


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       __lowercase						(    _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase  )     ->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       __lowercase						(    _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase=True  )     ->Any:







      """simple docstring"""

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




def       __lowercase						(    _UpperCamelCase,     _UpperCamelCase=False  )     ->List[Any]:







      """simple docstring"""

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




def       __lowercase						(    _UpperCamelCase  )     ->Tuple:







      """simple docstring"""

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




def       __lowercase						(    _UpperCamelCase  )     ->Optional[int]:







      """simple docstring"""

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




def       __lowercase						(    _UpperCamelCase=False,     _UpperCamelCase=False  )     ->str:







      """simple docstring"""

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




def       __lowercase						(    _UpperCamelCase=False,     _UpperCamelCase=False  )     ->List[str]:







      """simple docstring"""

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







      """simple docstring"""

      lowercase      : Dict        =		Accelerator()

      lowercase      : List[str]        =		RegressionDataset(length=80  )
      lowercase      : Optional[Any]        =		DataLoader(_lowercase,     batch_size=16  )
      lowercase      : Union[str, Any]        =		RegressionDataset(length=96  )
      lowercase      : Tuple        =		DataLoader(_lowercase,     batch_size=16  )
      lowercase ,      lowercase      : Optional[int]        =		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       __lowercase						(    )     ->Tuple:







      """simple docstring"""

      lowercase      : str        =		Accelerator()
      lowercase      : Tuple        =		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       __lowercase						(    _UpperCamelCase  )     ->str:







      """simple docstring"""

      main()


if __name__ == "__main__":
  main()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case   							=						{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''UniSpeechForCTC''',
        '''UniSpeechForPreTraining''',
        '''UniSpeechForSequenceClassification''',
        '''UniSpeechModel''',
        '''UniSpeechPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_unispeech import (
            UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
            UniSpeechForCTC,
            UniSpeechForPreTraining,
            UniSpeechForSequenceClassification,
            UniSpeechModel,
            UniSpeechPreTrainedModel,
        )

else:
    import sys

    __snake_case   							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


