

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


UpperCamelCase__  :      int			=    get_logger(__name__)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		List[str]=0       ):





    """simple docstring"""


    os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_SNAKE_CASE       )
    with FSDP.state_dict_type(
        _SCREAMING_SNAKE_CASE ,   fsdp_plugin.state_dict_type ,   fsdp_plugin.state_dict_config ,   fsdp_plugin.optim_state_dict_config       ):
        SCREAMING_SNAKE_CASE_									=						model.state_dict()
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            if accelerator.process_index == 0:
                logger.info(f"""Saving model to {output_model_file}"""       )
                torch.save(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                logger.info(f"""Model saved to {output_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						(
                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_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Saving model to {output_model_file}"""       )
            torch.save(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Model saved to {output_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{MODEL_NAME}_{model_index}"""       )
            os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Saving model to {ckpt_dir}"""       )
            SCREAMING_SNAKE_CASE_									=						{'model': state_dict}

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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Optional[int]=0       ):





    """simple docstring"""


    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        _SCREAMING_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(_SCREAMING_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
            SCREAMING_SNAKE_CASE_									=						f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Loading model from {input_model_file}"""       )
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Model loaded from {input_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						(
                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_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Loading model from {input_model_file}"""       )
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Model loaded from {input_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						(
                os.path.join(_SCREAMING_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}"""       )
            SCREAMING_SNAKE_CASE_									=						{'model': model.state_dict()}
            dist_cp.load_state_dict(
                state_dict=_SCREAMING_SNAKE_CASE ,   storage_reader=dist_cp.FileSystemReader(_SCREAMING_SNAKE_CASE       ) ,   planner=DefaultLoadPlanner() ,   )
            SCREAMING_SNAKE_CASE_									=						state_dict['model']
            logger.info(f"""Model loaded from {ckpt_dir}"""       )
        model.load_state_dict(_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		List[str]=0       ):





    """simple docstring"""


    os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_SNAKE_CASE       )
    with FSDP.state_dict_type(
        _SCREAMING_SNAKE_CASE ,   fsdp_plugin.state_dict_type ,   fsdp_plugin.state_dict_config ,   fsdp_plugin.optim_state_dict_config       ):
        SCREAMING_SNAKE_CASE_									=						FSDP.optim_state_dict(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            if accelerator.process_index == 0:
                SCREAMING_SNAKE_CASE_									=						(
                    f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
                )
                SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                logger.info(f"""Saving Optimizer state to {output_optimizer_file}"""       )
                torch.save(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                logger.info(f"""Optimizer state saved in {output_optimizer_file}"""       )
        else:
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{OPTIMIZER_NAME}_{optimizer_index}"""       )
            os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_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(_SCREAMING_SNAKE_CASE       ) ,   planner=DefaultSavePlanner() ,   )
            logger.info(f"""Optimizer state saved in {ckpt_dir}"""       )





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]=0       ):





    """simple docstring"""


    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        _SCREAMING_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:
            SCREAMING_SNAKE_CASE_									=						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_									=						(
                f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Loading Optimizer state from {input_optimizer_file}"""       )
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Optimizer state loaded from {input_optimizer_file}"""       )
        else:
            SCREAMING_SNAKE_CASE_									=						(
                os.path.join(_SCREAMING_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}"""       )
            SCREAMING_SNAKE_CASE_									=						load_sharded_optimizer_state_dict(
                model_state_dict=model.state_dict() ,   optimizer_key='optimizer' ,   storage_reader=dist_cp.FileSystemReader(_SCREAMING_SNAKE_CASE       ) ,   )
            SCREAMING_SNAKE_CASE_									=						optim_state['optimizer']
            logger.info(f"""Optimizer loaded from {ckpt_dir}"""       )
        SCREAMING_SNAKE_CASE_									=						FSDP.optim_state_dict_to_load(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        optimizer.load_state_dict(_SCREAMING_SNAKE_CASE       )








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

      # pass init params to Encoder
      SCREAMING_SNAKE_CASE_									=						Encoder(
          in_channels=_A						,		out_channels=_A						,		down_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		double_z=_A						,		)

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

      # pass init params to Decoder
      SCREAMING_SNAKE_CASE_									=						Decoder(
          in_channels=_A						,		out_channels=_A						,		up_block_types=_A						,		block_out_channels=_A						,		layers_per_block=_A						,		act_fn=_A						,		norm_num_groups=_A						,		norm_type=_A						,		)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


