

'''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   (       lowercase_						:    List[Any]     ,					lowercase_						:    Tuple     ,					lowercase_						:    int     ,					lowercase_						:    Optional[Any]	)		->	List[str]:




  '''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 	UpperCamelCase   (       lowercase_						:    int     ,					lowercase_						:    Any     ,					lowercase_						:    Union[str, Any]     ,					lowercase_						:    Optional[int]     ,					lowercase_						:    Union[str, Any]=True	)		->	Optional[Any]:




  '''simple docstring'''



  model.train()
  lowercase									=model(lowercase_	)
  lowercase									=F.mse_loss(lowercase_     ,					target.to(output.device	)	)
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    Dict=False	)		->	int:




  '''simple docstring'''



  set_seed(4_2	)
  lowercase									=RegressionModel()
  lowercase									=deepcopy(lowercase_	)
  lowercase									=RegressionDataset(length=8_0	)
  lowercase									=DataLoader(lowercase_     ,					batch_size=1_6	)
  model.to(accelerator.device	)
  if sched:
    lowercase									=AdamW(params=model.parameters()     ,					lr=1E-3	)
    lowercase									=AdamW(params=ddp_model.parameters()     ,					lr=1E-3	)
    lowercase									=LambdaLR(lowercase_     ,					lr_lambda=lambda lowercase_	: epoch**0.6_5	)
    lowercase									=LambdaLR(lowercase_     ,					lr_lambda=lambda lowercase_	: epoch**0.6_5	)
  # Make a copy of `model`
  if sched:
    lowercase							, lowercase							, lowercase							, lowercase									=accelerator.prepare(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
  else:
    lowercase							, lowercase									=accelerator.prepare(lowercase_     ,					lowercase_	)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader




def 	UpperCamelCase   (       lowercase_						:    int	)		->	List[str]:




  '''simple docstring'''



  lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_	)
  # Use a single batch
  lowercase							, lowercase									=next(iter(lowercase_	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(lowercase_	):
        step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
    else:
      # Sync grads
      step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)

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




def 	UpperCamelCase   (       lowercase_						:    Any	)		->	Optional[Any]:




  '''simple docstring'''



  lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_	)
  # Use a single batch
  lowercase							, lowercase									=next(iter(lowercase_	)	).values()
  for iteration in range(3	):
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
    # Do "gradient accumulation" (noop)
    if iteration % 2 == 0:
      # Accumulate grads locally
      with accelerator.no_sync(lowercase_	):
        step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
    else:
      # Sync grads
      step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)

    # 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	)
    lowercase									=ddp_input[torch.randperm(len(lowercase_	)	)]




def 	UpperCamelCase   (       lowercase_						:    Dict=False     ,					lowercase_						:    Tuple=False	)		->	str:




  '''simple docstring'''



  lowercase									=Accelerator(
      split_batches=lowercase_     ,					dispatch_batches=lowercase_     ,					gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_	)
  for iteration, batch in enumerate(lowercase_	):
    lowercase							, lowercase									=batch.values()
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
    # Do "gradient accumulation" (noop)
    with accelerator.accumulate(lowercase_	):
      step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)

    # 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(lowercase_	) - 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	)
    lowercase									=ddp_input[torch.randperm(len(lowercase_	)	)]
  GradientState._reset_state()




def 	UpperCamelCase   (       lowercase_						:    List[str]=False     ,					lowercase_						:    str=False	)		->	Union[str, Any]:




  '''simple docstring'''



  lowercase									=Accelerator(
      split_batches=lowercase_     ,					dispatch_batches=lowercase_     ,					gradient_accumulation_steps=2	)
  # Test that context manager behaves properly
  lowercase							, lowercase							, lowercase							, lowercase							, lowercase							, lowercase							, lowercase									=get_training_setup(lowercase_     ,					lowercase_	)
  for iteration, batch in enumerate(lowercase_	):
    lowercase							, lowercase									=batch.values()
    # Gather the distributed inputs and targs for the base model
    lowercase							, lowercase									=accelerator.gather((ddp_input, ddp_target)	)
    lowercase							, lowercase									=input.to(accelerator.device	), target.to(accelerator.device	)
    # Perform our initial ground truth step in non "DDP"
    model.train()
    ddp_model.train()
    step_model(lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_	)
    opt.step()

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




def 	UpperCamelCase   (       )		->	Dict:




  '''simple docstring'''



  lowercase									=Accelerator()

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




  '''simple docstring'''



  lowercase									=Accelerator()
  lowercase									=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(lowercase_	)
  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(lowercase_	)
  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(lowercase_     ,					lowercase_	)

    # 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(lowercase_     ,					lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    Dict	)		->	Optional[int]:




  '''simple docstring'''



  main()


if __name__ == "__main__":
     main()





"""simple docstring"""






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case   					=				logging.get_logger(__name__)

snake_case   					=				{'''vocab_file''': '''vocab.txt'''}

snake_case   					=				{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

snake_case   					=				{
    '''openbmb/cpm-ant-10b''': 1_0_2_4,
}




def 			snake_case		(		lowerCAmelCase_	)						->				int:
  _snake_case   	=      collections.OrderedDict()
  with open(lowerCAmelCase_  ,						'''r'''  ,						encoding='''utf-8'''	) as reader:
    _snake_case   	=      reader.readlines()
  for index, token in enumerate(lowerCAmelCase_	):
    _snake_case   	=      token.rstrip('''\n'''	)
    _snake_case   	=      index
  return vocab



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def __init__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	List[str]="<unk>"							,       __lowerCamelCase    :	Tuple=2_0_0				):



     """simple docstring"""
     _snake_case   	=      vocab
     _snake_case   	=      unk_token
     _snake_case   	=      max_input_chars_per_word





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      list(__lowerCamelCase				)
     if len(__lowerCamelCase				) > self.max_input_chars_per_word:
       return [self.unk_token]

     _snake_case   	=      0
     _snake_case   	=      []
     while start < len(__lowerCamelCase				):
       _snake_case   	=      len(__lowerCamelCase				)
       _snake_case   	=      None
       while start < end:
         _snake_case   	=      ''''''.join(chars[start:end]				)
         if substr in self.vocab:
           _snake_case   	=      substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token				)
         start += 1
       else:
         sub_tokens.append(__lowerCamelCase				)
         _snake_case   	=      end

     return sub_tokens



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	List[str]           =  VOCAB_FILES_NAMES
   A__		:	str           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	str           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Union[str, Any]           =  ['''input_ids''', '''attention_mask''']
   A__		:	Optional[int]           =  False



   def __init__(       self    :	Dict							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	str="<d>"							,       __lowerCamelCase    :	Tuple="</d>"							,       __lowerCamelCase    :	Tuple="<s>"							,       __lowerCamelCase    :	int="</s>"							,       __lowerCamelCase    :	List[str]="<pad>"							,       __lowerCamelCase    :	int="<unk>"							,       __lowerCamelCase    :	int="</n>"							,       __lowerCamelCase    :	Tuple="</_>"							,       __lowerCamelCase    :	Optional[Any]="left"							,       **__lowerCamelCase    :	str							,       ):



     """simple docstring"""
     requires_backends(self							,       ['''jieba''']				)
     super().__init__(
         bod_token=__lowerCamelCase							,       eod_token=__lowerCamelCase							,       bos_token=__lowerCamelCase							,       eos_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       unk_token=__lowerCamelCase							,       line_token=__lowerCamelCase							,       space_token=__lowerCamelCase							,       padding_side=__lowerCamelCase							,       **__lowerCamelCase							,       )
     _snake_case   	=      bod_token
     _snake_case   	=      eod_token
     _snake_case   	=      load_vocab(__lowerCamelCase				)
     _snake_case   	=      self.encoder[space_token]
     _snake_case   	=      self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     _snake_case   	=      {v: k for k, v in self.encoder.items()}

     _snake_case   	=      WordpieceTokenizer(vocab=self.encoder							,       unk_token=self.unk_token				)



   @property
   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     return self.encoder[self.bod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return self.encoder[self.eod_token]



   @property
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     return self.encoder["\n"]



   @property
   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     return len(self.encoder				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     return dict(self.encoder							,       **self.added_tokens_encoder				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      []
     for x in jieba.cut(__lowerCamelCase							,       cut_all=__lowerCamelCase				):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCamelCase				)				)
     return output_tokens



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      [i for i in token_ids if i >= 0]
     _snake_case   	=      [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(__lowerCamelCase							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     return token in self.encoder



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return "".join(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.encoder.get(__lowerCamelCase							,       self.encoder.get(self.unk_token				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.decoder.get(__lowerCamelCase							,       self.unk_token				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if os.path.isdir(__lowerCamelCase				):
       _snake_case   	=      os.path.join(
           __lowerCamelCase							,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
     else:
       _snake_case   	=      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
     _snake_case   	=      0
     if " " in self.encoder:
       _snake_case   	=      self.encoder[''' ''']
       del self.encoder[" "]
     if "\n" in self.encoder:
       _snake_case   	=      self.encoder['''\n''']
       del self.encoder["\n"]
     _snake_case   	=      collections.OrderedDict(sorted(self.encoder.items()							,       key=lambda __lowerCamelCase				: x[1]				)				)
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as writer:
       for token, token_index in self.encoder.items():
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               ''' Please check that the vocabulary is not corrupted!'''				)
           _snake_case   	=      token_index
         writer.write(token + '''\n'''				)
         index += 1
     return (vocab_file,)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	List[int] = None				):



     """simple docstring"""
     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None							,       __lowerCamelCase    :	bool = False				):



     """simple docstring"""

     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCamelCase							,       token_ids_a=__lowerCamelCase							,       already_has_special_tokens=__lowerCamelCase				)

     if token_ids_a is not None:
       return [1] + ([0] * len(__lowerCamelCase				)) + [1] + ([0] * len(__lowerCamelCase				))
     return [1] + ([0] * len(__lowerCamelCase				))





