





'''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  a__    (    lowercase :					List[str], lowercase :					Tuple, lowercase :					Union[str, Any], lowercase :					str				)		->		Union[str, 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__    (    lowercase :					List[str], lowercase :					Optional[Any], lowercase :					Union[str, Any], lowercase :					int, lowercase :					Dict=True				)		->		List[str]:





     """simple docstring"""

     model.train()
     _UpperCamelCase       =	model(lowercase				)
     _UpperCamelCase       =	F.mse_loss(lowercase, target.to(output.device				)				)
     if not do_backward:
          loss /= accelerator.gradient_accumulation_steps
          loss.backward()
     else:
          accelerator.backward(lowercase				)







def  a__    (    lowercase :					int, lowercase :					Optional[Any]=False				)		->		Union[str, Any]:





     """simple docstring"""

     set_seed(42				)
     _UpperCamelCase       =	RegressionModel()
     _UpperCamelCase       =	deepcopy(lowercase				)
     _UpperCamelCase       =	RegressionDataset(length=80				)
     _UpperCamelCase       =	DataLoader(lowercase, batch_size=16				)
     model.to(accelerator.device				)
     if sched:
          _UpperCamelCase       =	AdamW(params=model.parameters(), lr=1e-3				)
          _UpperCamelCase       =	AdamW(params=ddp_model.parameters(), lr=1e-3				)
          _UpperCamelCase       =	LambdaLR(lowercase, lr_lambda=lambda lowercase				: epoch**0.6_5				)
          _UpperCamelCase       =	LambdaLR(lowercase, lr_lambda=lambda lowercase				: epoch**0.6_5				)
     # Make a copy of `model`
     if sched:
          _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(lowercase, lowercase, lowercase, lowercase				)
     else:
          _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(lowercase, lowercase				)
     if sched:
          return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
     return model, ddp_model, dataloader







def  a__    (    lowercase :					Optional[Any]				)		->		Optional[Any]:





     """simple docstring"""

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

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







def  a__    (    lowercase :					Optional[int]				)		->		Dict:





     """simple docstring"""

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

          # 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				)
          _UpperCamelCase       =	ddp_input[torch.randperm(len(lowercase				)				)]







def  a__    (    lowercase :					int=False, lowercase :					List[str]=False				)		->		List[str]:





     """simple docstring"""

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

          # 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(lowercase				) - 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				)
          _UpperCamelCase       =	ddp_input[torch.randperm(len(lowercase				)				)]
     GradientState._reset_state()







def  a__    (    lowercase :					Tuple=False, lowercase :					Dict=False				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	Accelerator(
         split_batches=lowercase, dispatch_batches=lowercase, gradient_accumulation_steps=2				)
     # Test that context manager behaves properly
     _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	get_training_setup(lowercase, lowercase				)
     for iteration, batch in enumerate(lowercase				):
          _UpperCamelCase   ,    _UpperCamelCase       =	batch.values()
          # Gather the distributed inputs and targs for the base model
          _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.gather((ddp_input, ddp_target)				)
          _UpperCamelCase   ,    _UpperCamelCase       =	input.to(accelerator.device				), target.to(accelerator.device				)
          # Perform our initial ground truth step in non "DDP"
          model.train()
          ddp_model.train()
          step_model(lowercase, lowercase, lowercase, lowercase, lowercase				)
          opt.step()

          if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase				)):
               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(lowercase				):
               step_model(lowercase, lowercase, lowercase, lowercase				)
               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"""
          _UpperCamelCase       =	(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase				))
          if accelerator.num_processes > 1:
               check_model_parameters(lowercase, lowercase, lowercase, lowercase				)
          # Shuffle ddp_input on each iteration
          torch.manual_seed(1337 + iteration				)
     GradientState._reset_state()







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	Accelerator()

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





     """simple docstring"""

     _UpperCamelCase       =	Accelerator()
     _UpperCamelCase       =	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(lowercase				)
     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(lowercase				)
     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(lowercase, lowercase				)

    # 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(lowercase, lowercase				)







def  a__    (    lowercase :					int				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()










'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)

lowercase__						:      List[Any]	=    {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   'biogpt'





       def __init__(							self       :		Optional[Any]	,			lowerCAmelCase__       :		List[str]=42384	,			lowerCAmelCase__       :		Optional[int]=1024	,			lowerCAmelCase__       :		List[str]=24	,			lowerCAmelCase__       :		List[Any]=16	,			lowerCAmelCase__       :		Optional[int]=4096	,			lowerCAmelCase__       :		Optional[int]="gelu"	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		Optional[int]=0.1	,			lowerCAmelCase__       :		Union[str, Any]=1024	,			lowerCAmelCase__       :		List[str]=0.02	,			lowerCAmelCase__       :		Tuple=1e-1_2	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Tuple=True	,			lowerCAmelCase__       :		Dict=0.0	,			lowerCAmelCase__       :		Union[str, Any]=0.0	,			lowerCAmelCase__       :		Optional[int]=1	,			lowerCAmelCase__       :		Union[str, Any]=0	,			lowerCAmelCase__       :		Optional[Any]=2	,			**lowerCAmelCase__       :		Optional[Any]	,			)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	hidden_size
            _UpperCamelCase       =	num_hidden_layers
            _UpperCamelCase       =	num_attention_heads
            _UpperCamelCase       =	intermediate_size
            _UpperCamelCase       =	hidden_act
            _UpperCamelCase       =	hidden_dropout_prob
            _UpperCamelCase       =	attention_probs_dropout_prob
            _UpperCamelCase       =	initializer_range
            _UpperCamelCase       =	layer_norm_eps
            _UpperCamelCase       =	scale_embedding
            _UpperCamelCase       =	use_cache
            _UpperCamelCase       =	layerdrop
            _UpperCamelCase       =	activation_dropout
            super().__init__(pad_token_id=lowerCAmelCase__	,			bos_token_id=lowerCAmelCase__	,			eos_token_id=lowerCAmelCase__	,			**lowerCAmelCase__					)



