





'''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_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """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_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=True	):





      """simple docstring"""



      model.train()
      _snake_case		:	Optional[int]					     =     model(lowerCAmelCase_	)
      _snake_case		:	Any					     =     F.mse_loss(lowerCAmelCase_			,      target.to(output.device	)	)
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(lowerCAmelCase_	)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_=False	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     get_training_setup(lowerCAmelCase_	)
      # Use a single batch
      _snake_case		:	Any					     =     next(iter(lowerCAmelCase_	)	).values()
      for iteration in range(3	):
            # Gather the distributed inputs and targs for the base model
            _snake_case		:	int					     =     accelerator.gather((ddp_input, ddp_target)	)
            _snake_case		:	Dict					     =     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(1_337 + iteration	)
            _snake_case		:	Dict					     =     ddp_input[torch.randperm(len(lowerCAmelCase_	)	)]




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	int					     =     get_training_setup(lowerCAmelCase_	)
      # Use a single batch
      _snake_case		:	int					     =     next(iter(lowerCAmelCase_	)	).values()
      for iteration in range(3	):
            # Gather the distributed inputs and targs for the base model
            _snake_case		:	Optional[int]					     =     accelerator.gather((ddp_input, ddp_target)	)
            _snake_case		:	Dict					     =     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(1_337 + iteration	)
            _snake_case		:	List[Any]					     =     ddp_input[torch.randperm(len(lowerCAmelCase_	)	)]




def    _a    (							lowerCAmelCase_=False			,      lowerCAmelCase_=False	):





      """simple docstring"""



      _snake_case		:	Any					     =     Accelerator(
          split_batches=lowerCAmelCase_			,      dispatch_batches=lowerCAmelCase_			,      gradient_accumulation_steps=2	)
      # Test that context manager behaves properly
      _snake_case		:	Union[str, Any]					     =     get_training_setup(lowerCAmelCase_	)
      for iteration, batch in enumerate(lowerCAmelCase_	):
            _snake_case		:	Union[str, Any]					     =     batch.values()
            # Gather the distributed inputs and targs for the base model
            _snake_case		:	List[Any]					     =     accelerator.gather((ddp_input, ddp_target)	)
            _snake_case		:	Dict					     =     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(1_337 + iteration	)
            _snake_case		:	str					     =     ddp_input[torch.randperm(len(lowerCAmelCase_	)	)]
      GradientState._reset_state()




def    _a    (							lowerCAmelCase_=False			,      lowerCAmelCase_=False	):





      """simple docstring"""



      _snake_case		:	Union[str, Any]					     =     Accelerator(
          split_batches=lowerCAmelCase_			,      dispatch_batches=lowerCAmelCase_			,      gradient_accumulation_steps=2	)
      # Test that context manager behaves properly
      _snake_case		:	int					     =     get_training_setup(lowerCAmelCase_			,      lowerCAmelCase_	)
      for iteration, batch in enumerate(lowerCAmelCase_	):
            _snake_case		:	Dict					     =     batch.values()
            # Gather the distributed inputs and targs for the base model
            _snake_case		:	Any					     =     accelerator.gather((ddp_input, ddp_target)	)
            _snake_case		:	List[Any]					     =     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		:	Optional[Any]					     =     (((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(1_337 + iteration	)
      GradientState._reset_state()




def    _a    (							):





      """simple docstring"""



      _snake_case		:	int					     =     Accelerator()

      _snake_case		:	Optional[int]					     =     RegressionDataset(length=80	)
      _snake_case		:	Tuple					     =     DataLoader(lowerCAmelCase_			,      batch_size=16	)
      _snake_case		:	Optional[int]					     =     RegressionDataset(length=96	)
      _snake_case		:	Tuple					     =     DataLoader(lowerCAmelCase_			,      batch_size=16	)
      _snake_case		:	Optional[int]					     =     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    _a    (							):





      """simple docstring"""



      _snake_case		:	Dict					     =     Accelerator()
      _snake_case		:	Optional[Any]					     =     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    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      main()


if __name__ == "__main__":
   main()









'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase					:		Optional[int]		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[Any]		=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase					:		Any		=							{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase					:		Optional[Any]		=							{
    'gpt-neox-20b': 2_0_4_8,
}





class lowerCamelCase						(a__					):
  _lowercase  :	Optional[int]       					=				VOCAB_FILES_NAMES
  _lowercase  :	str       					=				PRETRAINED_VOCAB_FILES_MAP
  _lowercase  :	Dict       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase  :	Optional[int]       					=				["""input_ids""", """attention_mask"""]







  def __init__(		self     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__=None     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__="<|endoftext|>"     ,     lowercase__=False     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        super().__init__(
            lowercase__     ,     lowercase__     ,     tokenizer_file=lowercase__     ,     unk_token=lowercase__     ,     bos_token=lowercase__     ,     eos_token=lowercase__     ,     add_prefix_space=lowercase__     ,     **lowercase__     ,     )

        _snake_case		:	Optional[int]					     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get('''add_prefix_space'''     ,     lowercase__		) != add_prefix_space:
              _snake_case		:	int					     =     getattr(lowercase__     ,     pre_tok_state.pop('''type'''		)		)
              _snake_case		:	int					     =     add_prefix_space
              _snake_case		:	Optional[Any]					     =     pre_tok_class(**lowercase__		)

        _snake_case		:	List[str]					     =     add_prefix_space







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[str]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self._tokenizer.model.save(lowercase__     ,     name=lowercase__		)
        return tuple(lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__		)		->						List[int]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase__     ,     add_special_tokens=lowercase__		) + [self.eos_token_id]		)

        if len(lowercase__		) > self.model_max_length:
              _snake_case		:	Dict					     =     input_ids[-self.model_max_length :]
        return input_ids


