
"""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 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Optional[int]		)     ->					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 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       List[Any]=True		)     ->					Any:
  model.train()
  _snake_case					     =       model(__lowerCamelCase		)
  _snake_case					     =       F.mse_loss(__lowerCamelCase ,					target.to(output.device		)		)
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[str]=False		)     ->					Union[str, Any]:
  set_seed(42		)
  _snake_case					     =       RegressionModel()
  _snake_case					     =       deepcopy(__lowerCamelCase		)
  _snake_case					     =       RegressionDataset(length=80		)
  _snake_case					     =       DataLoader(__lowerCamelCase ,					batch_size=16		)
  model.to(accelerator.device		)
  if sched:
    _snake_case					     =       AdamW(params=model.parameters() ,					lr=1E-3		)
    _snake_case					     =       AdamW(params=ddp_model.parameters() ,					lr=1E-3		)
    _snake_case					     =       LambdaLR(__lowerCamelCase ,					lr_lambda=lambda __lowerCamelCase		: epoch**0.65		)
    _snake_case					     =       LambdaLR(__lowerCamelCase ,					lr_lambda=lambda __lowerCamelCase		: epoch**0.65		)
  # Make a copy of `model`
  if sched:
    _snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case					     =       accelerator.prepare(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
  else:
    _snake_case     ,					_snake_case					     =       accelerator.prepare(__lowerCamelCase ,					__lowerCamelCase		)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any]		)     ->					List[str]:
  # Test when on a single CPU or GPU that the context manager does nothing
  _snake_case     ,					_snake_case     ,					_snake_case					     =       get_training_setup(__lowerCamelCase		)
  # Use a single batch
  _snake_case     ,					_snake_case					     =       next(iter(__lowerCamelCase		)		).values()
  for iteration in range(3		):
    # Gather the distributed inputs and targs for the base model
    _snake_case     ,					_snake_case					     =       accelerator.gather((ddp_input, ddp_target)		)
    _snake_case     ,					_snake_case					     =       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(13_37 + iteration		)
    _snake_case					     =       ddp_input[torch.randperm(len(__lowerCamelCase		)		)]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]		)     ->					List[Any]:
  # Test on distributed setup that context manager behaves properly
  _snake_case     ,					_snake_case     ,					_snake_case					     =       get_training_setup(__lowerCamelCase		)
  # Use a single batch
  _snake_case     ,					_snake_case					     =       next(iter(__lowerCamelCase		)		).values()
  for iteration in range(3		):
    # Gather the distributed inputs and targs for the base model
    _snake_case     ,					_snake_case					     =       accelerator.gather((ddp_input, ddp_target)		)
    _snake_case     ,					_snake_case					     =       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(13_37 + iteration		)
    _snake_case					     =       ddp_input[torch.randperm(len(__lowerCamelCase		)		)]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int=False ,					__lowerCamelCase   :       str=False		)     ->					List[Any]:
  _snake_case					     =       Accelerator(
      split_batches=__lowerCamelCase ,					dispatch_batches=__lowerCamelCase ,					gradient_accumulation_steps=2		)
  # Test that context manager behaves properly
  _snake_case     ,					_snake_case     ,					_snake_case					     =       get_training_setup(__lowerCamelCase		)
  for iteration, batch in enumerate(__lowerCamelCase		):
    _snake_case     ,					_snake_case					     =       batch.values()
    # Gather the distributed inputs and targs for the base model
    _snake_case     ,					_snake_case					     =       accelerator.gather((ddp_input, ddp_target)		)
    _snake_case     ,					_snake_case					     =       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(13_37 + iteration		)
    _snake_case					     =       ddp_input[torch.randperm(len(__lowerCamelCase		)		)]
  GradientState._reset_state()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]=False ,					__lowerCamelCase   :       Optional[int]=False		)     ->					Optional[Any]:
  _snake_case					     =       Accelerator(
      split_batches=__lowerCamelCase ,					dispatch_batches=__lowerCamelCase ,					gradient_accumulation_steps=2		)
  # Test that context manager behaves properly
  _snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case					     =       get_training_setup(__lowerCamelCase ,					__lowerCamelCase		)
  for iteration, batch in enumerate(__lowerCamelCase		):
    _snake_case     ,					_snake_case					     =       batch.values()
    # Gather the distributed inputs and targs for the base model
    _snake_case     ,					_snake_case					     =       accelerator.gather((ddp_input, ddp_target)		)
    _snake_case     ,					_snake_case					     =       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'''
    _snake_case					     =       (((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(13_37 + iteration		)
  GradientState._reset_state()
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       Accelerator()

  _snake_case					     =       RegressionDataset(length=80		)
  _snake_case					     =       DataLoader(__lowerCamelCase ,					batch_size=16		)
  _snake_case					     =       RegressionDataset(length=96		)
  _snake_case					     =       DataLoader(__lowerCamelCase ,					batch_size=16		)
  _snake_case     ,					_snake_case					     =       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 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       Accelerator()
  _snake_case					     =       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 					_UpperCAmelCase      ( __lowerCamelCase   :       Any		)     ->					List[Any]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
 main()





"""simple docstring"""



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCAmelCase__          = ['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase__          = {'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = ' Hello world! cécé herlolip'

UpperCAmelCase__          = [
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					Optional[int]:
  _snake_case					     =       [
      '''encoder.version''',
      '''decoder.version''',
      '''model.encoder.version''',
      '''model.decoder.version''',
      '''_float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCamelCase ,					__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					str:
  _snake_case					     =       torch.load(__lowerCamelCase ,					map_location='''cpu'''		)
  _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					'''bart.large.cnn'''		).eval()
  hub_interface.model.load_state_dict(sd['''model''']		)
  return hub_interface
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int]		)     ->					Union[str, Any]:
  _snake_case     ,					_snake_case					     =       emb.weight.shape
  _snake_case					     =       nn.Linear(__lowerCamelCase ,					__lowerCamelCase ,					bias=__lowerCamelCase		)
  _snake_case					     =       emb.weight.data
  return lin_layer
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any]=None		)     ->					List[Any]:
  if not os.path.exists(__lowerCamelCase		):
    _snake_case					     =       torch.hub.load('''pytorch/fairseq''' ,					__lowerCamelCase		).eval()
  else:
    _snake_case					     =       load_xsum_checkpoint(__lowerCamelCase		)

  bart.model.upgrade_state_dict(bart.model.state_dict()		)
  if hf_checkpoint_name is None:
    _snake_case					     =       checkpoint_path.replace('''.''' ,					'''-'''		)
  _snake_case					     =       BartConfig.from_pretrained(__lowerCamelCase		)
  _snake_case					     =       bart.encode(__lowerCamelCase		).unsqueeze(0		)
  _snake_case					     =       BartTokenizer.from_pretrained(__lowerCamelCase		).encode(__lowerCamelCase ,					return_tensors='''pt'''		).unsqueeze(0		)
  if not torch.eq(__lowerCamelCase ,					__lowerCamelCase		).all():
    raise ValueError(
        f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''		)

  if checkpoint_path == "bart.large.mnli":
    _snake_case					     =       bart.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''model.decoder.embed_tokens.weight''']
    for src, dest in mnli_rename_keys:
      rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       BartForSequenceClassification(__lowerCamelCase		).eval()
    model.load_state_dict(__lowerCamelCase		)
    _snake_case					     =       bart.predict('''mnli''' ,					__lowerCamelCase ,					return_logits=__lowerCamelCase		)
    _snake_case					     =       model(__lowerCamelCase		)[0]  # logits
  else:  # no classification heads to worry about
    _snake_case					     =       bart.model.state_dict()
    remove_ignore_keys_(__lowerCamelCase		)
    _snake_case					     =       state_dict['''decoder.embed_tokens.weight''']
    _snake_case					     =       bart.extract_features(__lowerCamelCase		)
    if hf_checkpoint_name == "facebook/bart-large":
      _snake_case					     =       BartModel(__lowerCamelCase		).eval()
      model.load_state_dict(__lowerCamelCase		)
      _snake_case					     =       model(__lowerCamelCase		).model[0]
    else:
      _snake_case					     =       BartForConditionalGeneration(__lowerCamelCase		).eval()  # an existing summarization ckpt
      model.model.load_state_dict(__lowerCamelCase		)
      if hasattr(__lowerCamelCase ,					'''lm_head'''		):
        _snake_case					     =       make_linear_from_emb(model.model.shared		)
      _snake_case					     =       model.model(__lowerCamelCase		)[0]

    # Check results
  if fairseq_output.shape != new_model_outputs.shape:
    raise ValueError(
        f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''		)
  if (fairseq_output != new_model_outputs).any().item():
    raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''		)
  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  model.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a 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.')
 parser.add_argument(
     '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
 )
 UpperCAmelCase__          = parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)



