






"""simple docstring"""
import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_lowerCAmelCase         =							{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_lowerCAmelCase         =							logging.get_logger(__name__)



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				"""maskformer"""
	_A       :				Optional[Any]    =				{"""hidden_size""": """mask_feature_size"""}
	_A       :				List[str]    =				["""resnet""", """swin"""]
	_A       :				str    =				["""detr"""]





	def __init__(self     ,     lowerCAmelCase_ = 256     ,     lowerCAmelCase_ = 256     ,     lowerCAmelCase_ = 0.1     ,     lowerCAmelCase_ = False     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 0.02     ,     lowerCAmelCase_ = 1.0     ,     lowerCAmelCase_ = 1.0     ,     lowerCAmelCase_ = 1.0     ,     lowerCAmelCase_ = 20.0     ,     lowerCAmelCase_ = None     ,     **lowerCAmelCase_     ,     ):
		if backbone_config is None:
			# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
			A_		:						str	    =   SwinConfig(
			    image_size=384     ,     in_channels=3     ,     patch_size=4     ,     embed_dim=128     ,     depths=[2, 2, 18, 2]     ,     num_heads=[4, 8, 16, 32]     ,     window_size=12     ,     drop_path_rate=0.3     ,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     ,     )

		if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
			A_		:						List[Any]	    =   backbone_config.pop("""model_type"""							)
			A_		:						Any	    =   CONFIG_MAPPING[backbone_model_type]
			A_		:						List[Any]	    =   config_class.from_dict(lowerCAmelCase_							)

		# verify that the backbone is supported
		if backbone_config.model_type not in self.backbones_supported:
			logger.warning_once(
			    f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
			    f"""Supported model types: {",".join(self.backbones_supported							)}"""							)

		if decoder_config is None:
			# fall back to https://huggingface.co/facebook/detr-resnet-50
			A_		:						List[Any]	    =   DetrConfig()
		else:
			# verify that the decoder is supported
			A_		:						Any	    =   (
			    decoder_config.pop("""model_type"""							) if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							) else decoder_config.model_type
			)
			if decoder_type not in self.decoders_supported:
				raise ValueError(
				    f"""Transformer Decoder {decoder_type} not supported, please use one of"""
				    f""" {",".join(self.decoders_supported							)}"""							)
			if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						Union[str, Any]	    =   CONFIG_MAPPING[decoder_type]
				A_		:						int	    =   config_class.from_dict(lowerCAmelCase_							)

		A_		:						Optional[Any]	    =   backbone_config
		A_		:						Optional[int]	    =   decoder_config
		# main feature dimension for the model
		A_		:						str	    =   fpn_feature_size
		A_		:						List[str]	    =   mask_feature_size
		# initializer
		A_		:						str	    =   init_std
		A_		:						Any	    =   init_xavier_std
		# Hungarian matcher && loss
		A_		:						Optional[Any]	    =   cross_entropy_weight
		A_		:						Union[str, Any]	    =   dice_weight
		A_		:						Any	    =   mask_weight
		A_		:						Optional[Any]	    =   use_auxiliary_loss
		A_		:						Dict	    =   no_object_weight
		A_		:						str	    =   output_auxiliary_logits

		A_		:						Union[str, Any]	    =   self.decoder_config.encoder_attention_heads
		A_		:						Tuple	    =   self.decoder_config.num_hidden_layers
		super().__init__(**lowerCAmelCase_							)





	@classmethod
	def    lowerCamelCase(cls     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return cls(
		    backbone_config=lowerCAmelCase_     ,     decoder_config=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   copy.deepcopy(self.__dict__							)
		A_		:						Dict	    =   self.backbone_config.to_dict()
		A_		:						Any	    =   self.decoder_config.to_dict()
		A_		:						str	    =   self.__class__.model_type
		return output









"""simple docstring"""
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


_lowerCAmelCase         =							get_logger(__name__)






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

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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=0					):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__   ,   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(snake_case__					) != 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
			A_		:						Tuple	    =   F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
			A_		:						Any	    =   os.path.join(snake_case__   ,   snake_case__					)
			logger.info(F"""Loading model from {input_model_file}"""					)
			A_		:						List[Any]	    =   torch.load(snake_case__					)
			logger.info(F"""Model loaded from {input_model_file}"""					)
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A_		:						List[str]	    =   (
			    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
			    if model_index == 0
			    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
			)
			A_		:						Any	    =   os.path.join(snake_case__   ,   snake_case__					)
			logger.info(F"""Loading model from {input_model_file}"""					)
			A_		:						List[str]	    =   torch.load(snake_case__					)
			logger.info(F"""Model loaded from {input_model_file}"""					)
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A_		:						Optional[Any]	    =   (
			    os.path.join(snake_case__   ,   F"""{MODEL_NAME}_{model_index}"""					)
			    if F"""{MODEL_NAME}""" not in input_dir
			    else input_dir
			)
			logger.info(F"""Loading model from {ckpt_dir}"""					)
			A_		:						List[str]	    =   {"""model""": model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=snake_case__   ,   storage_reader=dist_cp.FileSystemReader(snake_case__					)   ,   planner=DefaultLoadPlanner()   ,   )
			A_		:						Tuple	    =   state_dict["""model"""]
			logger.info(F"""Model loaded from {ckpt_dir}"""					)
		model.load_state_dict(snake_case__					)






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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=0					):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    snake_case__   ,   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:
			A_		:						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:
			A_		:						Tuple	    =   (
			    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
			)
			A_		:						int	    =   os.path.join(snake_case__   ,   snake_case__					)
			logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""					)
			A_		:						Tuple	    =   torch.load(snake_case__					)
			logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""					)
		else:
			A_		:						str	    =   (
			    os.path.join(snake_case__   ,   F"""{OPTIMIZER_NAME}_{optimizer_index}"""					)
			    if F"""{OPTIMIZER_NAME}""" not in input_dir
			    else input_dir
			)
			logger.info(F"""Loading Optimizer from {ckpt_dir}"""					)
			A_		:						Union[str, Any]	    =   load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()   ,   optimizer_key="""optimizer"""   ,   storage_reader=dist_cp.FileSystemReader(snake_case__					)   ,   )
			A_		:						Tuple	    =   optim_state["""optimizer"""]
			logger.info(F"""Optimizer loaded from {ckpt_dir}"""					)
		A_		:						List[Any]	    =   FSDP.optim_state_dict_to_load(snake_case__   ,   snake_case__   ,   snake_case__					)
		optimizer.load_state_dict(snake_case__					)

