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_					(	__A,    __A,    __A,    __A				)	->							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   lowerCAmelCase_					(	__A,    __A,    __A,    __A,    __A=True				)	->							Tuple:







      '''simple docstring'''


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




def   lowerCAmelCase_					(	__A,    __A=False				)	->							Union[str, Any]:







      '''simple docstring'''


      set_seed(42				)
      UpperCAmelCase__								=    RegressionModel()
      UpperCAmelCase__								=    deepcopy(_UpperCamelCase				)
      UpperCAmelCase__								=    RegressionDataset(length=80				)
      UpperCAmelCase__								=    DataLoader(_UpperCamelCase,    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(_UpperCamelCase,    lr_lambda=lambda __A				: epoch**0.65				)
            UpperCAmelCase__								=    LambdaLR(_UpperCamelCase,    lr_lambda=lambda __A				: epoch**0.65				)
      # Make a copy of `model`
      if sched:
            UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__								=    accelerator.prepare(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
      else:
            UpperCAmelCase__						, UpperCAmelCase__								=    accelerator.prepare(_UpperCamelCase,    _UpperCamelCase				)
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader




def   lowerCAmelCase_					(	__A				)	->							Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__								=    get_training_setup(_UpperCamelCase				)
      # Use a single batch
      UpperCAmelCase__						, UpperCAmelCase__								=    next(iter(_UpperCamelCase				)				).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(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(_UpperCamelCase				):
                        step_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            else:
                  # Sync grads
                  step_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)

            # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
            check_model_parameters(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            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(1_337 + iteration				)
            UpperCAmelCase__								=    ddp_input[torch.randperm(len(_UpperCamelCase				)				)]




def   lowerCAmelCase_					(	__A				)	->							Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__								=    get_training_setup(_UpperCamelCase				)
      # Use a single batch
      UpperCAmelCase__						, UpperCAmelCase__								=    next(iter(_UpperCamelCase				)				).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(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(_UpperCamelCase				):
                        step_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            else:
                  # Sync grads
                  step_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)

            # 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(1_337 + iteration				)
            UpperCAmelCase__								=    ddp_input[torch.randperm(len(_UpperCamelCase				)				)]




def   lowerCAmelCase_					(	__A=False,    __A=False				)	->							Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=    Accelerator(
          split_batches=_UpperCamelCase,    dispatch_batches=_UpperCamelCase,    gradient_accumulation_steps=2				)
      # Test that context manager behaves properly
      UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__								=    get_training_setup(_UpperCamelCase				)
      for iteration, batch in enumerate(_UpperCamelCase				):
            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(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            # Do "gradient accumulation" (noop)
            with accelerator.accumulate(_UpperCamelCase				):
                  step_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)

            # 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(_UpperCamelCase				) - 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(1_337 + iteration				)
            UpperCAmelCase__								=    ddp_input[torch.randperm(len(_UpperCamelCase				)				)]
      GradientState._reset_state()




def   lowerCAmelCase_					(	__A=False,    __A=False				)	->							Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=    Accelerator(
          split_batches=_UpperCamelCase,    dispatch_batches=_UpperCamelCase,    gradient_accumulation_steps=2				)
      # Test that context manager behaves properly
      UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__						, UpperCAmelCase__								=    get_training_setup(_UpperCamelCase,    _UpperCamelCase				)
      for iteration, batch in enumerate(_UpperCamelCase				):
            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(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            opt.step()

            if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_UpperCamelCase				)):
                  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(_UpperCamelCase				):
                  step_model(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
                  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(_UpperCamelCase				))
            if accelerator.num_processes > 1:
                  check_model_parameters(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase				)
            # Shuffle ddp_input on each iteration
            torch.manual_seed(1_337 + iteration				)
      GradientState._reset_state()




def   lowerCAmelCase_					(	)	->							List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=    Accelerator()

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







      '''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(_UpperCamelCase				)
      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(_UpperCamelCase				)
      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(_UpperCamelCase,    _UpperCamelCase				)

                        # 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(_UpperCamelCase,    _UpperCamelCase				)




def   lowerCAmelCase_					(	__A				)	->							Optional[Any]:







      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()






import argparse
import os
import re


a_     :    List[str]     		=   "src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a_     :    Optional[Any]     		=   re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
a_     :    Optional[int]     		=   re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	bool = False						)		->				Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,				'r'		,				encoding='utf-8'						) as f:
  SCREAMING_SNAKE_CASE		    =      f.read()

 SCREAMING_SNAKE_CASE		    =      content.split('\n'						)
 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      0
 while line_idx < len(_UpperCamelCase						):
  if _re_intro_mapping.search(lines[line_idx]						) is not None:
   SCREAMING_SNAKE_CASE		    =      len(re.search(R'^(\s*)\S'		,				lines[line_idx]						).groups()[0]						) + 8
   # Start of a new mapping!
   while not lines[line_idx].startswith(' ' * indent + '('						):
    new_lines.append(lines[line_idx]						)
    line_idx += 1

   SCREAMING_SNAKE_CASE		    =      []
   while lines[line_idx].strip() != "]":
    # Blocks either fit in one line or not
    if lines[line_idx].strip() == "(":
     SCREAMING_SNAKE_CASE		    =      line_idx
     while not lines[line_idx].startswith(' ' * indent + ')'						):
      line_idx += 1
     blocks.append('\n'.join(lines[start_idx : line_idx + 1]						)						)
    else:
     blocks.append(lines[line_idx]						)
    line_idx += 1

   # Sort blocks by their identifiers
   SCREAMING_SNAKE_CASE		    =      sorted(_UpperCamelCase		,				key=lambda _UpperCamelCase						: _re_identifier.search(_UpperCamelCase						).groups()[0]						)
   new_lines += blocks
  else:
   new_lines.append(lines[line_idx]						)
   line_idx += 1

 if overwrite:
  with open(_UpperCamelCase		,				'w'		,				encoding='utf-8'						) as f:
   f.write('\n'.join(_UpperCamelCase						)						)
 elif "\n".join(_UpperCamelCase						) != content:
  return True


def 							__lowerCAmelCase     (							_UpperCamelCase		:	bool = False						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [os.path.join(_UpperCamelCase		,				_UpperCamelCase						) for f in os.listdir(_UpperCamelCase						) if f.endswith('.py'						)]
 SCREAMING_SNAKE_CASE		    =      [sort_auto_mapping(_UpperCamelCase		,				overwrite=_UpperCamelCase						) for fname in fnames]

 if not overwrite and any(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE		    =      [f for f, d in zip(_UpperCamelCase		,				_UpperCamelCase						) if d]
  raise ValueError(
      f"""The following files have auto mappings that need sorting: {', '.join(_UpperCamelCase						)}. Run `make style` to fix"""
      ' this.'						)


if __name__ == "__main__":
      a_     :    Union[str, Any]     		=   argparse.ArgumentParser()
      parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
      a_     :    List[str]     		=   parser.parse_args()

      sort_all_auto_mappings(not args.check_only)

