





'''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


lowerCAmelCase_					:    Dict      	=			logging.get_logger(__name__)

# General docstring
lowerCAmelCase_					:    Any      	=			'PoolFormerConfig'

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

# Image classification docstring
lowerCAmelCase_					:    Dict      	=			'sail/poolformer_s12'
lowerCAmelCase_					:    List[str]      	=			'tabby, tabby cat'

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






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      float = 0.0    ,							lowercase						:      bool = False						)	->	str:
				if drop_prob == 0.0 or not training:
								return input
				_a								= 1 - drop_prob
				_a								= (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
				_a								= keep_prob + torch.rand(lowercase    ,							dtype=input.dtype    ,							device=input.device						)
				random_tensor.floor_()  # binarize
				_a								= input.div(lowercase						) * random_tensor
				return output
class 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






				"""simple docstring"""







				def __init__(  self     :					int					,				__a     :					Optional[float] = None					):
								super().__init__()
								_a								= drop_prob




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




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


class 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






				"""simple docstring"""







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

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




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Optional[Any]					):
								_a								= self.projection(__a					)
								_a								= self.norm(__a					)
								return embeddings


class 				__SCREAMING_SNAKE_CASE  (nn.GroupNorm      ):






				"""simple docstring"""







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


class 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






				"""simple docstring"""







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




				def   UpperCamelCase__   (  self     :					Dict					,				__a     :					List[Any]					):
								return self.pool(__a					) - hidden_states


class 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






				"""simple docstring"""







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




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					str					):
								_a								= self.conva(__a					)
								_a								= self.act_fn(__a					)
								_a								= self.drop(__a					)
								_a								= self.conva(__a					)
								_a								= self.drop(__a					)

								return hidden_states


class 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






				"""simple docstring"""







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

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




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

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

												_a								= (output,) + outputs
												return outputs

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

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

												_a								= (output,) + outputs
												return outputs


class 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






				"""simple docstring"""







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

								# patch embeddings
								_a								= []
								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								= nn.ModuleList(__a					)

								# Transformer blocks
								_a								= []
								_a								= 0
								for i in range(config.num_encoder_blocks					):
												# each block consists of layers
												_a								= []
												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								= nn.ModuleList(__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]=False					,				__a     :					Union[str, Any]=True					):
								_a								= () if output_hidden_states else None

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

												if output_hidden_states:
																_a								= 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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =PoolFormerConfig
				__a            ='poolformer'
				__a            ='pixel_values'
				__a            =True




				def   UpperCamelCase__   (  self     :					List[Any]					,				__a     :					Union[str, 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   UpperCamelCase__   (  self     :					Dict					,				__a     :					List[str]					,				__a     :					Optional[int]=False					):
								if isinstance(__a					,				__a					):
												_a								= value


lowerCAmelCase_					:    List[Any]      	=			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'

lowerCAmelCase_					:    Optional[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.'       ,							lowerCamelCase_       ,							)
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					str					,				__a     :					Optional[Any]					):
								super().__init__(__a					)
								_a								= config

								_a								= PoolFormerEncoder(__a					)

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




				def   UpperCamelCase__   (  self     :					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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					Optional[torch.FloatTensor] = None					,				__a     :					Optional[bool] = None					,				__a     :					Optional[bool] = None					,				):
								_a								= (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								_a								= 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								= self.encoder(
								    __a					,				output_hidden_states=__a					,				return_dict=__a					,				)
								_a								= 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 				__SCREAMING_SNAKE_CASE  (nn.Module      ):






				"""simple docstring"""







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




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Optional[int]					):
								_a								= self.dense(__a					)
								return output


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






				"""simple docstring"""







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

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

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

								_a								= outputs[0]

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

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

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

								if not return_dict:
												_a								= (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()
