


'''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__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      Tuple,   UpperCamelCase_						:      Dict,   UpperCamelCase_						:      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        a__     (					UpperCamelCase_						:      Tuple,   UpperCamelCase_						:      Dict,   UpperCamelCase_						:      str,   UpperCamelCase_						:      Optional[int],   UpperCamelCase_						:      Optional[Any]=True						):
   model.train()
   UpperCAmelCase__	:int      =      model(UpperCamelCase_						)
   UpperCAmelCase__	:Union[str, Any]      =      F.mse_loss(UpperCamelCase_,   target.to(output.device						)						)
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Any,   UpperCamelCase_						:      int=False						):
   set_seed(42						)
   UpperCAmelCase__	:Union[str, Any]      =      RegressionModel()
   UpperCAmelCase__	:int      =      deepcopy(UpperCamelCase_						)
   UpperCAmelCase__	:Dict      =      RegressionDataset(length=80						)
   UpperCAmelCase__	:Any      =      DataLoader(UpperCamelCase_,   batch_size=16						)
   model.to(accelerator.device						)
   if sched:
      UpperCAmelCase__	:Tuple      =      AdamW(params=model.parameters(),   lr=1e-3						)
      UpperCAmelCase__	:str      =      AdamW(params=ddp_model.parameters(),   lr=1e-3						)
      UpperCAmelCase__	:List[str]      =      LambdaLR(UpperCamelCase_,   lr_lambda=lambda UpperCamelCase_						: epoch**0.65						)
      UpperCAmelCase__	:int      =      LambdaLR(UpperCamelCase_,   lr_lambda=lambda UpperCamelCase_						: epoch**0.65						)
   # Make a copy of `model`
   if sched:
      UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      accelerator.prepare(UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)
   else:
      UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[int]      =      accelerator.prepare(UpperCamelCase_,   UpperCamelCase_						)
   if sched:
      return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
   return model, ddp_model, dataloader


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


def        a__     (					UpperCamelCase_						:      str						):
   # Test on distributed setup that context manager behaves properly
   UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      get_training_setup(UpperCamelCase_						)
   # Use a single batch
   UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      next(iter(UpperCamelCase_						)						).values()
   for iteration in range(3						):
      # Gather the distributed inputs and targs for the base model
      UpperCAmelCase__   ,						UpperCAmelCase__	:Union[str, Any]      =      accelerator.gather((ddp_input, ddp_target)						)
      UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      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__	:List[Any]      =      ddp_input[torch.randperm(len(UpperCamelCase_						)						)]


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


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

   UpperCAmelCase__	:Any      =      RegressionDataset(length=80						)
   UpperCAmelCase__	:Union[str, Any]      =      DataLoader(UpperCamelCase_,   batch_size=16						)
   UpperCAmelCase__	:Tuple      =      RegressionDataset(length=96						)
   UpperCAmelCase__	:Optional[Any]      =      DataLoader(UpperCamelCase_,   batch_size=16						)
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      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        a__     (					):
   UpperCAmelCase__	:str      =      Accelerator()
   UpperCAmelCase__	:Dict      =      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        a__     (					UpperCamelCase_						:      Tuple						):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



