
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 							a		(				snake_case__:   List[Any]		,  snake_case__:   Dict		,  snake_case__:   List[Any]		,  snake_case__:   List[Any]   ):


				'''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 							a		(				snake_case__:   List[str]		,  snake_case__:   Any		,  snake_case__:   List[str]		,  snake_case__:   Optional[int]		,  snake_case__:   List[Any]=True   ):


				'''simple docstring'''
				model.train()
				lowercase_	    =   model(snake_case__   )
				lowercase_	    =   F.mse_loss(snake_case__		,  target.to(output.device   )   )
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(snake_case__   )




def 							a		(				snake_case__:   Any		,  snake_case__:   Optional[Any]=False   ):


				'''simple docstring'''
				set_seed(42   )
				lowercase_	    =   RegressionModel()
				lowercase_	    =   deepcopy(snake_case__   )
				lowercase_	    =   RegressionDataset(length=80   )
				lowercase_	    =   DataLoader(snake_case__		,  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(snake_case__		,  lr_lambda=lambda snake_case__   : epoch**0.6_5   )
								lowercase_	    =   LambdaLR(snake_case__		,  lr_lambda=lambda snake_case__   : epoch**0.6_5   )
				# Make a copy of `model`
				if sched:
								lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   accelerator.prepare(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
				else:
								lowercase_    ,      lowercase_	    =   accelerator.prepare(snake_case__		,  snake_case__   )
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader




def 							a		(				snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				# Test when on a single CPU or GPU that the context manager does nothing
				lowercase_    ,      lowercase_    ,      lowercase_	    =   get_training_setup(snake_case__   )
				# Use a single batch
				lowercase_    ,      lowercase_	    =   next(iter(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(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(snake_case__   ):
																step_model(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								else:
												# Sync grads
												step_model(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(snake_case__		,  snake_case__		,  snake_case__		,  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_337 + iteration   )
								lowercase_	    =   ddp_input[torch.randperm(len(snake_case__   )   )]




def 							a		(				snake_case__:   Optional[Any]   ):


				'''simple docstring'''
				# Test on distributed setup that context manager behaves properly
				lowercase_    ,      lowercase_    ,      lowercase_	    =   get_training_setup(snake_case__   )
				# Use a single batch
				lowercase_    ,      lowercase_	    =   next(iter(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(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(snake_case__   ):
																step_model(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								else:
												# Sync grads
												step_model(snake_case__		,  snake_case__		,  snake_case__		,  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_337 + iteration   )
								lowercase_	    =   ddp_input[torch.randperm(len(snake_case__   )   )]




def 							a		(				snake_case__:   int=False		,  snake_case__:   Union[str, Any]=False   ):


				'''simple docstring'''
				lowercase_	    =   Accelerator(
				    split_batches=snake_case__		,  dispatch_batches=snake_case__		,  gradient_accumulation_steps=2   )
				# Test that context manager behaves properly
				lowercase_    ,      lowercase_    ,      lowercase_	    =   get_training_setup(snake_case__   )
				for iteration, batch in enumerate(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(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(snake_case__   ):
												step_model(snake_case__		,  snake_case__		,  snake_case__		,  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(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_337 + iteration   )
								lowercase_	    =   ddp_input[torch.randperm(len(snake_case__   )   )]
				GradientState._reset_state()




def 							a		(				snake_case__:   Optional[int]=False		,  snake_case__:   List[str]=False   ):


				'''simple docstring'''
				lowercase_	    =   Accelerator(
				    split_batches=snake_case__		,  dispatch_batches=snake_case__		,  gradient_accumulation_steps=2   )
				# Test that context manager behaves properly
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   get_training_setup(snake_case__		,  snake_case__   )
				for iteration, batch in enumerate(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(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								opt.step()

								if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(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(snake_case__   ):
												step_model(snake_case__		,  snake_case__		,  snake_case__		,  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(snake_case__   ))
								if accelerator.num_processes > 1:
												check_model_parameters(snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								# Shuffle ddp_input on each iteration
								torch.manual_seed(1_337 + iteration   )
				GradientState._reset_state()




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   Accelerator()

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


				'''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(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(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(snake_case__		,  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(snake_case__		,  snake_case__   )




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
						main()






__a         =   'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'




def 							a		(				snake_case__:   bytes   ):


				'''simple docstring'''
				# Make sure the supplied data is a bytes-like object
				if not isinstance(snake_case__		,  snake_case__   ):
								lowercase_	    =   F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
								raise TypeError(snake_case__   )

				lowercase_	    =   ''''''.join(bin(snake_case__   )[2:].zfill(8   ) for byte in data   )

				lowercase_	    =   len(snake_case__   ) % 6 != 0

				if padding_needed:
								# The padding that will be added later
								lowercase_	    =   B'''=''' * ((6 - len(snake_case__   ) % 6) // 2)

								# Append binary_stream with arbitrary binary digits (0's by default) to make its
								# length a multiple of 6.
								binary_stream += "0" * (6 - len(snake_case__   ) % 6)
				else:
								lowercase_	    =   B''''''

				# Encode every 6 binary digits to their corresponding Base64 character
				return (
				    "".join(
				        B64_CHARSET[int(binary_stream[index : index + 6]		,  2   )]
				        for index in range(0		,  len(snake_case__   )		,  6   )   ).encode()
				    + padding
				)




def 							a		(				snake_case__:   str   ):


				'''simple docstring'''
				# Make sure encoded_data is either a string or a bytes-like object
				if not isinstance(snake_case__		,  snake_case__   ) and not isinstance(snake_case__		,  snake_case__   ):
								lowercase_	    =   (
								    '''argument should be a bytes-like object or ASCII string, '''
								    F'''not \'{encoded_data.__class__.__name__}\''''
								)
								raise TypeError(snake_case__   )

				# In case encoded_data is a bytes-like object, make sure it contains only
				# ASCII characters so we convert it to a string object
				if isinstance(snake_case__		,  snake_case__   ):
								try:
												lowercase_	    =   encoded_data.decode('''utf-8'''   )
								except UnicodeDecodeError:
												raise ValueError('''base64 encoded data should only contain ASCII characters'''   )

				lowercase_	    =   encoded_data.count('''='''   )

				# Check if the encoded string contains non base64 characters
				if padding:
								assert all(
								    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
				else:
								assert all(
								    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

				# Check the padding
				assert len(snake_case__   ) % 4 == 0 and padding < 3, "Incorrect padding"

				if padding:
								# Remove padding if there is one
								lowercase_	    =   encoded_data[:-padding]

								lowercase_	    =   ''''''.join(
								    bin(B64_CHARSET.index(snake_case__   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
				else:
								lowercase_	    =   ''''''.join(
								    bin(B64_CHARSET.index(snake_case__   )   )[2:].zfill(6   ) for char in encoded_data   )

				lowercase_	    =   [
				    int(binary_stream[index : index + 8]		,  2   )
				    for index in range(0		,  len(snake_case__   )		,  8   )
				]

				return bytes(snake_case__   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




