






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__a:     Dict   					=					['''small''', '''medium''', '''large''']

__a:     int   					=					'''lm_head.decoder.weight'''
__a:     Dict   					=					'''lm_head.weight'''




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						int:
       _UpperCAmelCase							=    torch.load(__snake_case )
       _UpperCAmelCase							=    d.pop(__snake_case )
       os.makedirs(__snake_case   ,      exist_ok=__snake_case )
       torch.save(__snake_case   ,      os.path.join(__snake_case   ,      __snake_case ) )


if __name__ == "__main__":
  __a:     Optional[Any]   					=					argparse.ArgumentParser()
  parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
  __a:     str   					=					parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    __a:     Optional[int]   					=					os.path.join(args.dialogpt_path, F"{MODEL}_ft.pkl")
    __a:     Dict   					=					F"./DialoGPT-{MODEL}"
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )







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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						List[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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case=True )				->						Optional[int]:
       model.train()
       _UpperCAmelCase							=    model(__snake_case )
       _UpperCAmelCase							=    F.mse_loss(__snake_case   ,      target.to(output.device ) )
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(__snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case=False )				->						Optional[int]:
       set_seed(4_2 )
       _UpperCAmelCase							=    RegressionModel()
       _UpperCAmelCase							=    deepcopy(__snake_case )
       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       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(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
              _UpperCAmelCase							=    LambdaLR(__snake_case   ,      lr_lambda=lambda __snake_case : epoch**0.65 )
       # Make a copy of `model`
       if sched:
              _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
       else:
              _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Dict:
       # Test when on a single CPU or GPU that the context manager does nothing
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).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(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              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_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						int:
       # Test on distributed setup that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       # Use a single batch
       _UpperCAmelCase	,			_UpperCAmelCase							=    next(iter(__snake_case ) ).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(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(__snake_case ):
                            step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              else:
                     # Sync grads
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # 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_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Any:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _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(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )

              # 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(__snake_case ) - 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_3_3_7 + iteration )
              _UpperCAmelCase							=    ddp_input[torch.randperm(len(__snake_case ) )]
       GradientState._reset_state()




def       _SCREAMING_SNAKE_CASE (							__snake_case=False   ,      __snake_case=False )				->						Dict:
       _UpperCAmelCase							=    Accelerator(
           split_batches=__snake_case   ,      dispatch_batches=__snake_case   ,      gradient_accumulation_steps=2 )
       # Test that context manager behaves properly
       _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    get_training_setup(__snake_case   ,      __snake_case )
       for iteration, batch in enumerate(__snake_case ):
              _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(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__snake_case )):
                     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(__snake_case ):
                     step_model(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
                     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(__snake_case ))
              if accelerator.num_processes > 1:
                     check_model_parameters(__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_3_3_7 + iteration )
       GradientState._reset_state()




def       _SCREAMING_SNAKE_CASE (							)				->						Union[str, Any]:
       _UpperCAmelCase							=    Accelerator()

       _UpperCAmelCase							=    RegressionDataset(length=8_0 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase							=    RegressionDataset(length=9_6 )
       _UpperCAmelCase							=    DataLoader(__snake_case   ,      batch_size=1_6 )
       _UpperCAmelCase	,			_UpperCAmelCase							=    accelerator.prepare(__snake_case   ,      __snake_case )
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(__snake_case ):
              assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
              if iteration < len(__snake_case ) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(__snake_case ):
                                   assert id(accelerator.gradient_state.active_dataloader ) == id(__snake_case )
                                   if batch_num < len(__snake_case ) - 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       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       _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(__snake_case )
       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(__snake_case )
       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(__snake_case   ,      __snake_case )

    # 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(__snake_case   ,      __snake_case )




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
  main()