



"""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      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):






   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      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase=True ):
   model.train()
   SCREAMING_SNAKE_CASE_: str     			=model(__UpperCamelCase )
   SCREAMING_SNAKE_CASE_: Tuple     			=F.mse_loss(__UpperCamelCase							,			target.to(output.device ) )
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(__UpperCamelCase )





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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=get_training_setup(__UpperCamelCase )
   # Use a single batch
   SCREAMING_SNAKE_CASE_: List[Any]     			=next(iter(__UpperCamelCase ) ).values()
   for iteration in range(3 ):
      # Gather the distributed inputs and targs for the base model
      SCREAMING_SNAKE_CASE_: List[Any]     			=accelerator.gather((ddp_input, ddp_target) )
      SCREAMING_SNAKE_CASE_: 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(1337 + iteration )
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ddp_input[torch.randperm(len(__UpperCamelCase ) )]





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=get_training_setup(__UpperCamelCase )
   # Use a single batch
   SCREAMING_SNAKE_CASE_: str     			=next(iter(__UpperCamelCase ) ).values()
   for iteration in range(3 ):
      # Gather the distributed inputs and targs for the base model
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=accelerator.gather((ddp_input, ddp_target) )
      SCREAMING_SNAKE_CASE_: Optional[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(1337 + iteration )
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ddp_input[torch.randperm(len(__UpperCamelCase ) )]





def      __magic_name__				(							lowercase=False							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=Accelerator(
       split_batches=__UpperCamelCase							,			dispatch_batches=__UpperCamelCase							,			gradient_accumulation_steps=2 )
   # Test that context manager behaves properly
   SCREAMING_SNAKE_CASE_: List[str]     			=get_training_setup(__UpperCamelCase )
   for iteration, batch in enumerate(__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_: int     			=batch.values()
      # Gather the distributed inputs and targs for the base model
      SCREAMING_SNAKE_CASE_: Any     			=accelerator.gather((ddp_input, ddp_target) )
      SCREAMING_SNAKE_CASE_: int     			=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(1337 + iteration )
      SCREAMING_SNAKE_CASE_: str     			=ddp_input[torch.randperm(len(__UpperCamelCase ) )]
   GradientState._reset_state()





def      __magic_name__				(							lowercase=False							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: Dict     			=Accelerator(
       split_batches=__UpperCamelCase							,			dispatch_batches=__UpperCamelCase							,			gradient_accumulation_steps=2 )
   # Test that context manager behaves properly
   SCREAMING_SNAKE_CASE_: List[str]     			=get_training_setup(__UpperCamelCase							,			__UpperCamelCase )
   for iteration, batch in enumerate(__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_: List[str]     			=batch.values()
      # Gather the distributed inputs and targs for the base model
      SCREAMING_SNAKE_CASE_: str     			=accelerator.gather((ddp_input, ddp_target) )
      SCREAMING_SNAKE_CASE_: 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(__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'''
      SCREAMING_SNAKE_CASE_: Tuple     			=(((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(1337 + iteration )
   GradientState._reset_state()





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: int     			=Accelerator()

   SCREAMING_SNAKE_CASE_: Tuple     			=RegressionDataset(length=80 )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(__UpperCamelCase							,			batch_size=16 )
   SCREAMING_SNAKE_CASE_: Any     			=RegressionDataset(length=96 )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=DataLoader(__UpperCamelCase							,			batch_size=16 )
   SCREAMING_SNAKE_CASE_: 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      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Any     			=Accelerator()
   SCREAMING_SNAKE_CASE_: Optional[int]     			=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      __magic_name__				(							lowercase ):
   main()


if __name__ == "__main__":
   main()








"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_UpperCAmelCase     =			{
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					'albert'



  def __init__(						self :					Dict       ,		lowerCAmelCase :					List[str]=3_0000       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					List[str]=4096       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=1       ,		lowerCAmelCase :					Tuple=64       ,		lowerCAmelCase :					Dict=1_6384       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					str="gelu_new"       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Optional[Any]=0       ,		lowerCAmelCase :					str=512       ,		lowerCAmelCase :					Optional[int]=2       ,		lowerCAmelCase :					List[Any]=0.0_2       ,		lowerCAmelCase :					Union[str, Any]=1E-12       ,		lowerCAmelCase :					Tuple=0.1       ,		lowerCAmelCase :					List[Any]="absolute"       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					Optional[int]=3       ,		**lowerCAmelCase :					int       ,		)     ->					Tuple:


     '''simple docstring'''




     super().__init__(pad_token_id=lowerCAmelCase       ,		bos_token_id=lowerCAmelCase       ,		eos_token_id=lowerCAmelCase       ,		**lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=vocab_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=embedding_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=inner_group_num
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_act
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Any     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=max_position_embeddings
     SCREAMING_SNAKE_CASE_: Any     			=type_vocab_size
     SCREAMING_SNAKE_CASE_: int     			=initializer_range
     SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Dict     			=classifier_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=position_embedding_type







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




     if self.task == "multiple-choice":
        SCREAMING_SNAKE_CASE_: str     			={0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
        SCREAMING_SNAKE_CASE_: Dict     			={0: """batch""", 1: """sequence"""}
     return OrderedDict(
         [
             ("""input_ids""", dynamic_axis),
             ("""attention_mask""", dynamic_axis),
             ("""token_type_ids""", dynamic_axis),
         ]		)



