


"""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_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Dict       ):






     """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       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					int,     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Optional[int],     _lowerCAmelCase     :					str=True       ):






     """simple docstring"""

     model.train()
     _a    	=				model(__UpperCamelCase       )
     _a    	=				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_   (   _lowerCAmelCase     :					Any,     _lowerCAmelCase     :					str=False       ):






     """simple docstring"""

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






     """simple docstring"""

     _a    	=				get_training_setup(__UpperCamelCase       )
     # Use a single batch
     _a    	=				next(iter(__UpperCamelCase       )       ).values()
     for iteration in range(3       ):
          # Gather the distributed inputs and targs for the base model
          _a    	=				accelerator.gather((ddp_input, ddp_target)       )
          _a    	=				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(13_37 + iteration       )
          _a    	=				ddp_input[torch.randperm(len(__UpperCamelCase       )       )]
def       A_   (   _lowerCAmelCase     :					List[Any]       ):






     """simple docstring"""

     _a    	=				get_training_setup(__UpperCamelCase       )
     # Use a single batch
     _a    	=				next(iter(__UpperCamelCase       )       ).values()
     for iteration in range(3       ):
          # Gather the distributed inputs and targs for the base model
          _a    	=				accelerator.gather((ddp_input, ddp_target)       )
          _a    	=				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(13_37 + iteration       )
          _a    	=				ddp_input[torch.randperm(len(__UpperCamelCase       )       )]
def       A_   (   _lowerCAmelCase     :					Optional[int]=False,     _lowerCAmelCase     :					Optional[Any]=False       ):






     """simple docstring"""

     _a    	=				Accelerator(
         split_batches=__UpperCamelCase,     dispatch_batches=__UpperCamelCase,     gradient_accumulation_steps=2       )
     # Test that context manager behaves properly
     _a    	=				get_training_setup(__UpperCamelCase       )
     for iteration, batch in enumerate(__UpperCamelCase       ):
          _a    	=				batch.values()
          # Gather the distributed inputs and targs for the base model
          _a    	=				accelerator.gather((ddp_input, ddp_target)       )
          _a    	=				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(13_37 + iteration       )
          _a    	=				ddp_input[torch.randperm(len(__UpperCamelCase       )       )]
     GradientState._reset_state()
def       A_   (   _lowerCAmelCase     :					List[Any]=False,     _lowerCAmelCase     :					str=False       ):






     """simple docstring"""

     _a    	=				Accelerator(
         split_batches=__UpperCamelCase,     dispatch_batches=__UpperCamelCase,     gradient_accumulation_steps=2       )
     # Test that context manager behaves properly
     _a    	=				get_training_setup(__UpperCamelCase,     __UpperCamelCase       )
     for iteration, batch in enumerate(__UpperCamelCase       ):
          _a    	=				batch.values()
          # Gather the distributed inputs and targs for the base model
          _a    	=				accelerator.gather((ddp_input, ddp_target)       )
          _a    	=				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'
          _a    	=				(((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(13_37 + iteration       )
     GradientState._reset_state()
def       A_   (   ):






     """simple docstring"""

     _a    	=				Accelerator()

     _a    	=				RegressionDataset(length=80       )
     _a    	=				DataLoader(__UpperCamelCase,     batch_size=16       )
     _a    	=				RegressionDataset(length=96       )
     _a    	=				DataLoader(__UpperCamelCase,     batch_size=16       )
     _a    	=				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_   (   ):






     """simple docstring"""

     _a    	=				Accelerator()
     _a    	=				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_   (   _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     main()


if __name__ == "__main__":
   main()



"""simple docstring"""






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		['input_ids', 'attention_mask']


     def __init__(      self    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase=125    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase    ,				)       ->							None:
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
               _a    	=				[F'<extra_id_{i}>' for i in range(__UpperCAmelCase							)]
          elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra_id special tokens
               _a    	=				len(set(filter(lambda __UpperCAmelCase							: bool('''extra_id''' in str(__UpperCAmelCase							)							)    ,				__UpperCAmelCase							)							)							)
               if extra_tokens != extra_ids:
                    raise ValueError(
                        F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                        ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
                        ''' extra_ids tokens'''							)

          _a    	=				AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							) else pad_token
          _a    	=				AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							) else eos_token
          _a    	=				AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase							) if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							) else unk_token

          super().__init__(
              eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				extra_ids=__UpperCAmelCase    ,				additional_special_tokens=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

          _a    	=				extra_ids

          _a    	=				2**8  # utf is 8 bits

          # define special tokens dict
          _a    	=				{
              self.pad_token: 0,
              self.eos_token: 1,
              self.unk_token: 2,
          }
          _a    	=				len(self.special_tokens_encoder							)
          _a    	=				len(__UpperCAmelCase							)
          for i, token in enumerate(__UpperCAmelCase							):
               _a    	=				self.vocab_size + i - n
          _a    	=				{v: k for k, v in self.special_tokens_encoder.items()}


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          return self._utf_vocab_size + self._num_special_tokens + self._extra_ids


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase							)

          # normal case: some special tokens
          if token_ids_a is None:
               return ([0] * len(__UpperCAmelCase							)) + [1]
          return ([0] * len(__UpperCAmelCase							)) + [1] + ([0] * len(__UpperCAmelCase							)) + [1]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[int]:
          if len(__UpperCAmelCase							) > 0 and token_ids[-1] == self.eos_token_id:
               warnings.warn(
                   F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                   ''' eos tokens being added.'''							)
               return token_ids
          else:
               return token_ids + [self.eos_token_id]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.eos_token_id]

          if token_ids_a is None:
               return len(token_ids_a + eos							) * [0]
          return len(token_ids_a + eos + token_ids_a + eos							) * [0]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				self._add_eos_if_not_present(__UpperCAmelCase							)
          if token_ids_a is None:
               return token_ids_a
          else:
               _a    	=				self._add_eos_if_not_present(__UpperCAmelCase							)
               return token_ids_a + token_ids_a


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				[chr(__UpperCAmelCase							) for i in text.encode('''utf-8'''							)]
          return tokens


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Union[str, Any]:
          if token in self.special_tokens_encoder:
               _a    	=				self.special_tokens_encoder[token]
          elif token in self.added_tokens_encoder:
               _a    	=				self.added_tokens_encoder[token]
          elif len(__UpperCAmelCase							) != 1:
               _a    	=				self.unk_token_id
          else:
               _a    	=				ord(__UpperCAmelCase							) + self._num_special_tokens
          return token_id


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[Any]:
          if index in self.special_tokens_decoder:
               _a    	=				self.special_tokens_decoder[index]
          else:
               _a    	=				chr(index - self._num_special_tokens							)
          return token


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          _a    	=				B''''''
          for token in tokens:
               if token in self.special_tokens_decoder:
                    _a    	=				self.special_tokens_decoder[token].encode('''utf-8'''							)
               elif token in self.added_tokens_decoder:
                    _a    	=				self.special_tokens_decoder[token].encode('''utf-8'''							)
               elif token in self.special_tokens_encoder:
                    _a    	=				token.encode('''utf-8'''							)
               elif token in self.added_tokens_encoder:
                    _a    	=				token.encode('''utf-8'''							)
               else:
                    _a    	=				bytes([ord(__UpperCAmelCase							)]							)
               bstring += tok_string
          _a    	=				bstring.decode('''utf-8'''    ,				errors='''ignore'''							)
          return string





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          return ()