'''simple docstring'''





import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


SCREAMING_SNAKE_CASE_:     str  =logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE_:     Optional[Any]  ='PoolFormerConfig'

# Base docstring
SCREAMING_SNAKE_CASE_:     Optional[int]  ='sail/poolformer_s12'
SCREAMING_SNAKE_CASE_:     Union[str, Any]  =[1, 5_12, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE_:     Dict  ='sail/poolformer_s12'
SCREAMING_SNAKE_CASE_:     Optional[int]  ='tabby, tabby cat'

SCREAMING_SNAKE_CASE_:     Union[str, Any]  =[
    'sail/poolformer_s12',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]





def 	lowerCAmelCase_				(					snake_case_       : List[str]					,    snake_case_       : float = 0.0					,    snake_case_       : bool = False			)    ->      Optional[int]:


     '''simple docstring'''




     if drop_prob == 0.0 or not training:
          return input
     UpperCAmelCase_           =   1 - drop_prob
     UpperCAmelCase_           =   (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
     UpperCAmelCase_           =   keep_prob + torch.rand(snake_case_					,    dtype=input.dtype					,    device=input.device			)
     random_tensor.floor_()  # binarize
     UpperCAmelCase_           =   input.div(snake_case_			) * random_tensor
     return output




class        __A      ( nn.Module	):



    def __init__(self    :    List[str]   ,			__a    :    Optional[float] = None ):
         super().__init__()
         UpperCAmelCase_           =   drop_prob



    def 					_lowercase						(self    :    List[str]   ,			__a    :    torch.Tensor ):
         return drop_path(__a   ,			self.drop_prob   ,			self.training )






    def 					_lowercase						(self    :    Tuple ):
         return "p={}".format(self.drop_prob )




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    Union[str, Any]   ,			__a    :    str   ,			__a    :    List[Any]   ,			__a    :    int   ,			__a    :    List[str]   ,			__a    :    Optional[Any]=None ):
         super().__init__()
         UpperCAmelCase_           =   patch_size if isinstance(__a   ,			collections.abc.Iterable ) else (patch_size, patch_size)
         UpperCAmelCase_           =   stride if isinstance(__a   ,			collections.abc.Iterable ) else (stride, stride)
         UpperCAmelCase_           =   padding if isinstance(__a   ,			collections.abc.Iterable ) else (padding, padding)

         UpperCAmelCase_           =   nn.Convad(__a   ,			__a   ,			kernel_size=__a   ,			stride=__a   ,			padding=__a )
         UpperCAmelCase_           =   norm_layer(__a ) if norm_layer else nn.Identity()






    def 					_lowercase						(self    :    List[str]   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.projection(__a )
         UpperCAmelCase_           =   self.norm(__a )
         return embeddings




class        __A      ( nn.GroupNorm	):



    def __init__(self    :    Optional[Any]   ,			__a    :    Any   ,			**__a    :    Tuple ):
         super().__init__(1   ,			__a   ,			**__a )




class        __A      ( nn.Module	):



    def __init__(self    :    List[str]   ,			__a    :    Any ):
         super().__init__()
         UpperCAmelCase_           =   nn.AvgPoolad(__a   ,			stride=1   ,			padding=pool_size // 2   ,			count_include_pad=__a )






    def 					_lowercase						(self    :    str   ,			__a    :    int ):
         return self.pool(__a ) - hidden_states




class        __A      ( nn.Module	):



    def __init__(self    :    str   ,			__a    :    Union[str, Any]   ,			__a    :    Dict   ,			__a    :    int   ,			__a    :    Optional[Any] ):
         super().__init__()
         UpperCAmelCase_           =   nn.Convad(__a   ,			__a   ,			1 )
         UpperCAmelCase_           =   nn.Convad(__a   ,			__a   ,			1 )
         UpperCAmelCase_           =   PoolFormerDropPath(__a )
         if isinstance(config.hidden_act   ,			__a ):
              UpperCAmelCase_           =   ACTaFN[config.hidden_act]
         else:
              UpperCAmelCase_           =   config.hidden_act






    def 					_lowercase						(self    :    Dict   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.conva(__a )
         UpperCAmelCase_           =   self.act_fn(__a )
         UpperCAmelCase_           =   self.drop(__a )
         UpperCAmelCase_           =   self.conva(__a )
         UpperCAmelCase_           =   self.drop(__a )

         return hidden_states




class        __A      ( nn.Module	):



    def __init__(self    :    Dict   ,			__a    :    Tuple   ,			__a    :    Optional[Any]   ,			__a    :    int   ,			__a    :    str   ,			__a    :    Union[str, Any]   ,			__a    :    int ):
         super().__init__()
         UpperCAmelCase_           =   PoolFormerPooling(__a )
         UpperCAmelCase_           =   PoolFormerOutput(__a   ,			__a   ,			__a   ,			__a )
         UpperCAmelCase_           =   PoolFormerGroupNorm(__a )
         UpperCAmelCase_           =   PoolFormerGroupNorm(__a )

         # Useful for training neural nets
         UpperCAmelCase_           =   PoolFormerDropPath(__a ) if drop_path > 0.0 else nn.Identity()
         UpperCAmelCase_           =   config.use_layer_scale
         if config.use_layer_scale:
              UpperCAmelCase_           =   nn.Parameter(
                  config.layer_scale_init_value * torch.ones((__a) )   ,			requires_grad=__a )
              UpperCAmelCase_           =   nn.Parameter(
                  config.layer_scale_init_value * torch.ones((__a) )   ,			requires_grad=__a )






    def 					_lowercase						(self    :    Any   ,			__a    :    Union[str, Any] ):
         if self.use_layer_scale:
              UpperCAmelCase_           =   self.pooling(self.before_norm(__a ) )
              UpperCAmelCase_           =   self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
              # First residual connection
              UpperCAmelCase_           =   hidden_states + self.drop_path(__a )
              UpperCAmelCase_           =   ()

              UpperCAmelCase_           =   self.output(self.after_norm(__a ) )
              UpperCAmelCase_           =   self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
              # Second residual connection
              UpperCAmelCase_           =   hidden_states + self.drop_path(__a )

              UpperCAmelCase_           =   (output,) + outputs
              return outputs

         else:
              UpperCAmelCase_           =   self.drop_path(self.pooling(self.before_norm(__a ) ) )
              # First residual connection
              UpperCAmelCase_           =   pooling_output + hidden_states
              UpperCAmelCase_           =   ()

              # Second residual connection inside the PoolFormerOutput block
              UpperCAmelCase_           =   self.drop_path(self.output(self.after_norm(__a ) ) )
              UpperCAmelCase_           =   hidden_states + layer_output

              UpperCAmelCase_           =   (output,) + outputs
              return outputs




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    Tuple ):
         super().__init__()
         UpperCAmelCase_           =   config
         # stochastic depth decay rule
         UpperCAmelCase_           =   [x.item() for x in torch.linspace(0   ,			config.drop_path_rate   ,			sum(config.depths ) )]

         # patch embeddings
         UpperCAmelCase_           =   []
         for i in range(config.num_encoder_blocks ):
              embeddings.append(
                  PoolFormerEmbeddings(
                      patch_size=config.patch_sizes[i]   ,			stride=config.strides[i]   ,			padding=config.padding[i]   ,			num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]   ,			hidden_size=config.hidden_sizes[i]   ,			) )
         UpperCAmelCase_           =   nn.ModuleList(__a )

         # Transformer blocks
         UpperCAmelCase_           =   []
         UpperCAmelCase_           =   0
         for i in range(config.num_encoder_blocks ):
              # each block consists of layers
              UpperCAmelCase_           =   []
              if i != 0:
                   cur += config.depths[i - 1]
              for j in range(config.depths[i] ):
                   layers.append(
                       PoolFormerLayer(
                           __a   ,			num_channels=config.hidden_sizes[i]   ,			pool_size=config.pool_size   ,			hidden_size=config.hidden_sizes[i]   ,			intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio )   ,			drop_path=dpr[cur + j]   ,			) )
              blocks.append(nn.ModuleList(__a ) )

         UpperCAmelCase_           =   nn.ModuleList(__a )






    def 					_lowercase						(self    :    Any   ,			__a    :    int   ,			__a    :    str=False   ,			__a    :    Optional[Any]=True ):
         UpperCAmelCase_           =   () if output_hidden_states else None

         UpperCAmelCase_           =   pixel_values
         for idx, layers in enumerate(zip(self.patch_embeddings   ,			self.block ) ):
              UpperCAmelCase_    , UpperCAmelCase_           =   layers
              # Get patch embeddings from hidden_states
              UpperCAmelCase_           =   embedding_layer(__a )
              # Send the embeddings through the blocks
              for _, blk in enumerate(__a ):
                   UpperCAmelCase_           =   blk(__a )
                   UpperCAmelCase_           =   layer_outputs[0]

              if output_hidden_states:
                   UpperCAmelCase_           =   all_hidden_states + (hidden_states,)

         if not return_dict:
              return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )

         return BaseModelOutputWithNoAttention(last_hidden_state=__a   ,			hidden_states=__a )




class        __A      ( UpperCamelCase__	):
    a__       :						List[Any]          =  PoolFormerConfig
    a__       :						Tuple          =  """poolformer"""
    a__       :						List[str]          =  """pixel_values"""
    a__       :						Dict          =  True



    def 					_lowercase						(self    :    Tuple   ,			__a    :    Optional[Any] ):
         if isinstance(__a   ,			(nn.Linear, nn.Convad) ):
              module.weight.data.normal_(mean=0.0   ,			std=self.config.initializer_range )
              if module.bias is not None:
                   module.bias.data.zero_()
         elif isinstance(__a   ,			nn.LayerNorm ):
              module.bias.data.zero_()
              module.weight.data.fill_(1.0 )






    def 					_lowercase						(self    :    str   ,			__a    :    Dict   ,			__a    :    Dict=False ):
         if isinstance(__a   ,			__a ):
              UpperCAmelCase_           =   value




SCREAMING_SNAKE_CASE_:     List[str]  =r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

SCREAMING_SNAKE_CASE_:     List[Any]  =r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n'




@add_start_docstrings(
    """The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."""		,	UpperCamelCase__		,	)
class        __A      ( UpperCamelCase__	):



    def __init__(self    :    List[str]   ,			__a    :    str ):
         super().__init__(__a )
         UpperCAmelCase_           =   config

         UpperCAmelCase_           =   PoolFormerEncoder(__a )

         # Initialize weights and apply final processing
         self.post_init()



    def 					_lowercase						(self    :    List[str] ):
         return self.embeddings.patch_embeddings






    @add_start_docstrings_to_model_forward(__a )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC   ,			output_type=__a   ,			config_class=_CONFIG_FOR_DOC   ,			modality="vision"   ,			expected_output=_EXPECTED_OUTPUT_SHAPE   ,			)
    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Optional[torch.FloatTensor] = None   ,			__a    :    Optional[bool] = None   ,			__a    :    Optional[bool] = None   ,			):
         UpperCAmelCase_           =   (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         UpperCAmelCase_           =   return_dict if return_dict is not None else self.config.use_return_dict

         if pixel_values is None:
              raise ValueError("You have to specify pixel_values" )

         UpperCAmelCase_           =   self.encoder(
             __a   ,			output_hidden_states=__a   ,			return_dict=__a   ,			)
         UpperCAmelCase_           =   encoder_outputs[0]

         if not return_dict:
              return (sequence_output, None) + encoder_outputs[1:]

         return BaseModelOutputWithNoAttention(
             last_hidden_state=__a   ,			hidden_states=encoder_outputs.hidden_states   ,			)




class        __A      ( nn.Module	):



    def __init__(self    :    Any   ,			__a    :    str ):
         super().__init__()
         UpperCAmelCase_           =   nn.Linear(config.hidden_size   ,			config.hidden_size )






    def 					_lowercase						(self    :    List[str]   ,			__a    :    Tuple ):
         UpperCAmelCase_           =   self.dense(__a )
         return output




@add_start_docstrings(
    """
    PoolFormer Model transformer with an image classification head on top
    """		,	UpperCamelCase__		,	)
class        __A      ( UpperCamelCase__	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    List[str] ):
         super().__init__(__a )
         UpperCAmelCase_           =   config.num_labels
         UpperCAmelCase_           =   PoolFormerModel(__a )

         # Final norm
         UpperCAmelCase_           =   PoolFormerGroupNorm(config.hidden_sizes[-1] )
         # Classifier head
         UpperCAmelCase_           =   (
             nn.Linear(config.hidden_sizes[-1]   ,			config.num_labels ) if config.num_labels > 0 else nn.Identity()
         )

         # Initialize weights and apply final processing
         self.post_init()






    @add_start_docstrings_to_model_forward(__a )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT   ,			output_type=__a   ,			config_class=_CONFIG_FOR_DOC   ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,			)
    def 					_lowercase						(self    :    List[Any]   ,			__a    :    Optional[torch.FloatTensor] = None   ,			__a    :    Optional[torch.LongTensor] = None   ,			__a    :    Optional[bool] = None   ,			__a    :    Optional[bool] = None   ,			):
         UpperCAmelCase_           =   return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase_           =   self.poolformer(
             __a   ,			output_hidden_states=__a   ,			return_dict=__a   ,			)

         UpperCAmelCase_           =   outputs[0]

         UpperCAmelCase_           =   self.classifier(self.norm(__a ).mean([-2, -1] ) )

         UpperCAmelCase_           =   None
         if labels is not None:
              if self.config.problem_type is None:
                   if self.num_labels == 1:
                        UpperCAmelCase_           =   "regression"
                   elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                        UpperCAmelCase_           =   "single_label_classification"
                   else:
                        UpperCAmelCase_           =   "multi_label_classification"

              if self.config.problem_type == "regression":
                   UpperCAmelCase_           =   MSELoss()
                   if self.num_labels == 1:
                        UpperCAmelCase_           =   loss_fct(logits.squeeze()   ,			labels.squeeze() )
                   else:
                        UpperCAmelCase_           =   loss_fct(__a   ,			__a )
              elif self.config.problem_type == "single_label_classification":
                   UpperCAmelCase_           =   CrossEntropyLoss()
                   UpperCAmelCase_           =   loss_fct(logits.view(-1   ,			self.num_labels )   ,			labels.view(-1 ) )
              elif self.config.problem_type == "multi_label_classification":
                   UpperCAmelCase_           =   BCEWithLogitsLoss()
                   UpperCAmelCase_           =   loss_fct(__a   ,			__a )

         if not return_dict:
              UpperCAmelCase_           =   (logits,) + outputs[2:]
              return ((loss,) + output) if loss is not None else output

         return ImageClassifierOutputWithNoAttention(loss=__a   ,			logits=__a   ,			hidden_states=outputs.hidden_states )





'''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 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Union[str, Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : List[Any]			)    ->      List[Any]:


     '''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 	lowerCAmelCase_				(					snake_case_       : Any					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : List[Any]					,    snake_case_       : str=True			)    ->      Optional[Any]:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Any=False			)    ->      Dict:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      int:


     '''simple docstring'''




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

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





def 	lowerCAmelCase_				(					snake_case_       : Tuple			)    ->      str:


     '''simple docstring'''




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

          # 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			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]=False					,    snake_case_       : str=False			)    ->      List[str]:


     '''simple docstring'''




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

          # 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(snake_case_			) - 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			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]
     GradientState._reset_state()





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]=False					,    snake_case_       : Tuple=False			)    ->      Union[str, Any]:


     '''simple docstring'''




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

          if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case_			)):
               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(snake_case_			):
               step_model(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
               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"""
          UpperCAmelCase_           =   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case_			))
          if accelerator.num_processes > 1:
               check_model_parameters(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
          # Shuffle ddp_input on each iteration
          torch.manual_seed(13_37 + iteration			)
     GradientState._reset_state()





def 	lowerCAmelCase_				(					)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   Accelerator()

     UpperCAmelCase_           =   RegressionDataset(length=80			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     UpperCAmelCase_           =   RegressionDataset(length=96			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     UpperCAmelCase_    , UpperCAmelCase_           =   accelerator.prepare(snake_case_					,    snake_case_			)
     assert accelerator.gradient_state.active_dataloader is None
     for iteration, _ in enumerate(snake_case_			):
          assert id(accelerator.gradient_state.active_dataloader			) == id(snake_case_			)
          if iteration < len(snake_case_			) - 1:
               assert not accelerator.gradient_state.end_of_dataloader
               if iteration == 1:
                    for batch_num, _ in enumerate(snake_case_			):
                         assert id(accelerator.gradient_state.active_dataloader			) == id(snake_case_			)
                         if batch_num < len(snake_case_			) - 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 	lowerCAmelCase_				(					)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   Accelerator()
     UpperCAmelCase_           =   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(snake_case_			)
     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(snake_case_			)
     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(snake_case_					,    snake_case_			)

    # 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(snake_case_					,    snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : Dict			)    ->      int:


     '''simple docstring'''




     main()


if __name__ == "__main__":
    main()




