


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__     					=			logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE__     					=			"PoolFormerConfig"

# Base docstring
SCREAMING_SNAKE_CASE__     					=			"sail/poolformer_s12"
SCREAMING_SNAKE_CASE__     					=			[1, 512, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE__     					=			"sail/poolformer_s12"
SCREAMING_SNAKE_CASE__     					=			"tabby, tabby cat"

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


def     lowercase	(    a     , a = 0.0     , a = False ):



		'''simple docstring'''




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



class 						_UpperCAmelCase      (     nn.Module							):






		def __init__(						self				:     int   ,  UpperCAmelCase				:     Optional[float] = None):
				super().__init__()
				SCREAMING_SNAKE_CASE_				:List[str]				     =						drop_prob






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     torch.Tensor):
				return drop_path(UpperCAmelCase   ,  self.drop_prob   ,  self.training)







		def _snake_case		(						self				:     int):
				return "p={}".format(self.drop_prob)




class 						_UpperCAmelCase      (     nn.Module							):






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

				SCREAMING_SNAKE_CASE_				:Dict				     =						nn.Convad(UpperCAmelCase   ,  UpperCAmelCase   ,  kernel_size=UpperCAmelCase   ,  stride=UpperCAmelCase   ,  padding=UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Dict				     =						norm_layer(UpperCAmelCase) if norm_layer else nn.Identity()







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Dict):
				SCREAMING_SNAKE_CASE_				:str				     =						self.projection(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.norm(UpperCAmelCase)
				return embeddings




class 						_UpperCAmelCase      (     nn.GroupNorm							):






		def __init__(						self				:     str   ,  UpperCAmelCase				:     Optional[Any]   ,  **UpperCAmelCase				:     Optional[Any]):
				super().__init__(1   ,  UpperCAmelCase   ,  **UpperCAmelCase)




class 						_UpperCAmelCase      (     nn.Module							):






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







		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Tuple):
				return self.pool(UpperCAmelCase) - hidden_states




class 						_UpperCAmelCase      (     nn.Module							):






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







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.conva(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Dict				     =						self.act_fn(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.drop(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Any				     =						self.conva(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:int				     =						self.drop(UpperCAmelCase)

				return hidden_states




class 						_UpperCAmelCase      (     nn.Module							):






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

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







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Union[str, Any]):
				if self.use_layer_scale:
						SCREAMING_SNAKE_CASE_				:int				     =						self.pooling(self.before_norm(UpperCAmelCase))
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.layer_scale_a.unsqueeze(-1).unsqueeze(-1) * pooling_output
						# First residual connection
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						hidden_states + self.drop_path(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:Dict				     =						()

						SCREAMING_SNAKE_CASE_				:int				     =						self.output(self.after_norm(UpperCAmelCase))
						SCREAMING_SNAKE_CASE_				:Any				     =						self.layer_scale_a.unsqueeze(-1).unsqueeze(-1) * layer_output
						# Second residual connection
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						hidden_states + self.drop_path(UpperCAmelCase)

						SCREAMING_SNAKE_CASE_				:List[Any]				     =						(output,) + outputs
						return outputs

				else:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.drop_path(self.pooling(self.before_norm(UpperCAmelCase)))
						# First residual connection
						SCREAMING_SNAKE_CASE_				:Dict				     =						pooling_output + hidden_states
						SCREAMING_SNAKE_CASE_				:Any				     =						()

						# Second residual connection inside the PoolFormerOutput block
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.drop_path(self.output(self.after_norm(UpperCAmelCase)))
						SCREAMING_SNAKE_CASE_				:Tuple				     =						hidden_states + layer_output

						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(output,) + outputs
						return outputs




class 						_UpperCAmelCase      (     nn.Module							):






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

				# patch embeddings
				SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
				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]   ,  ))
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						nn.ModuleList(UpperCAmelCase)

				# Transformer blocks
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
				SCREAMING_SNAKE_CASE_				:List[str]				     =						0
				for i in range(config.num_encoder_blocks):
						# each block consists of layers
						SCREAMING_SNAKE_CASE_				:Dict				     =						[]
						if i != 0:
								cur += config.depths[i - 1]
						for j in range(config.depths[i]):
								layers.append(
								    PoolFormerLayer(
								        UpperCAmelCase   ,  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(UpperCAmelCase))

				SCREAMING_SNAKE_CASE_				:int				     =						nn.ModuleList(UpperCAmelCase)







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     List[Any]=False   ,  UpperCAmelCase				:     Optional[Any]=True):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						() if output_hidden_states else None

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

						if output_hidden_states:
								SCREAMING_SNAKE_CASE_				:str				     =						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=UpperCAmelCase   ,  hidden_states=UpperCAmelCase)




class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Any       			=  PoolFormerConfig
		lowerCamelCase_	:			Optional[Any]       			=  """poolformer"""
		lowerCamelCase_	:			List[str]       			=  """pixel_values"""
		lowerCamelCase_	:			str       			=  True






		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     Optional[Any]):
				if isinstance(UpperCAmelCase   ,  (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(UpperCAmelCase   ,  nn.LayerNorm):
						module.bias.data.zero_()
						module.weight.data.fill_(1.0)







		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     str=False):
				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						value




SCREAMING_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"

SCREAMING_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."""		,		lowercase		,		)
class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     int   ,  UpperCAmelCase				:     List[str]):
				super().__init__(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						config

				SCREAMING_SNAKE_CASE_				:Tuple				     =						PoolFormerEncoder(UpperCAmelCase)

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






		def _snake_case		(						self				:     int):
				return self.embeddings.patch_embeddings







		@add_start_docstrings_to_model_forward(UpperCAmelCase)
		@add_code_sample_docstrings(
		    checkpoint=_CHECKPOINT_FOR_DOC   ,  output_type=UpperCAmelCase   ,  config_class=_CONFIG_FOR_DOC   ,  modality="vision"   ,  expected_output=_EXPECTED_OUTPUT_SHAPE   ,  )
		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     Optional[torch.FloatTensor] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						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")

				SCREAMING_SNAKE_CASE_				:Dict				     =						self.encoder(
				    UpperCAmelCase   ,  output_hidden_states=UpperCAmelCase   ,  return_dict=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:Tuple				     =						encoder_outputs[0]

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

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




class 						_UpperCAmelCase      (     nn.Module							):






		def __init__(						self				:     Any   ,  UpperCAmelCase				:     Optional[int]):
				super().__init__()
				SCREAMING_SNAKE_CASE_				:Any				     =						nn.Linear(config.hidden_size   ,  config.hidden_size)







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:str				     =						self.dense(UpperCAmelCase)
				return output




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






		def __init__(						self				:     List[str]   ,  UpperCAmelCase				:     Optional[Any]):
				super().__init__(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						config.num_labels
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						PoolFormerModel(UpperCAmelCase)

				# Final norm
				SCREAMING_SNAKE_CASE_				:Any				     =						PoolFormerGroupNorm(config.hidden_sizes[-1])
				# Classifier head
				SCREAMING_SNAKE_CASE_				:int				     =						(
				    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(UpperCAmelCase)
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,  output_type=UpperCAmelCase   ,  config_class=_CONFIG_FOR_DOC   ,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,  )
		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     Optional[torch.FloatTensor] = None   ,  UpperCAmelCase				:     Optional[torch.LongTensor] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						return_dict if return_dict is not None else self.config.use_return_dict

				SCREAMING_SNAKE_CASE_				:Any				     =						self.poolformer(
				    UpperCAmelCase   ,  output_hidden_states=UpperCAmelCase   ,  return_dict=UpperCAmelCase   ,  )

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						outputs[0]

				SCREAMING_SNAKE_CASE_				:int				     =						self.classifier(self.norm(UpperCAmelCase).mean([-2, -1]))

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

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

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

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










import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


SCREAMING_SNAKE_CASE__     					=			get_logger(__name__)


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		os.makedirs(a     , exist_ok=a )
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						SCREAMING_SNAKE_CASE_				:str				     =						os.path.join(a     , a )
						if accelerator.process_index == 0:
								logger.info(F"Saving model to {output_model_file}" )
								torch.save(a     , a )
								logger.info(F"Model saved to {output_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						(
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , a )
						logger.info(F"Saving model to {output_model_file}" )
						torch.save(a     , a )
						logger.info(F"Model saved to {output_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , F"{MODEL_NAME}_{model_index}" )
						os.makedirs(a     , exist_ok=a )
						logger.info(F"Saving model to {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:int				     =						{"model": state_dict}

						dist_cp.save_state_dict(
						    state_dict=a     , storage_writer=dist_cp.FileSystemWriter(a )     , planner=DefaultSavePlanner()     , )
						logger.info(F"Model saved to {ckpt_dir}" )


def     lowercase	(    a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(a ) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
										    "initializing FSDP object" )
								return
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:int				     =						(
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:int				     =						os.path.join(a     , a )
						logger.info(F"Loading model from {input_model_file}" )
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.load(a )
						logger.info(F"Model loaded from {input_model_file}" )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						(
						    os.path.join(a     , F"{MODEL_NAME}_{model_index}" )
						    if F"{MODEL_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading model from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Any				     =						{"model": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=a     , storage_reader=dist_cp.FileSystemReader(a )     , planner=DefaultLoadPlanner()     , )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						state_dict["model"]
						logger.info(F"Model loaded from {ckpt_dir}" )
				model.load_state_dict(a )


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		os.makedirs(a     , exist_ok=a )
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				SCREAMING_SNAKE_CASE_				:str				     =						FSDP.optim_state_dict(a     , a )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								SCREAMING_SNAKE_CASE_				:List[str]				     =						(
								    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
								)
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						os.path.join(a     , a )
								logger.info(F"Saving Optimizer state to {output_optimizer_file}" )
								torch.save(a     , a )
								logger.info(F"Optimizer state saved in {output_optimizer_file}" )
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						os.path.join(a     , F"{OPTIMIZER_NAME}_{optimizer_index}" )
						os.makedirs(a     , exist_ok=a )
						logger.info(F"Saving Optimizer state to {ckpt_dir}" )
						dist_cp.save_state_dict(
						    state_dict={"optimizer": optim_state}     , storage_writer=dist_cp.FileSystemWriter(a )     , planner=DefaultSavePlanner()     , )
						logger.info(F"Optimizer state saved in {ckpt_dir}" )


def     lowercase	(    a     , a     , a     , a     , a     , a=0 ):



		'''simple docstring'''




		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    a     , fsdp_plugin.state_dict_type     , fsdp_plugin.state_dict_config     , fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						(
						    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
						)
						SCREAMING_SNAKE_CASE_				:Dict				     =						os.path.join(a     , a )
						logger.info(F"Loading Optimizer state from {input_optimizer_file}" )
						SCREAMING_SNAKE_CASE_				:str				     =						torch.load(a )
						logger.info(F"Optimizer state loaded from {input_optimizer_file}" )
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						(
						    os.path.join(a     , F"{OPTIMIZER_NAME}_{optimizer_index}" )
						    if F"{OPTIMIZER_NAME}" not in input_dir
						    else input_dir
						)
						logger.info(F"Loading Optimizer from {ckpt_dir}" )
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()     , optimizer_key="optimizer"     , storage_reader=dist_cp.FileSystemReader(a )     , )
						SCREAMING_SNAKE_CASE_				:List[str]				     =						optim_state["optimizer"]
						logger.info(F"Optimizer loaded from {ckpt_dir}" )
				SCREAMING_SNAKE_CASE_				:int				     =						FSDP.optim_state_dict_to_load(a     , a     , a )
				optimizer.load_state_dict(a )






