



'''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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		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_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=True  )      ->				Optional[Any]:
		model.train()
		_a					:				Tuple				=							model(lowerCAmelCase_  )
		_a					:				Tuple				=							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_						,      lowerCAmelCase_=False  )      ->				Union[str, Any]:
		set_seed(42  )
		_a					:				List[str]				=							RegressionModel()
		_a					:				Any				=							deepcopy(lowerCAmelCase_  )
		_a					:				Any				=							RegressionDataset(length=80  )
		_a					:				Tuple				=							DataLoader(lowerCAmelCase_						,      batch_size=16  )
		model.to(accelerator.device  )
		if sched:
				_a					:				str				=							AdamW(params=model.parameters()						,      lr=1E-3  )
				_a					:				List[Any]				=							AdamW(params=ddp_model.parameters()						,      lr=1E-3  )
				_a					:				Dict				=							LambdaLR(lowerCAmelCase_						,      lr_lambda=lambda lowerCAmelCase_  : epoch**0.65  )
				_a					:				Any				=							LambdaLR(lowerCAmelCase_						,      lr_lambda=lambda lowerCAmelCase_  : epoch**0.65  )
		# Make a copy of `model`
		if sched:
				_a					:				Optional[int]				=							accelerator.prepare(lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )
		else:
				_a					:				Optional[Any]				=							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_  )      ->				Tuple:
		# Test when on a single CPU or GPU that the context manager does nothing
		_a					:				str				=							get_training_setup(lowerCAmelCase_  )
		# Use a single batch
		_a					:				int				=							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					:				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  )
				_a					:				Dict				=							ddp_input[torch.randperm(len(lowerCAmelCase_  )  )]



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		# Test on distributed setup that context manager behaves properly
		_a					:				Tuple				=							get_training_setup(lowerCAmelCase_  )
		# Use a single batch
		_a					:				Union[str, Any]				=							next(iter(lowerCAmelCase_  )  ).values()
		for iteration in range(3  ):
				# Gather the distributed inputs and targs for the base model
				_a					:				str				=							accelerator.gather((ddp_input, ddp_target)  )
				_a					:				Dict				=							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					:				Dict				=							ddp_input[torch.randperm(len(lowerCAmelCase_  )  )]



def 					__lowerCamelCase					(				lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				List[Any]:
		_a					:				str				=							Accelerator(
		    split_batches=lowerCAmelCase_						,      dispatch_batches=lowerCAmelCase_						,      gradient_accumulation_steps=2  )
		# Test that context manager behaves properly
		_a					:				List[str]				=							get_training_setup(lowerCAmelCase_  )
		for iteration, batch in enumerate(lowerCAmelCase_  ):
				_a					:				Union[str, Any]				=							batch.values()
				# Gather the distributed inputs and targs for the base model
				_a					:				Optional[Any]				=							accelerator.gather((ddp_input, ddp_target)  )
				_a					:				Union[str, 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					:				Union[str, Any]				=							ddp_input[torch.randperm(len(lowerCAmelCase_  )  )]
		GradientState._reset_state()



def 					__lowerCamelCase					(				lowerCAmelCase_=False						,      lowerCAmelCase_=False  )      ->				Union[str, Any]:
		_a					:				List[Any]				=							Accelerator(
		    split_batches=lowerCAmelCase_						,      dispatch_batches=lowerCAmelCase_						,      gradient_accumulation_steps=2  )
		# Test that context manager behaves properly
		_a					:				Tuple				=							get_training_setup(lowerCAmelCase_						,      lowerCAmelCase_  )
		for iteration, batch in enumerate(lowerCAmelCase_  ):
				_a					:				List[str]				=							batch.values()
				# Gather the distributed inputs and targs for the base model
				_a					:				Dict				=							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"
				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					:				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					(				)      ->				Tuple:
		_a					:				List[str]				=							Accelerator()

		_a					:				Union[str, Any]				=							RegressionDataset(length=80  )
		_a					:				str				=							DataLoader(lowerCAmelCase_						,      batch_size=16  )
		_a					:				Any				=							RegressionDataset(length=96  )
		_a					:				Union[str, Any]				=							DataLoader(lowerCAmelCase_						,      batch_size=16  )
		_a					:				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 					__lowerCamelCase					(				)      ->				Tuple:
		_a					:				Union[str, Any]				=							Accelerator()
		_a					:				Dict				=							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_  )      ->				Any:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
							main()









'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__lowerCAmelCase						      =       '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__lowerCAmelCase						      =       [
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
__lowerCAmelCase						      =       dict(zip(vocab, range(len(vocab))))
__lowerCAmelCase						      =       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase						      =       Path(tmpdirname)
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''merges_file''']
							with open(src_vocab_file, '''w''') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, '''w''') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, '''w''') as fp:
														fp.write('''\n'''.join(merges))

							__lowerCAmelCase						      =       FSMTTokenizer(
							    langs=['''en''', '''ru'''],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__lowerCAmelCase						      =       FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__lowerCAmelCase						      =       FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase						      =       tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__lowerCAmelCase						      =       tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru




