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					:					Any=			get_logger(__name__)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Tuple      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    str=0  )							->      int:

	'''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  ):
		__snake_case      :							List[Any]						=			model.state_dict()
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			__snake_case      :							Optional[int]						=			F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
			__snake_case      :							Any						=			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:
			__snake_case      :							Dict						=			(
			    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
			    if model_index == 0
			    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
			)
			__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:
			__snake_case      :							Union[str, Any]						=			os.path.join(a__      ,							F"{MODEL_NAME}_{model_index}"  )
			os.makedirs(a__      ,							exist_ok=a__  )
			logger.info(F"Saving model to {ckpt_dir}"  )
			__snake_case      :							List[Any]						=			{'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   __UpperCAmelCase    (   UpperCAmelCase_   :    Dict      ,							UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    Dict      ,							UpperCAmelCase_   :    int=0  )							->      Union[str, Any]:

	'''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
			__snake_case      :							Optional[int]						=			F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
			__snake_case      :							List[Any]						=			os.path.join(a__      ,							a__  )
			logger.info(F"Loading model from {input_model_file}"  )
			__snake_case      :							Any						=			torch.load(a__  )
			logger.info(F"Model loaded from {input_model_file}"  )
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			__snake_case      :							Optional[Any]						=			(
			    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
			    if model_index == 0
			    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
			)
			__snake_case      :							Any						=			os.path.join(a__      ,							a__  )
			logger.info(F"Loading model from {input_model_file}"  )
			__snake_case      :							Optional[int]						=			torch.load(a__  )
			logger.info(F"Model loaded from {input_model_file}"  )
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			__snake_case      :							Dict						=			(
			    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}"  )
			__snake_case      :							Any						=			{'model': model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=a__      ,							storage_reader=dist_cp.FileSystemReader(a__  )      ,							planner=DefaultLoadPlanner()      ,							)
			__snake_case      :							Any						=			state_dict['model']
			logger.info(F"Model loaded from {ckpt_dir}"  )
		model.load_state_dict(a__  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Dict      ,							UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Tuple      ,							UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Dict      ,							UpperCAmelCase_   :    Optional[Any]=0  )							->      List[str]:

	'''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  ):
		__snake_case      :							Union[str, Any]						=			FSDP.optim_state_dict(a__      ,							a__  )
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				__snake_case      :							Optional[int]						=			(
				    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
				)
				__snake_case      :							Tuple						=			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:
			__snake_case      :							Tuple						=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    str=0  )							->      str:

	'''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:
			__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:
			__snake_case      :							Optional[Any]						=			(
			    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
			)
			__snake_case      :							Any						=			os.path.join(a__      ,							a__  )
			logger.info(F"Loading Optimizer state from {input_optimizer_file}"  )
			__snake_case      :							Optional[Any]						=			torch.load(a__  )
			logger.info(F"Optimizer state loaded from {input_optimizer_file}"  )
		else:
			__snake_case      :							Tuple						=			(
			    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}"  )
			__snake_case      :							str						=			load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()      ,							optimizer_key='optimizer'      ,							storage_reader=dist_cp.FileSystemReader(a__  )      ,							)
			__snake_case      :							str						=			optim_state['optimizer']
			logger.info(F"Optimizer loaded from {ckpt_dir}"  )
		__snake_case      :							Dict						=			FSDP.optim_state_dict_to_load(a__      ,							a__      ,							a__  )
		optimizer.load_state_dict(a__  )






"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_a					:					List[str]=			False

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
					return 32



				@property
				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					torch.manual_seed(0)
					__snake_case      :							Any						=			VQModel(
					    block_out_channels=[32, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,  latent_channels=3     ,  num_vq_embeddings=self.num_embed     ,  vq_embed_dim=3     ,  )
					return model



				@property
				def  _lowercase					(self    :				List[Any])   ->     Optional[int]:
					__snake_case      :							Dict						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					torch.manual_seed(0)
					__snake_case      :							Any						=			CLIPTextConfig(
					    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=self.text_embedder_hidden_size     ,  intermediate_size=37     ,  layer_norm_eps=1E-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=10_00     ,  )
					return CLIPTextModel(_A)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					torch.manual_seed(0)

					__snake_case      :							Any						=			12
					__snake_case      :							int						=			12

					__snake_case      :							List[Any]						=			{
					    'attention_bias': True,
					    'cross_attention_dim': 32,
					    'attention_head_dim': height * width,
					    'num_attention_heads': 1,
					    'num_vector_embeds': self.num_embed,
					    'num_embeds_ada_norm': self.num_embeds_ada_norm,
					    'norm_num_groups': 32,
					    'sample_size': width,
					    'activation_fn': 'geglu-approximate',
					}

					__snake_case      :							Union[str, Any]						=			TransformeraDModel(**_A)
					return model



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					__snake_case      :							Tuple						=			'cpu'

					__snake_case      :							List[str]						=			self.dummy_vqvae
					__snake_case      :							str						=			self.dummy_text_encoder
					__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
					__snake_case      :							Dict						=			self.dummy_transformer
					__snake_case      :							Optional[int]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_A)

					__snake_case      :							List[Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							List[Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Optional[Any]						=			'teddy bear playing in the pool'

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[int]						=			output.images

					__snake_case      :							int						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							str						=			image[0, -3:, -3:, -1]
					__snake_case      :							Union[str, Any]						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							str						=			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

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






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			'cpu'

					__snake_case      :							Optional[int]						=			self.dummy_vqvae
					__snake_case      :							List[str]						=			self.dummy_text_encoder
					__snake_case      :							Optional[int]						=			self.dummy_tokenizer
					__snake_case      :							Optional[Any]						=			self.dummy_transformer
					__snake_case      :							Union[str, Any]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							Optional[int]						=			LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_A     ,  hidden_size=self.text_embedder_hidden_size     ,  length=tokenizer.model_max_length)

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							Union[str, Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Union[str, Any]						=			'teddy bear playing in the pool'

					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[Any]						=			output.images

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Dict						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							int						=			image[0, -3:, -3:, -1]
					__snake_case      :							Tuple						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							Optional[Any]						=			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







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



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							List[Any]						=			load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
					__snake_case      :							Tuple						=			pipeline.to(_A)
					pipeline.set_progress_bar_config(disable=_A)

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							int						=			pipeline(
					    'teddy bear playing in the pool'     ,  num_images_per_prompt=1     ,  generator=_A     ,  output_type='np'     ,  )

					__snake_case      :							int						=			output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image).max() < 2.0





