






"""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


_snake_case          =   logging.get_logger(__name__)

# General docstring
_snake_case          =   'PoolFormerConfig'

# Base docstring
_snake_case          =   'sail/poolformer_s12'
_snake_case          =   [1, 512, 7, 7]

# Image classification docstring
_snake_case          =   'sail/poolformer_s12'
_snake_case          =   'tabby, tabby cat'

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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__ = 0.0     ,   UpperCamelCase__ = False				):



  '''simple docstring'''
  if drop_prob == 0.0 or not training:
    return input
  _a  :       List[Any]             =	1 - drop_prob
  _a  :       Dict             =	(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
  _a  :       Dict             =	keep_prob + torch.rand(UpperCamelCase__     ,   dtype=input.dtype     ,   device=input.device				)
  random_tensor.floor_()  # binarize
  _a  :       List[str]             =	input.div(UpperCamelCase__				) * random_tensor
  return output






class        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						int							,						UpperCAmelCase__		:						Optional[float] = None							)					->       Union[str, Any]:
   super().__init__()
   _a  :       Union[str, Any]             =	drop_prob


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







 def _lowercase	(     self		:						Union[str, Any]							)					->       int:
   return "p={}".format(self.drop_prob							)







class        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						Any							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						List[Any]=None							)					->       int:
   super().__init__()
   _a  :       Dict             =	patch_size if isinstance(__a							,						collections.abc.Iterable							) else (patch_size, patch_size)
   _a  :       int             =	stride if isinstance(__a							,						collections.abc.Iterable							) else (stride, stride)
   _a  :       str             =	padding if isinstance(__a							,						collections.abc.Iterable							) else (padding, padding)

   _a  :       int             =	nn.Convad(__a							,						__a							,						kernel_size=__a							,						stride=__a							,						padding=__a							)
   _a  :       Any             =	norm_layer(__a							) if norm_layer else nn.Identity()







 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Optional[Any]							)					->       int:
   _a  :       Union[str, Any]             =	self.projection(__a							)
   _a  :       str             =	self.norm(__a							)
   return embeddings







class        UpperCamelCase			(	nn.GroupNorm      ):


 def __init__(     self		:						Tuple							,						UpperCAmelCase__		:						Dict							,						**UpperCAmelCase__		:						Union[str, Any]							)					->       Optional[int]:
   super().__init__(1							,						__a							,						**__a							)







class        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						int							)					->       Dict:
   super().__init__()
   _a  :       List[Any]             =	nn.AvgPoolad(__a							,						stride=1							,						padding=pool_size // 2							,						count_include_pad=__a							)







 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						List[Any]							)					->       int:
   return self.pool(__a							) - hidden_states







class        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						int							,						UpperCAmelCase__		:						Union[str, Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						str							)					->       int:
   super().__init__()
   _a  :       Tuple             =	nn.Convad(__a							,						__a							,						1							)
   _a  :       Union[str, Any]             =	nn.Convad(__a							,						__a							,						1							)
   _a  :       Any             =	PoolFormerDropPath(__a							)
   if isinstance(config.hidden_act							,						__a							):
     _a  :       Union[str, Any]             =	ACTaFN[config.hidden_act]
   else:
     _a  :       List[str]             =	config.hidden_act







 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						str							)					->       str:
   _a  :       Dict             =	self.conva(__a							)
   _a  :       Tuple             =	self.act_fn(__a							)
   _a  :       List[str]             =	self.drop(__a							)
   _a  :       List[str]             =	self.conva(__a							)
   _a  :       int             =	self.drop(__a							)

   return hidden_states







class        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						int							)					->       Tuple:
   super().__init__()
   _a  :       List[Any]             =	PoolFormerPooling(__a							)
   _a  :       str             =	PoolFormerOutput(__a							,						__a							,						__a							,						__a							)
   _a  :       Union[str, Any]             =	PoolFormerGroupNorm(__a							)
   _a  :       Tuple             =	PoolFormerGroupNorm(__a							)

   # Useful for training neural nets
   _a  :       List[str]             =	PoolFormerDropPath(__a							) if drop_path > 0.0 else nn.Identity()
   _a  :       Optional[int]             =	config.use_layer_scale
   if config.use_layer_scale:
     _a  :       int             =	nn.Parameter(
         config.layer_scale_init_value * torch.ones((__a)							)							,						requires_grad=__a							)
     _a  :       Dict             =	nn.Parameter(
         config.layer_scale_init_value * torch.ones((__a)							)							,						requires_grad=__a							)







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

     _a  :       Any             =	self.output(self.after_norm(__a							)							)
     _a  :       int             =	self.layer_scale_a.unsqueeze(-1							).unsqueeze(-1							) * layer_output
     # Second residual connection
     _a  :       Union[str, Any]             =	hidden_states + self.drop_path(__a							)

     _a  :       Union[str, Any]             =	(output,) + outputs
     return outputs

   else:
     _a  :       Optional[Any]             =	self.drop_path(self.pooling(self.before_norm(__a							)							)							)
     # First residual connection
     _a  :       int             =	pooling_output + hidden_states
     _a  :       Tuple             =	()

     # Second residual connection inside the PoolFormerOutput block
     _a  :       List[Any]             =	self.drop_path(self.output(self.after_norm(__a							)							)							)
     _a  :       Any             =	hidden_states + layer_output

     _a  :       Dict             =	(output,) + outputs
     return outputs







class        UpperCamelCase			(	nn.Module      ):


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

   # patch embeddings
   _a  :       int             =	[]
   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]							,						)							)
   _a  :       Union[str, Any]             =	nn.ModuleList(__a							)

   # Transformer blocks
   _a  :       str             =	[]
   _a  :       Optional[Any]             =	0
   for i in range(config.num_encoder_blocks							):
     # each block consists of layers
     _a  :       Optional[Any]             =	[]
     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							)							)

   _a  :       str             =	nn.ModuleList(__a							)







 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						List[Any]=False							,						UpperCAmelCase__		:						Union[str, Any]=True							)					->       Optional[int]:
   _a  :       Dict             =	() if output_hidden_states else None

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

     if output_hidden_states:
       _a  :       int             =	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        UpperCamelCase			(	lowerCamelCase_      ):
 UpperCamelCase					:       Union[str, Any]			=					PoolFormerConfig
 UpperCamelCase					:       Tuple			=					'''poolformer'''
 UpperCamelCase					:       List[str]			=					'''pixel_values'''
 UpperCamelCase					:       Tuple			=					True


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						Union[str, Any]							)					->       Tuple:
   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		:						Dict							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						Optional[int]=False							)					->       str:
   if isinstance(__a							,						__a							):
     _a  :       Tuple             =	value







_snake_case          =   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'

_snake_case          =   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.'''		,     lowerCamelCase_		,     )
class        UpperCamelCase			(	lowerCamelCase_      ):


 def __init__(     self		:						str							,						UpperCAmelCase__		:						Optional[Any]							)					->       Tuple:
   super().__init__(__a							)
   _a  :       Dict             =	config

   _a  :       List[Any]             =	PoolFormerEncoder(__a							)

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


 def _lowercase	(     self		:						str							)					->       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		:						Union[str, Any]							,						UpperCAmelCase__		:						Optional[torch.FloatTensor] = None							,						UpperCAmelCase__		:						Optional[bool] = None							,						UpperCAmelCase__		:						Optional[bool] = None							,						)					->       Any:
   _a  :       int             =	(
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   _a  :       int             =	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"""							)

   _a  :       Dict             =	self.encoder(
       __a							,						output_hidden_states=__a							,						return_dict=__a							,						)
   _a  :       Any             =	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        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						Any							,						UpperCAmelCase__		:						int							)					->       Optional[int]:
   super().__init__()
   _a  :       Any             =	nn.Linear(config.hidden_size							,						config.hidden_size							)







 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Optional[int]							)					->       Any:
   _a  :       List[str]             =	self.dense(__a							)
   return output







@add_start_docstrings(
    '''\n    PoolFormer Model transformer with an image classification head on top\n    '''		,     lowerCamelCase_		,     )
class        UpperCamelCase			(	lowerCamelCase_      ):


 def __init__(     self		:						Dict							,						UpperCAmelCase__		:						Optional[Any]							)					->       List[Any]:
   super().__init__(__a							)
   _a  :       Dict             =	config.num_labels
   _a  :       Optional[Any]             =	PoolFormerModel(__a							)

   # Final norm
   _a  :       List[str]             =	PoolFormerGroupNorm(config.hidden_sizes[-1]							)
   # Classifier head
   _a  :       List[str]             =	(
       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		:						Any							,						UpperCAmelCase__		:						Optional[torch.FloatTensor] = None							,						UpperCAmelCase__		:						Optional[torch.LongTensor] = None							,						UpperCAmelCase__		:						Optional[bool] = None							,						UpperCAmelCase__		:						Optional[bool] = None							,						)					->       Any:
   _a  :       Optional[Any]             =	return_dict if return_dict is not None else self.config.use_return_dict

   _a  :       List[Any]             =	self.poolformer(
       __a							,						output_hidden_states=__a							,						return_dict=__a							,						)

   _a  :       List[Any]             =	outputs[0]

   _a  :       Optional[int]             =	self.classifier(self.norm(__a							).mean([-2, -1]							)							)

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

     if self.config.problem_type == "regression":
       _a  :       List[str]             =	MSELoss()
       if self.num_labels == 1:
         _a  :       str             =	loss_fct(logits.squeeze()							,						labels.squeeze()							)
       else:
         _a  :       List[str]             =	loss_fct(__a							,						__a							)
     elif self.config.problem_type == "single_label_classification":
       _a  :       List[str]             =	CrossEntropyLoss()
       _a  :       List[str]             =	loss_fct(logits.view(-1							,						self.num_labels							)							,						labels.view(-1							)							)
     elif self.config.problem_type == "multi_label_classification":
       _a  :       Optional[int]             =	BCEWithLogitsLoss()
       _a  :       Optional[Any]             =	loss_fct(__a							,						__a							)

   if not return_dict:
     _a  :       Tuple             =	(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				( lowercase						:      Optional[Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	str:
				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				( lowercase						:      Optional[Any]    ,							lowercase						:      int    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]    ,							lowercase						:      int=True						)	->	Any:
				model.train()
				_a								= model(lowercase						)
				_a								= F.mse_loss(lowercase    ,							target.to(output.device						)						)
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowercase						)






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






def  _lowerCamelCase				( lowercase						:      Optional[Any]						)	->	Optional[int]:
				# Test when on a single CPU or GPU that the context manager does nothing
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= 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(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      Tuple						)	->	Tuple:
				# Test on distributed setup that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= 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(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






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






def  _lowerCamelCase				( lowercase						:      int=False    ,							lowercase						:      int=False						)	->	Dict:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a	,					_a	,					_a	,					_a	,					_a								= get_training_setup(lowercase    ,							lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_a	,					_a								= batch.values()
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								model.train()
								ddp_model.train()
								step_model(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'
								_a								= (((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(1337 + iteration						)
				GradientState._reset_state()






def  _lowerCamelCase				( )	->	Any:
				_a								= Accelerator()

				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a								= RegressionDataset(length=96						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a	,					_a								= 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  _lowerCamelCase				( )	->	Optional[Any]:
				_a								= Accelerator()
				_a								= accelerator.state
				if state.local_process_index == 0:
								print("**Test `accumulate` gradient accumulation with dataloader break**"						)
				test_dataloader_break()
				if state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print("**Test NOOP `no_sync` context manager**"						)
								test_noop_sync(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  _lowerCamelCase				( lowercase						:      Any						)	->	Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()
