'''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


__a:			Union[str, Any]	       =       get_logger(__name__)
def       __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					):
  lowercase__    :   Tuple						= model.state_dict()
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   lowercase__    :   int						= F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
   lowercase__    :   Optional[int]						= 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:
   lowercase__    :   Any						= (
       F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
       if model_index == 0
       else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
   )
   lowercase__    :   Optional[Any]						= 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:
   lowercase__    :   str						= os.path.join(UpperCAmelCase		,					F"""{MODEL_NAME}_{model_index}"""					)
   os.makedirs(UpperCAmelCase		,					exist_ok=UpperCAmelCase					)
   logger.info(F"""Saving model to {ckpt_dir}"""					)
   lowercase__    :   List[str]						= {'''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       __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:
   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
   lowercase__    :   Any						= F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
   lowercase__    :   Union[str, Any]						= os.path.join(UpperCAmelCase		,					UpperCAmelCase					)
   logger.info(F"""Loading model from {input_model_file}"""					)
   lowercase__    :   str						= torch.load(UpperCAmelCase					)
   logger.info(F"""Model loaded from {input_model_file}"""					)
  elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
   lowercase__    :   Tuple						= (
       F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
       if model_index == 0
       else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
   )
   lowercase__    :   Tuple						= os.path.join(UpperCAmelCase		,					UpperCAmelCase					)
   logger.info(F"""Loading model from {input_model_file}"""					)
   lowercase__    :   Any						= torch.load(UpperCAmelCase					)
   logger.info(F"""Model loaded from {input_model_file}"""					)
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   lowercase__    :   List[str]						= (
       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}"""					)
   lowercase__    :   int						= {'''model''': model.state_dict()}
   dist_cp.load_state_dict(
       state_dict=UpperCAmelCase		,					storage_reader=dist_cp.FileSystemReader(UpperCAmelCase					)		,					planner=DefaultLoadPlanner()		,					)
   lowercase__    :   List[str]						= state_dict['''model''']
   logger.info(F"""Model loaded from {ckpt_dir}"""					)
  model.load_state_dict(UpperCAmelCase					)
def       __UpperCamelCase	(       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					):
  lowercase__    :   int						= FSDP.optim_state_dict(UpperCAmelCase		,					UpperCAmelCase					)
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   if accelerator.process_index == 0:
    lowercase__    :   Optional[Any]						= (
        F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
    )
    lowercase__    :   int						= 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:
   lowercase__    :   Optional[int]						= 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       __UpperCamelCase	(       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:
   lowercase__    :   Optional[int]						= 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:
   lowercase__    :   Tuple						= (
       F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
   )
   lowercase__    :   Optional[Any]						= os.path.join(UpperCAmelCase		,					UpperCAmelCase					)
   logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""					)
   lowercase__    :   Union[str, Any]						= torch.load(UpperCAmelCase					)
   logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""					)
  else:
   lowercase__    :   Optional[int]						= (
       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}"""					)
   lowercase__    :   Tuple						= load_sharded_optimizer_state_dict(
       model_state_dict=model.state_dict()		,					optimizer_key='''optimizer'''		,					storage_reader=dist_cp.FileSystemReader(UpperCAmelCase					)		,					)
   lowercase__    :   str						= optim_state['''optimizer''']
   logger.info(F"""Optimizer loaded from {ckpt_dir}"""					)
  lowercase__    :   int						= FSDP.optim_state_dict_to_load(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					)
  optimizer.load_state_dict(UpperCAmelCase					)

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   DDIMPipeline
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       SCREAMING_SNAKE_CASE 		=   PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= UNetaDModel(
            block_out_channels=(32, 64)     ,       layers_per_block=2     ,       sample_size=32     ,       in_channels=3     ,       out_channels=3     ,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')     ,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')     ,       )
        lowercase__    :   Any						= DDIMScheduler()
        lowercase__    :   Union[str, Any]						= {'''unet''': unet, '''scheduler''': scheduler}
        return components

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=0			)  ->  int:
        if str(__lowerCAmelCase			).startswith('''mps'''			):
         lowercase__    :   Optional[Any]						= torch.manual_seed(__lowerCAmelCase			)
        else:
         lowercase__    :   int						= torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
        lowercase__    :   List[Any]						= {
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   int						= '''cpu'''

        lowercase__    :   Optional[Any]						= self.get_dummy_components()
        lowercase__    :   str						= self.pipeline_class(**__lowerCAmelCase			)
        pipe.to(__lowerCAmelCase			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   str						= self.get_dummy_inputs(__lowerCAmelCase			)
        lowercase__    :   str						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        self.assertEqual(image.shape     ,       (1, 32, 32, 3)			)
        lowercase__    :   Optional[int]						= np.array(
            [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]			)
        lowercase__    :   Optional[Any]						= np.abs(image_slice.flatten() - expected_slice			).max()
        self.assertLessEqual(__lowerCAmelCase     ,       1E-3			)

       def _lowerCAmelCase(						self			)  ->  str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        super().test_save_load_local(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        super().test_save_load_optional_components(expected_max_difference=3E-3			)


       def _lowerCAmelCase(						self			)  ->  int:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3			)



@slow
@require_torch_gpu
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= '''google/ddpm-cifar10-32'''

        lowercase__    :   List[Any]						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   str						= DDIMScheduler()

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddim.to(__lowerCAmelCase			)
        ddim.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Dict						= torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= ddim(generator=__lowerCAmelCase     ,       eta=0.0     ,       output_type='''numpy'''			).images

        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        lowercase__    :   Optional[int]						= np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= '''google/ddpm-ema-bedroom-256'''

        lowercase__    :   Tuple						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= DDIMScheduler.from_pretrained(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddpm.to(__lowerCAmelCase			)
        ddpm.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= torch.manual_seed(0			)
        lowercase__    :   Optional[Any]						= ddpm(generator=__lowerCAmelCase     ,       output_type='''numpy'''			).images

        lowercase__    :   List[Any]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        lowercase__    :   Union[str, Any]						= np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

