
import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Optional[int]							=      int(lowerCamelCase							)
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      t // 3600, (t // 60) % 60, t % 60
 return F'''{h}:{m:02d}:{s:02d}''' if h != 0 else F'''{m:02d}:{s:02d}'''





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=300							):
 # docstyle-ignore
 return F'''
    <div>
      {prefix}
      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>
      {label}
    </div>
    '''





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Tuple							=      """<table border=\"1\" class=\"dataframe\">\n"""
 html_code += """  <thead>\n <tr style="text-align: left;">\n"""
 for i in items[0]:
  html_code += F'''      <th>{i}</th>\n'''
 html_code += "    </tr>\n  </thead>\n  <tbody>\n"
 for line in items[1:]:
  html_code += "    <tr>\n"
  for elt in line:
   lowerCamelCase		:	List[Any]							=      F'''{elt:.6f}''' if isinstance(lowerCamelCase,	lowerCamelCase							) else str(lowerCamelCase							)
   html_code += F'''      <td>{elt}</td>\n'''
  html_code += "    </tr>\n"
 html_code += "  </tbody>\n</table><p>"
 return html_code





class 							A__		:
      _UpperCAmelCase				:    List[Any]           =			5
      _UpperCAmelCase				:    Dict           =			0.2
      def __init__(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = 3_0_0    ,  ):
       lowerCamelCase		:	Optional[int]							=      total
       lowerCamelCase		:	Optional[Any]							=      """""" if prefix is None else prefix
       lowerCamelCase		:	Dict							=      leave
       lowerCamelCase		:	List[str]							=      parent
       lowerCamelCase		:	Optional[Any]							=      width
       lowerCamelCase		:	List[str]							=      None
       lowerCamelCase		:	List[str]							=      None
       lowerCamelCase		:	List[str]							=      None
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = False    ,  __magic_name__ = None      ):
       lowerCamelCase		:	Union[str, Any]							=      value
       if comment is not None:
        lowerCamelCase		:	Tuple							=      comment
       if self.last_value is None:
        lowerCamelCase		:	Tuple							=      time.time()
        lowerCamelCase		:	List[Any]							=      value
        lowerCamelCase		:	Optional[Any]							=      None
        lowerCamelCase		:	List[str]							=      self.warmup
        lowerCamelCase		:	Union[str, Any]							=      1
        self.update_bar(__magic_name__      )
       elif value <= self.last_value and not force_update:
        return
       elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for    ,  self.total      ):
        if self.first_calls > 0:
         self.first_calls -= 1
        lowerCamelCase		:	str							=      time.time()
        lowerCamelCase		:	Union[str, Any]							=      current_time - self.start_time
        # We could have value = self.start_value if the update is called twixe with the same start value.
        if value > self.start_value:
         lowerCamelCase		:	Optional[Any]							=      self.elapsed_time / (value - self.start_value)
        else:
         lowerCamelCase		:	List[Any]							=      None
        if value >= self.total:
         lowerCamelCase		:	Optional[int]							=      self.total
         lowerCamelCase		:	Optional[int]							=      None
         if not self.leave:
          self.close()
        elif self.average_time_per_item is not None:
         lowerCamelCase		:	str							=      self.average_time_per_item * (self.total - value)
        self.update_bar(__magic_name__      )
        lowerCamelCase		:	Dict							=      value
        lowerCamelCase		:	int							=      current_time
        if self.average_time_per_item is None:
         lowerCamelCase		:	List[Any]							=      1
        else:
         lowerCamelCase		:	Union[str, Any]							=      max(int(self.update_every / self.average_time_per_item      )    ,  1      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__=None      ):
       lowerCamelCase		:	str							=      """ """ * (len(str(self.total      )      ) - len(str(__magic_name__      )      )) + str(__magic_name__      )
       if self.elapsed_time is None:
        lowerCamelCase		:	List[Any]							=      F'''[{spaced_value}/{self.total} : < :'''
       elif self.predicted_remaining is None:
        lowerCamelCase		:	List[str]							=      F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time      )}'''
       else:
        lowerCamelCase		:	Union[str, Any]							=      (
            F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time      )} <'''
            F''' {format_time(self.predicted_remaining      )}'''
        )
        self.label += F''', {1/self.average_time_per_item:.2f} it/s'''
       self.label += "]" if self.comment is None or len(self.comment      ) == 0 else F''', {self.comment}]'''
       self.display()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      html_progress_bar(self.value    ,  self.total    ,  self.prefix    ,  self.label    ,  self.width      )
       if self.parent is not None:
        # If this is a child bar, the parent will take care of the display.
        self.parent.display()
        return
       if self.output is None:
        lowerCamelCase		:	int							=      disp.display(disp.HTML(self.html_code      )    ,  display_id=__magic_name__      )
       else:
        self.output.update(disp.HTML(self.html_code      )      )






      def 						UpperCamelCase__							(      self      ):
       if self.parent is None and self.output is not None:
        self.output.update(disp.HTML(""""""      )      )





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=None      ):
       super().__init__(__magic_name__      )
       lowerCamelCase		:	Dict							=      None if column_names is None else [column_names]
       lowerCamelCase		:	Optional[Any]							=      None
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      html_progress_bar(self.value    ,  self.total    ,  self.prefix    ,  self.label    ,  self.width      )
       if self.inner_table is not None:
        self.html_code += text_to_html_table(self.inner_table      )
       if self.child_bar is not None:
        self.html_code += self.child_bar.html_code
       if self.output is None:
        lowerCamelCase		:	List[str]							=      disp.display(disp.HTML(self.html_code      )    ,  display_id=__magic_name__      )
       else:
        self.output.update(disp.HTML(self.html_code      )      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if self.inner_table is None:
        lowerCamelCase		:	Union[str, Any]							=      [list(values.keys()      ), list(values.values()      )]
       else:
        lowerCamelCase		:	Union[str, Any]							=      self.inner_table[0]
        if len(self.inner_table      ) == 1:
         # We give a chance to update the column names at the first iteration
         for key in values.keys():
          if key not in columns:
           columns.append(__magic_name__      )
         lowerCamelCase		:	List[str]							=      columns
        self.inner_table.append([values[c] for c in columns]      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__=None    ,  __magic_name__=3_0_0      ):
       lowerCamelCase		:	Tuple							=      NotebookProgressBar(__magic_name__    ,  prefix=__magic_name__    ,  parent=self    ,  width=__magic_name__      )
       return self.child_bar






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      None
       self.display()





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self      ):
       lowerCamelCase		:	Optional[Any]							=      None
       lowerCamelCase		:	Optional[Any]							=      None
       lowerCamelCase		:	str							=      False
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase		:	str							=      """Epoch""" if args.evaluation_strategy == IntervalStrategy.EPOCH else """Step"""
       lowerCamelCase		:	List[Any]							=      0
       lowerCamelCase		:	Any							=      0
       lowerCamelCase		:	Tuple							=      [self.first_column] + ["""Training Loss"""]
       if args.evaluation_strategy != IntervalStrategy.NO:
        column_names.append("""Validation Loss"""      )
       lowerCamelCase		:	str							=      NotebookTrainingTracker(state.max_steps    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       lowerCamelCase		:	Union[str, Any]							=      int(state.epoch      ) if int(state.epoch      ) == state.epoch else F'''{state.epoch:.2f}'''
       self.training_tracker.update(
           state.global_step + 1    ,  comment=F'''Epoch {epoch}/{state.num_train_epochs}'''    ,  force_update=self._force_next_update    ,  )
       lowerCamelCase		:	List[Any]							=      False
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=None    ,  **__magic_name__      ):
       if not has_length(__magic_name__      ):
        return
       if self.prediction_bar is None:
        if self.training_tracker is not None:
         lowerCamelCase		:	Tuple							=      self.training_tracker.add_child(len(__magic_name__      )      )
        else:
         lowerCamelCase		:	Union[str, Any]							=      NotebookProgressBar(len(__magic_name__      )      )
        self.prediction_bar.update(1      )
       else:
        self.prediction_bar.update(self.prediction_bar.value + 1      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       if self.prediction_bar is not None:
        self.prediction_bar.close()
       lowerCamelCase		:	str							=      None
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=None    ,  **__magic_name__      ):
       # Only for when there is no evaluation
       if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
        lowerCamelCase		:	Optional[int]							=      {"""Training Loss""": logs["""loss"""]}
        # First column is necessarily Step sine we're not in epoch eval strategy
        lowerCamelCase		:	Union[str, Any]							=      state.global_step
        self.training_tracker.write_line(__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=None    ,  **__magic_name__      ):
       if self.training_tracker is not None:
        lowerCamelCase		:	Optional[int]							=      {"""Training Loss""": """No log""", """Validation Loss""": """No log"""}
        for log in reversed(state.log_history      ):
         if "loss" in log:
          lowerCamelCase		:	List[str]							=      log["""loss"""]
          break

        if self.first_column == "Epoch":
         lowerCamelCase		:	List[Any]							=      int(state.epoch      )
        else:
         lowerCamelCase		:	List[str]							=      state.global_step
        lowerCamelCase		:	Dict							=      """eval"""
        for k in metrics:
         if k.endswith("""_loss"""      ):
          lowerCamelCase		:	str							=      re.sub(r"""\_loss$"""    ,  """"""    ,  __magic_name__      )
        lowerCamelCase		:	Optional[int]							=      metrics.pop("""total_flos"""    ,  __magic_name__      )
        lowerCamelCase		:	List[Any]							=      metrics.pop("""epoch"""    ,  __magic_name__      )
        lowerCamelCase		:	Dict							=      metrics.pop(F'''{metric_key_prefix}_runtime'''    ,  __magic_name__      )
        lowerCamelCase		:	int							=      metrics.pop(F'''{metric_key_prefix}_samples_per_second'''    ,  __magic_name__      )
        lowerCamelCase		:	int							=      metrics.pop(F'''{metric_key_prefix}_steps_per_second'''    ,  __magic_name__      )
        lowerCamelCase		:	int							=      metrics.pop(F'''{metric_key_prefix}_jit_compilation_time'''    ,  __magic_name__      )
        for k, v in metrics.items():
         if k == F'''{metric_key_prefix}_loss''':
          lowerCamelCase		:	Tuple							=      v
         else:
          lowerCamelCase		:	str							=      k.split("""_"""      )
          lowerCamelCase		:	Tuple							=      """ """.join([part.capitalize() for part in splits[1:]]      )
          lowerCamelCase		:	Dict							=      v
        self.training_tracker.write_line(__magic_name__      )
        self.training_tracker.remove_child()
        lowerCamelCase		:	int							=      None
        # Evaluation takes a long time so we should force the next update.
        lowerCamelCase		:	str							=      True






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  **__magic_name__      ):
       self.training_tracker.update(
           state.global_step    ,  comment=F'''Epoch {int(state.epoch      )}/{state.num_train_epochs}'''    ,  force_update=__magic_name__      )
       lowerCamelCase		:	Optional[Any]							=      None






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





def     _a					(						lowerCamelCase							):
 # Test when on a single CPU or GPU that the context manager does nothing
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      get_training_setup(lowerCamelCase							)
 # Use a single batch
 lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      next(iter(lowerCamelCase							)							).values()
 for iteration in range(3							):
  # Gather the distributed inputs and targs for the base model
  lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      accelerator.gather((ddp_input, ddp_target)							)
  lowerCamelCase			,    lowerCamelCase		:	int							=      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							)
  lowerCamelCase		:	List[Any]							=      ddp_input[torch.randperm(len(lowerCamelCase							)							)]





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

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





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





def     _a					(						lowerCamelCase=False,	lowerCamelCase=False							):
 lowerCamelCase		:	List[Any]							=      Accelerator(
     split_batches=lowerCamelCase,	dispatch_batches=lowerCamelCase,	gradient_accumulation_steps=2							)
 # Test that context manager behaves properly
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      get_training_setup(lowerCamelCase,	lowerCamelCase							)
 for iteration, batch in enumerate(lowerCamelCase							):
  lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      batch.values()
  # Gather the distributed inputs and targs for the base model
  lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      accelerator.gather((ddp_input, ddp_target)							)
  lowerCamelCase			,    lowerCamelCase		:	Tuple							=      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'''
  lowerCamelCase		:	Union[str, Any]							=      (((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     _a					(						):
 lowerCamelCase		:	int							=      Accelerator()

 lowerCamelCase		:	Optional[Any]							=      RegressionDataset(length=80							)
 lowerCamelCase		:	List[str]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase		:	int							=      RegressionDataset(length=96							)
 lowerCamelCase		:	Optional[int]							=      DataLoader(lowerCamelCase,	batch_size=16							)
 lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      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     _a					(						):
 lowerCamelCase		:	List[Any]							=      Accelerator()
 lowerCamelCase		:	int							=      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     _a					(						lowerCamelCase							):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()




