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


A   :     Tuple       =				get_logger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=0 ):
      os.makedirs(__UpperCamelCase   ,					exist_ok=__UpperCamelCase )
      with FSDP.state_dict_type(
          __UpperCamelCase   ,					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(__UpperCamelCase   ,					__UpperCamelCase )
                  if accelerator.process_index == 0:
                        logger.info(F'''Saving model to {output_model_file}''' )
                        torch.save(__UpperCamelCase   ,					__UpperCamelCase )
                        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(__UpperCamelCase   ,					__UpperCamelCase )
                  logger.info(F'''Saving model to {output_model_file}''' )
                  torch.save(__UpperCamelCase   ,					__UpperCamelCase )
                  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(__UpperCamelCase   ,					F'''{MODEL_NAME}_{model_index}''' )
                  os.makedirs(__UpperCamelCase   ,					exist_ok=__UpperCamelCase )
                  logger.info(F'''Saving model to {ckpt_dir}''' )
                  SCREAMING_SNAKE_CASE_      =     {"model": state_dict}

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


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=0 ):
      accelerator.wait_for_everyone()
      with FSDP.state_dict_type(
          __UpperCamelCase   ,					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(__UpperCamelCase ) != 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(__UpperCamelCase   ,					__UpperCamelCase )
                  logger.info(F'''Loading model from {input_model_file}''' )
                  SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase )
                  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(__UpperCamelCase   ,					__UpperCamelCase )
                  logger.info(F'''Loading model from {input_model_file}''' )
                  SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase )
                  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(__UpperCamelCase   ,					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=__UpperCamelCase   ,					storage_reader=dist_cp.FileSystemReader(__UpperCamelCase )   ,					planner=DefaultLoadPlanner()   ,					)
                  SCREAMING_SNAKE_CASE_      =     state_dict["model"]
                  logger.info(F'''Model loaded from {ckpt_dir}''' )
            model.load_state_dict(__UpperCamelCase )


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=0 ):
      os.makedirs(__UpperCamelCase   ,					exist_ok=__UpperCamelCase )
      with FSDP.state_dict_type(
          __UpperCamelCase   ,					fsdp_plugin.state_dict_type   ,					fsdp_plugin.state_dict_config   ,					fsdp_plugin.optim_state_dict_config ):
            SCREAMING_SNAKE_CASE_      =     FSDP.optim_state_dict(__UpperCamelCase   ,					__UpperCamelCase )
            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(__UpperCamelCase   ,					__UpperCamelCase )
                        logger.info(F'''Saving Optimizer state to {output_optimizer_file}''' )
                        torch.save(__UpperCamelCase   ,					__UpperCamelCase )
                        logger.info(F'''Optimizer state saved in {output_optimizer_file}''' )
            else:
                  SCREAMING_SNAKE_CASE_      =     os.path.join(__UpperCamelCase   ,					F'''{OPTIMIZER_NAME}_{optimizer_index}''' )
                  os.makedirs(__UpperCamelCase   ,					exist_ok=__UpperCamelCase )
                  logger.info(F'''Saving Optimizer state to {ckpt_dir}''' )
                  dist_cp.save_state_dict(
                      state_dict={"optimizer": optim_state}   ,					storage_writer=dist_cp.FileSystemWriter(__UpperCamelCase )   ,					planner=DefaultSavePlanner()   ,					)
                  logger.info(F'''Optimizer state saved in {ckpt_dir}''' )





def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase=0 ):
      accelerator.wait_for_everyone()
      with FSDP.state_dict_type(
          __UpperCamelCase   ,					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(__UpperCamelCase   ,					__UpperCamelCase )
                  logger.info(F'''Loading Optimizer state from {input_optimizer_file}''' )
                  SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase )
                  logger.info(F'''Optimizer state loaded from {input_optimizer_file}''' )
            else:
                  SCREAMING_SNAKE_CASE_      =     (
                      os.path.join(__UpperCamelCase   ,					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(__UpperCamelCase )   ,					)
                  SCREAMING_SNAKE_CASE_      =     optim_state["optimizer"]
                  logger.info(F'''Optimizer loaded from {ckpt_dir}''' )
            SCREAMING_SNAKE_CASE_      =     FSDP.optim_state_dict_to_load(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )
            optimizer.load_state_dict(__UpperCamelCase )



from ...processing_utils import ProcessorMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''feature_extractor''']
  lowerCamelCase__   =  '''TvltImageProcessor'''
  lowerCamelCase__   =  '''TvltFeatureExtractor'''







  def __init__(							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Any      )     ->		int:
        super().__init__(image_processor=__magic_name__     ,   feature_extractor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     image_processor
        SCREAMING_SNAKE_CASE_      =     feature_extractor







  def __call__(							self       :				List[str]     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				str=None     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				int=False     ,   *__magic_name__       :				int     ,   **__magic_name__       :				Any     ,   )     ->		List[Any]:

        if images is None and audio is None:
              raise ValueError("You need to specify either an `images` or `audio` input to process."      )

        SCREAMING_SNAKE_CASE_      =     None
        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   mask_pixel=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if images_mixed is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   is_mixed=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if audio is not None:
              SCREAMING_SNAKE_CASE_      =     self.feature_extractor(
                  __magic_name__     ,   *__magic_name__     ,   sampling_rate=__magic_name__     ,   mask_audio=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     {}
        if audio is not None:
              output_dict.update(__magic_name__      )
        if images is not None:
              output_dict.update(__magic_name__      )
        if images_mixed_dict is not None:
              output_dict.update(__magic_name__      )
        return output_dict







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.image_processor.model_input_names
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor.model_input_names
        return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )


