






import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
								# expert layer
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   flax_key_tuple[:-1] + ('weight',)
								SCREAMING_SNAKE_CASE_   :	str													=   torch.permute(lowerCAmelCase__    ,    (0, 2, 1)      )
				elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__      ):
								# linear layer
								SCREAMING_SNAKE_CASE_   :	Dict													=   flax_key_tuple[:-1] + ('weight',)
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   flax_tensor.T
				elif flax_key_tuple[-1] in ["scale", "embedding"]:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   flax_key_tuple[:-1] + ('weight',)

				return flax_key_tuple, flax_tensor



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		Tuple      ):





				'''simple docstring'''







				if "metadata" in layer:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   layer.split('metadata'      )
								SCREAMING_SNAKE_CASE_   :	Any													=   ''.join(split_layer[0]      )[:-1]
								SCREAMING_SNAKE_CASE_   :	Any													=   [tuple(('metadata' + split_layer[1]).split('/'      )      )]
				elif "kvstore" in layer:
								SCREAMING_SNAKE_CASE_   :	int													=   layer.split('kvstore'      )
								SCREAMING_SNAKE_CASE_   :	Dict													=   ''.join(split_layer[0]      )[:-1]
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [tuple(('kvstore' + split_layer[1]).split('/'      )      )]

				else:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   layer.split('/'      )
								SCREAMING_SNAKE_CASE_   :	int													=   '/'.join(split_layer[:-1]      )
								SCREAMING_SNAKE_CASE_   :	List[str]													=   (split_layer[-1],)

				if "kvstore/path" in layer:
								SCREAMING_SNAKE_CASE_   :	Dict													=   F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
				elif "kvstore/driver" in layer:
								SCREAMING_SNAKE_CASE_   :	str													=   'file'
				else:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   checkpoint_info[layer]

				return curr_real_layer_name, split_layer, content



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Dict													=   rename_keys(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Any													=   {}
				for k, v in current_block.items():
								SCREAMING_SNAKE_CASE_   :	Any													=   v
				SCREAMING_SNAKE_CASE_   :	Dict													=   new_current_block
				torch.save(lowerCAmelCase__    ,    lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		str = WEIGHTS_NAME      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   convert_file_size_to_int(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   []
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   {}
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   0
				SCREAMING_SNAKE_CASE_   :	int													=   0

				os.makedirs(lowerCAmelCase__    ,    exist_ok=lowerCAmelCase__      )
				with gfile.GFile(switch_checkpoint_path + '/checkpoint'    ,    'rb'      ) as fp:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   serialization.msgpack_restore(fp.read()      )['optimizer']['target']
								SCREAMING_SNAKE_CASE_   :	Dict													=   flatten_dict(lowerCAmelCase__    ,    sep='/'      )

				SCREAMING_SNAKE_CASE_   :	int													=   {}
				for layer in checkpoint_info.keys():
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_key_and_tensorstore_dict(
								    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								if curr_real_layer_name in all_layers:
												SCREAMING_SNAKE_CASE_   :	Any													=   content
								else:
												SCREAMING_SNAKE_CASE_   :	Dict													=   {split_layer[-1]: content}

				for key in all_layers.keys():
								# open tensorstore file
								SCREAMING_SNAKE_CASE_   :	str													=   ts.open(unflatten_dict(all_layers[key]      )      ).result().read().result()
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.tensor(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	List[str]													=   raw_weights.numel() * dtype_byte_size(raw_weights.dtype      )

								# use the renaming pattern from the small conversion scripts
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   rename_base_flax_keys(tuple(key.split('/'      )      )    ,    lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   '/'.join(lowerCAmelCase__      )

								# If this weight is going to tip up over the maximal size, we split.
								if current_block_size + weight_size > max_shard_size:
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   os.path.join(
												    lowerCAmelCase__    ,    weights_name.replace('.bin'    ,    F'''-{len(lowerCAmelCase__      )+1:05d}-of-???.bin'''      )      )
												rename_and_save_block(lowerCAmelCase__    ,    lowerCAmelCase__      )
												sharded_state_dicts.append(current_block.keys()      )
												del current_block
												SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {}
												SCREAMING_SNAKE_CASE_   :	str													=   0

								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   raw_weights.to(getattr(lowerCAmelCase__    ,    lowerCAmelCase__      )      )
								current_block_size += weight_size
								total_size += weight_size

				# Add the last block
				SCREAMING_SNAKE_CASE_   :	List[str]													=   os.path.join(lowerCAmelCase__    ,    weights_name.replace('.bin'    ,    F'''-{len(lowerCAmelCase__      )+1:05d}-of-???.bin'''      )      )
				rename_and_save_block(lowerCAmelCase__    ,    lowerCAmelCase__      )
				sharded_state_dicts.append(current_block.keys()      )

				# If we only have one shard, we return it
				if len(lowerCAmelCase__      ) == 1:
								return {weights_name: sharded_state_dicts[0]}, None

				# Otherwise, let's build the index
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {}
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {}
				for idx, shard in enumerate(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	int													=   weights_name.replace(
								    '.bin'    ,    F'''-{idx+1:05d}-of-{len(lowerCAmelCase__      ):05d}.bin'''      )  # len(sharded_state_dicts):05d}
								SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(lowerCAmelCase__    ,    weights_name.replace('.bin'    ,    F'''-{idx+1:05d}-of-???.bin'''      )      )
								os.rename(lowerCAmelCase__    ,    os.path.join(lowerCAmelCase__    ,    lowerCAmelCase__      )      )
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   shard
								for key in shard:
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   shard_file

    # Add the metadata
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   {'total_size': total_size}
				SCREAMING_SNAKE_CASE_   :	int													=   {'metadata': metadata, 'weight_map': weight_map}

				with open(os.path.join(lowerCAmelCase__    ,    lowerCAmelCase__      )    ,    'w'    ,    encoding='utf-8'      ) as f:
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   json.dumps(lowerCAmelCase__    ,    indent=2    ,    sort_keys=lowerCAmelCase__      ) + '\n'
								f.write(lowerCAmelCase__      )

				return metadata, index


if __name__ == "__main__":
				A        =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--switch_t5x_checkpoint_path''',
				    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
				    type=str,
				    required=False,
				    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
				)
				parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
				parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
				    type=str,
				    required=False,
				    help='''Path to the output pytorch model.''',
				)
				A        =			parser.parse_args()
				shard_on_the_fly(
				    args.switch_tax_checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.max_shard_size,
				    args.dtype,
				)



def 					_lowerCamelCase(			):





				'''simple docstring'''







				from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   SwitchTransformersConfig.from_pretrained('google/switch-base-8'      )
				config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'      )
				SCREAMING_SNAKE_CASE_   :	int													=   SwitchTransformersForConditionalGeneration.from_pretrained(
				    '/home/arthur_huggingface_co/transformers/switch_converted'    ,    device_map='auto'      )

				SCREAMING_SNAKE_CASE_   :	Any													=   TaTokenizer.from_pretrained('t5-small'      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

				SCREAMING_SNAKE_CASE_   :	int													=   tokenizer(lowerCAmelCase__    ,    return_tensors='pt'      ).input_ids
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   model.generate(lowerCAmelCase__    ,    decoder_start_token_id=0      )
				print(tokenizer.decode(out[0]      )      )







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 					_lowerCamelCase(			lowerCAmelCase__				:		int    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		Optional[int]      ):





				'''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 					_lowerCamelCase(			lowerCAmelCase__				:		Dict    ,    lowerCAmelCase__				:		List[Any]    ,    lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		Optional[int]    ,    lowerCAmelCase__				:		Optional[Any]=True      ):





				'''simple docstring'''







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



def 					_lowerCamelCase(			lowerCAmelCase__				:		Union[str, Any]    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







				set_seed(42      )
				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   RegressionModel()
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   deepcopy(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   RegressionDataset(length=80      )
				SCREAMING_SNAKE_CASE_   :	str													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				model.to(accelerator.device      )
				if sched:
								SCREAMING_SNAKE_CASE_   :	Dict													=   AdamW(params=model.parameters()    ,    lr=1E-3      )
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AdamW(params=ddp_model.parameters()    ,    lr=1E-3      )
								SCREAMING_SNAKE_CASE_   :	List[str]													=   LambdaLR(lowerCAmelCase__    ,    lr_lambda=lambda lowerCAmelCase__      : epoch**0.65      )
								SCREAMING_SNAKE_CASE_   :	Tuple													=   LambdaLR(lowerCAmelCase__    ,    lr_lambda=lambda lowerCAmelCase__      : epoch**0.65      )
				# Make a copy of `model`
				if sched:
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[int]													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
				else:
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[str]													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__      )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   get_training_setup(lowerCAmelCase__      )
				# Use a single batch
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   next(iter(lowerCAmelCase__      )      ).values()
				for iteration in range(3      ):
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Any													=   input.to(accelerator.device      ), target.to(accelerator.device      )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowerCAmelCase__      ):
																step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								else:
												# Sync grads
												step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								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      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]



def 					_lowerCamelCase(			lowerCAmelCase__				:		Any      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	str													=   get_training_setup(lowerCAmelCase__      )
				# Use a single batch
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   next(iter(lowerCAmelCase__      )      ).values()
				for iteration in range(3      ):
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	int													=   input.to(accelerator.device      ), target.to(accelerator.device      )
								# Perform our initial ground truth step in non "DDP"
								step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowerCAmelCase__      ):
																step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								else:
												# Sync grads
												step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

								# 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      )
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[Any]=False    ,    lowerCAmelCase__				:		Union[str, Any]=False      ):





				'''simple docstring'''







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

								# 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(lowerCAmelCase__      ) - 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      )
								SCREAMING_SNAKE_CASE_   :	str													=   ddp_input[torch.randperm(len(lowerCAmelCase__      )      )]
				GradientState._reset_state()



def 					_lowerCamelCase(			lowerCAmelCase__				:		int=False    ,    lowerCAmelCase__				:		List[str]=False      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   Accelerator(
				    split_batches=lowerCAmelCase__    ,    dispatch_batches=lowerCAmelCase__    ,    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_   :	Optional[Any]													=   get_training_setup(lowerCAmelCase__    ,    lowerCAmelCase__      )
				for iteration, batch in enumerate(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	List[Any]													=   batch.values()
								# Gather the distributed inputs and targs for the base model
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   accelerator.gather((ddp_input, ddp_target)      )
								SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   input.to(accelerator.device      ), target.to(accelerator.device      )
								# Perform our initial ground truth step in non "DDP"
								model.train()
								ddp_model.train()
								step_model(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )
								opt.step()

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



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Accelerator()

				SCREAMING_SNAKE_CASE_   :	int													=   RegressionDataset(length=80      )
				SCREAMING_SNAKE_CASE_   :	str													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   RegressionDataset(length=96      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   DataLoader(lowerCAmelCase__    ,    batch_size=16      )
				SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   accelerator.prepare(lowerCAmelCase__    ,    lowerCAmelCase__      )
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(lowerCAmelCase__      ):
								assert id(accelerator.gradient_state.active_dataloader      ) == id(lowerCAmelCase__      )
								if iteration < len(lowerCAmelCase__      ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(lowerCAmelCase__      ):
																				assert id(accelerator.gradient_state.active_dataloader      ) == id(lowerCAmelCase__      )
																				if batch_num < len(lowerCAmelCase__      ) - 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 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   Accelerator()
				SCREAMING_SNAKE_CASE_   :	str													=   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(lowerCAmelCase__      )
				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(lowerCAmelCase__      )
				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(lowerCAmelCase__    ,    lowerCAmelCase__      )

    # 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(lowerCAmelCase__    ,    lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[int]      ):





				'''simple docstring'''







				main()


if __name__ == "__main__":
				main()