

"""simple docstring"""




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	(			_lowercase		:					Dict					, _lowercase		:					Any					, _lowercase		:					str					, _lowercase		:					Optional[int]      )				->List[str]:






	'''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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					str					, _lowercase		:					Dict					, _lowercase		:					Union[str, Any]					, _lowercase		:					Dict=True      )				->str:






	'''simple docstring'''






	model.train()
	a   :	Optional[int]			=       model(_lowerCamelCase      )
	a   :	Union[str, 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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					Any=False      )				->Any:






	'''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[Any]      )				->str:






	'''simple docstring'''






	a   :	str			=       get_training_setup(_lowerCamelCase      )
	# Use a single batch
	a   :	List[str]			=       next(iter(_lowerCamelCase      )      ).values()
	for iteration in range(3      ):
		# Gather the distributed inputs and targs for the base model
		a   :	int			=       accelerator.gather((ddp_input, ddp_target)      )
		a   :	str			=       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      )
		a   :	List[Any]			=       ddp_input[torch.randperm(len(_lowerCamelCase      )      )]






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict      )				->int:






	'''simple docstring'''






	a   :	List[Any]			=       get_training_setup(_lowerCamelCase      )
	# Use a single batch
	a   :	List[Any]			=       next(iter(_lowerCamelCase      )      ).values()
	for iteration in range(3      ):
		# Gather the distributed inputs and targs for the base model
		a   :	List[str]			=       accelerator.gather((ddp_input, ddp_target)      )
		a   :	List[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      )

		# 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      )
		a   :	Optional[Any]			=       ddp_input[torch.randperm(len(_lowerCamelCase      )      )]






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]=False					, _lowercase		:					List[str]=False      )				->Union[str, Any]:






	'''simple docstring'''






	a   :	Tuple			=       Accelerator(
	    split_batches=_lowerCamelCase					, dispatch_batches=_lowerCamelCase					, gradient_accumulation_steps=2      )
	# Test that context manager behaves properly
	a   :	List[Any]			=       get_training_setup(_lowerCamelCase      )
	for iteration, batch in enumerate(_lowerCamelCase      ):
		a   :	Optional[Any]			=       batch.values()
		# Gather the distributed inputs and targs for the base model
		a   :	Dict			=       accelerator.gather((ddp_input, ddp_target)      )
		a   :	Any			=       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      )
		a   :	int			=       ddp_input[torch.randperm(len(_lowerCamelCase      )      )]
	GradientState._reset_state()






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]=False					, _lowercase		:					int=False      )				->Optional[int]:






	'''simple docstring'''






	a   :	Any			=       Accelerator(
	    split_batches=_lowerCamelCase					, dispatch_batches=_lowerCamelCase					, gradient_accumulation_steps=2      )
	# Test that context manager behaves properly
	a   :	Any			=       get_training_setup(_lowerCamelCase					, _lowerCamelCase      )
	for iteration, batch in enumerate(_lowerCamelCase      ):
		a   :	Optional[int]			=       batch.values()
		# Gather the distributed inputs and targs for the base model
		a   :	Optional[Any]			=       accelerator.gather((ddp_input, ddp_target)      )
		a   :	Optional[int]			=       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"""
		a   :	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    _SCREAMING_SNAKE_CASE	(			)				->List[Any]:






	'''simple docstring'''






	a   :	List[Any]			=       Accelerator()

	a   :	str			=       RegressionDataset(length=80      )
	a   :	Dict			=       DataLoader(_lowerCamelCase					, batch_size=16      )
	a   :	int			=       RegressionDataset(length=96      )
	a   :	List[Any]			=       DataLoader(_lowerCamelCase					, batch_size=16      )
	a   :	List[str]			=       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    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	int			=       Accelerator()
	a   :	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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]      )				->List[str]:






	'''simple docstring'''






	main()


if __name__ == "__main__":
		main()









"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_albert import AlbertTokenizer
else:
		a		:				Tuple  			=    None

a		:				Any  			=    logging.get_logger(__name__)
a		:				List[Any]  			=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				str  			=    {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

a		:				str  			=    {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

a		:				Union[str, Any]  			=    '''▁'''


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      List[Any]         =AlbertTokenizer






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[MASK]"   ,      **lowerCAmelCase__   ,      )			->				Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								a   :	Optional[int]			=       (
								    AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ,      normalized=lowerCAmelCase__   )
								    if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								    else mask_token
								)

								super().__init__(
								    lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      remove_space=lowerCAmelCase__   ,      keep_accents=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	Dict			=       do_lower_case
								a   :	Any			=       remove_space
								a   :	Optional[Any]			=       keep_accents
								a   :	List[str]			=       vocab_file
								a   :	Optional[Any]			=       False if not self.vocab_file else True






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."   )

								if not os.path.isdir(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	Dict			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase__   ):
									copyfile(self.vocab_file   ,      lowerCAmelCase__   )

								return (out_vocab_file,)


