				,  (
													    __a    
													)				,  (
													    __a    
													)				,  (
													    __a    
													)				,  (
													    __a    
													)				,      
													)		:		str						=							config_and_inputs
													__a		:		Dict						=							{
													    """states""": states,
													    """actions""": actions,
													    """rewards""": rewards,
													    """returns_to_go""": returns_to_go,
													    """timesteps""": timesteps,
													    """attention_mask""": attention_mask,
													}
													return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE__       (		UpperCamelCase__  ,       UpperCamelCase__  ,       UpperCamelCase__  ,       unittest.TestCase   ):
						_lowerCAmelCase    						=       (DecisionTransformerModel,) if is_torch_available() else ()
						_lowerCAmelCase    						=       ()
						_lowerCAmelCase    						=       {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

						# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
						_lowerCAmelCase    						=       False

						# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False
						_lowerCAmelCase    						=       False



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		List[Any]						=							DecisionTransformerModelTester(self				)
													__a		:		List[Any]						=							ConfigTester(self    ,  config_class=__A    ,  hidden_size=37				)



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													self.config_tester.run_common_tests()



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		Optional[Any]						=							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__A				)



						@slow
						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__a		:		Optional[int]						=							DecisionTransformerModel.from_pretrained(__A				)
																				self.assertIsNotNone(__A				)





						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a				,  __a		:		List[Any]						=							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__a		:		Union[str, Any]						=							model_class(__A				)
																				__a		:		Dict						=							inspect.signature(model.forward				)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				__a		:		int						=							[*signature.parameters.keys()]

																				__a		:		Union[str, Any]						=							[
																				    """states""",
																				    """actions""",
																				    """rewards""",
																				    """returns_to_go""",
																				    """timesteps""",
																				    """attention_mask""",
																				]

																				self.assertListEqual(arg_names[: len(__A				)]    ,  __A				)







@require_torch
class SCREAMING_SNAKE_CASE__       (		unittest.TestCase   ):



						@slow
						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		Optional[int]						=							2  # number of steps of autoregressive prediction we will perform
													__a		:		str						=							10  # defined by the RL environment, may be normalized
													__a		:		str						=							DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""				)
													__a		:		List[Any]						=							model.to(__A				)
													__a		:		str						=							model.config
													torch.manual_seed(0				)
													__a		:		List[Any]						=							torch.randn(1    ,  1    ,  config.state_dim				).to(device=__A    ,  dtype=torch.floataa				)  # env.reset()

													__a		:		Any						=							torch.tensor(
													    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]    ,  device=__A				)

													__a		:		Union[str, Any]						=							torch.tensor(__A    ,  device=__A    ,  dtype=torch.floataa				).reshape(1    ,  1    ,  1				)
													__a		:		Union[str, Any]						=							state
													__a		:		Any						=							torch.zeros(1    ,  0    ,  config.act_dim    ,  device=__A    ,  dtype=torch.floataa				)
													__a		:		List[str]						=							torch.zeros(1    ,  0    ,  device=__A    ,  dtype=torch.floataa				)
													__a		:		List[Any]						=							torch.tensor(0    ,  device=__A    ,  dtype=torch.long				).reshape(1    ,  1				)

													for step in range(__A				):
																				__a		:		List[str]						=							torch.cat([actions, torch.zeros(1    ,  1    ,  config.act_dim    ,  device=__A				)]    ,  dim=1				)
																				__a		:		List[Any]						=							torch.cat([rewards, torch.zeros(1    ,  1    ,  device=__A				)]    ,  dim=1				)

																				__a		:		Optional[Any]						=							torch.ones(1    ,  states.shape[1]				).to(dtype=torch.long    ,  device=states.device				)

																				with torch.no_grad():
																											__a				,  __a				,  __a		:		Any						=							model(
																											    states=__A    ,  actions=__A    ,  rewards=__A    ,  returns_to_go=__A    ,  timesteps=__A    ,  attention_mask=__A    ,  return_dict=__A    ,  )

																				self.assertEqual(action_pred.shape    ,  actions.shape				)
																				self.assertTrue(torch.allclose(action_pred[0, -1]    ,  expected_outputs[step]    ,  atol=1e-4				)				)
																				__a				,  __a				,  __a				,  __a		:		List[str]						=							(  # env.step(action)
																				    torch.randn(1    ,  1    ,  config.state_dim				).to(device=__A    ,  dtype=torch.floataa				),
																				    1.0,
																				    False,
																				    {},
																				)

																				__a		:		List[Any]						=							action_pred[0, -1]
																				__a		:		Any						=							torch.cat([states, state]    ,  dim=1				)
																				__a		:		Optional[Any]						=							returns_to_go[0, -1] - reward
																				__a		:		List[str]						=							torch.cat([returns_to_go, pred_return.reshape(1    ,  1    ,  1				)]    ,  dim=1				)
																				__a		:		Dict						=							torch.cat(
																				    [timesteps, torch.ones((1, 1)    ,  device=__A    ,  dtype=torch.long				) * (step + 1)]    ,  dim=1				)








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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Tuple=True     ):
		model.train()
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   F.mse_loss(UpperCamelCase__       , target.to(output.device     )     )
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				List[Any]=False     ):
		set_seed(42     )
		SCREAMING_SNAKE_CASE__								=   RegressionModel()
		SCREAMING_SNAKE_CASE__								=   deepcopy(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		model.to(accelerator.device     )
		if sched:
				SCREAMING_SNAKE_CASE__								=   AdamW(params=model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   AdamW(params=ddp_model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
		# Make a copy of `model`
		if sched:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		if sched:
				return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
		return model, ddp_model, dataloader






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		# Test when on a single CPU or GPU that the context manager does nothing
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		# Test on distributed setup that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int=False       , UpperCamelCase__:				Union[str, Any]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple=False       , UpperCamelCase__:				List[str]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__       , UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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'''
				SCREAMING_SNAKE_CASE__								=   (((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(1_337 + iteration     )
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()

		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=96     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()
		SCREAMING_SNAKE_CASE__								=   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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()