



import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
	import os

	from flax.core.frozen_dict import unfreeze
	from flax.traverse_util import flatten_dict

	from transformers import FlaxBertModel

	snake_case__					:     Any    		=   """0.12"""  # assumed parallelism: 8

@require_flax
@is_staging_test
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						@classmethod
						def     _snake_case	(	cls	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			TOKEN
							HfFolder.save_token(lowerCamelCase	)



						@classmethod
						def     _snake_case	(	cls	:							Tuple	):






							'''simple docstring'''



							try:
								delete_repo(token=cls._token							,      repo_id="test-model-flax"	)
							except HTTPError:
								pass

							try:
								delete_repo(token=cls._token							,      repo_id="valid_org/test-model-flax-org"	)
							except HTTPError:
								pass



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






							'''simple docstring'''



							__lowercase		       =			BertConfig(
							    vocab_size=99							,      hidden_size=32							,      num_hidden_layers=5							,      num_attention_heads=4							,      intermediate_size=37	)
							__lowercase		       =			FlaxBertModel(lowerCamelCase	)
							model.push_to_hub("test-model-flax"							,      use_auth_token=self._token	)

							__lowercase		       =			FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax"""	)

							__lowercase		       =			flatten_dict(unfreeze(model.params	)	)
							__lowercase		       =			flatten_dict(unfreeze(new_model.params	)	)

							for key in base_params.keys():
								__lowercase		       =			(base_params[key] - new_params[key]).sum().item()
								self.assertLessEqual(lowerCamelCase							,      1e-3							,      msg=f"""{key} not identical"""	)

							# Reset repo
							delete_repo(token=self._token							,      repo_id="test-model-flax"	)

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
								model.save_pretrained(lowerCamelCase							,      repo_id="test-model-flax"							,      push_to_hub=lowerCamelCase							,      use_auth_token=self._token	)

							__lowercase		       =			FlaxBertModel.from_pretrained(f"""{USER}/test-model-flax"""	)

							__lowercase		       =			flatten_dict(unfreeze(model.params	)	)
							__lowercase		       =			flatten_dict(unfreeze(new_model.params	)	)

							for key in base_params.keys():
								__lowercase		       =			(base_params[key] - new_params[key]).sum().item()
								self.assertLessEqual(lowerCamelCase							,      1e-3							,      msg=f"""{key} not identical"""	)



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			BertConfig(
							    vocab_size=99							,      hidden_size=32							,      num_hidden_layers=5							,      num_attention_heads=4							,      intermediate_size=37	)
							__lowercase		       =			FlaxBertModel(lowerCamelCase	)
							model.push_to_hub("valid_org/test-model-flax-org"							,      use_auth_token=self._token	)

							__lowercase		       =			FlaxBertModel.from_pretrained("valid_org/test-model-flax-org"	)

							__lowercase		       =			flatten_dict(unfreeze(model.params	)	)
							__lowercase		       =			flatten_dict(unfreeze(new_model.params	)	)

							for key in base_params.keys():
								__lowercase		       =			(base_params[key] - new_params[key]).sum().item()
								self.assertLessEqual(lowerCamelCase							,      1e-3							,      msg=f"""{key} not identical"""	)

							# Reset repo
							delete_repo(token=self._token							,      repo_id="valid_org/test-model-flax-org"	)

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
								model.save_pretrained(
								    lowerCamelCase							,      repo_id="valid_org/test-model-flax-org"							,      push_to_hub=lowerCamelCase							,      use_auth_token=self._token	)

							__lowercase		       =			FlaxBertModel.from_pretrained("valid_org/test-model-flax-org"	)

							__lowercase		       =			flatten_dict(unfreeze(model.params	)	)
							__lowercase		       =			flatten_dict(unfreeze(new_model.params	)	)

							for key in base_params.keys():
								__lowercase		       =			(base_params[key] - new_params[key]).sum().item()
								self.assertLessEqual(lowerCamelCase							,      1e-3							,      msg=f"""{key} not identical"""	)



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			True
	__lowercase		       =			flatten_dict(modela.params   )
	__lowercase		       =			flatten_dict(modela.params   )
	for key in flat_params_a.keys():
		if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]   )   ) > 1E-4:
			__lowercase		       =			False

	return models_are_equal

@require_flax
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"	)
							__lowercase		       =			FlaxBertModel(lowerCamelCase	)

							__lowercase		       =			"bert"
							with tempfile.TemporaryDirectory() as tmp_dir:
								model.save_pretrained(os.path.join(lowerCamelCase							,      lowerCamelCase	)	)

								with self.assertRaises(lowerCamelCase	):
									__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase	)

								__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase							,      subfolder=lowerCamelCase	)

							self.assertTrue(check_models_equal(lowerCamelCase							,      lowerCamelCase	)	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							__lowercase		       =			BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only"	)
							__lowercase		       =			FlaxBertModel(lowerCamelCase	)

							__lowercase		       =			"bert"
							with tempfile.TemporaryDirectory() as tmp_dir:
								model.save_pretrained(os.path.join(lowerCamelCase							,      lowerCamelCase	)							,      max_shard_size="10KB"	)

								with self.assertRaises(lowerCamelCase	):
									__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase	)

								__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase							,      subfolder=lowerCamelCase	)

							self.assertTrue(check_models_equal(lowerCamelCase							,      lowerCamelCase	)	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			"bert"
							__lowercase		       =			"hf-internal-testing/tiny-random-bert-subfolder"

							with self.assertRaises(lowerCamelCase	):
								__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase	)

							__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase							,      subfolder=lowerCamelCase	)

							self.assertIsNotNone(lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			"bert"
							__lowercase		       =			"hf-internal-testing/tiny-random-bert-sharded-subfolder"
							with self.assertRaises(lowerCamelCase	):
								__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase	)

							__lowercase		       =			FlaxBertModel.from_pretrained(lowerCamelCase							,      subfolder=lowerCamelCase	)

							self.assertIsNotNone(lowerCamelCase	)








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()



