



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


UpperCAmelCase :				str							=get_logger(__name__)



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						):
					# Model states
					for i, state in enumerate(_lowerCAmelCase):
										UpperCamelCase_			= f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										save(_lowerCAmelCase ,						_lowerCAmelCase)
										logger.info(f"""Model weights saved in {output_model_file}""")
					# Optimizer states
					for i, opt in enumerate(_lowerCAmelCase):
										UpperCamelCase_			= opt.state_dict()
										UpperCamelCase_			= f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										save(_lowerCAmelCase ,						_lowerCAmelCase)
										logger.info(f"""Optimizer state saved in {output_optimizer_file}""")
					# Scheduler states
					for i, scheduler in enumerate(_lowerCAmelCase):
										UpperCamelCase_			= scheduler.state_dict()
										UpperCamelCase_			= f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										save(_lowerCAmelCase ,						_lowerCAmelCase)
										logger.info(f"""Scheduler state saved in {output_scheduler_file}""")
					# GradScaler state
					if scaler is not None:
										UpperCamelCase_			= scaler.state_dict()
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										torch.save(_lowerCAmelCase ,						_lowerCAmelCase)
										logger.info(f"""Gradient scaler state saved in {output_scaler_file}""")
					# Random number generator states
					UpperCamelCase_			= {}
					UpperCamelCase_			= f"""{RNG_STATE_NAME}_{process_index}.pkl"""
					UpperCamelCase_			= random.getstate()
					UpperCamelCase_			= np.random.get_state()
					UpperCamelCase_			= torch.get_rng_state()
					if is_xpu_available():
										UpperCamelCase_			= torch.xpu.get_rng_state_all()
					else:
										UpperCamelCase_			= torch.cuda.get_rng_state_all()
					if is_tpu_available():
										UpperCamelCase_			= xm.get_rng_state()
					UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
					torch.save(_lowerCAmelCase ,						_lowerCAmelCase)
					logger.info(f"""Random states saved in {output_states_file}""")
					return output_dir



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						**_lowerCAmelCase ,						):
					if map_location not in [None, "cpu", "on_device"]:
										raise TypeError(
										    "Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`")
					if map_location is None:
										UpperCamelCase_			= "cpu"
					elif map_location == "on_device":
										UpperCamelCase_			= PartialState().device
					# Model states
					for i, model in enumerate(_lowerCAmelCase):
										UpperCamelCase_			= f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										models[i].load_state_dict(torch.load(_lowerCAmelCase ,						map_location=_lowerCAmelCase) ,						**_lowerCAmelCase)
					logger.info("All model weights loaded successfully")

					# Optimizer states
					for i, opt in enumerate(_lowerCAmelCase):
										UpperCamelCase_			= f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										UpperCamelCase_			= torch.load(_lowerCAmelCase ,						map_location=_lowerCAmelCase)
										optimizers[i].load_state_dict(_lowerCAmelCase)
					logger.info("All optimizer states loaded successfully")

					# Scheduler states
					for i, scheduler in enumerate(_lowerCAmelCase):
										UpperCamelCase_			= f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										scheduler.load_state_dict(torch.load(_lowerCAmelCase))
					logger.info("All scheduler states loaded successfully")

					# GradScaler state
					if scaler is not None:
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										scaler.load_state_dict(torch.load(_lowerCAmelCase))
										logger.info("GradScaler state loaded successfully")

					# Random states
					try:
										UpperCamelCase_			= torch.load(os.path.join(_lowerCAmelCase ,						f"""{RNG_STATE_NAME}_{process_index}.pkl"""))
										random.setstate(states["random_state"])
										np.random.set_state(states["numpy_random_seed"])
										torch.set_rng_state(states["torch_manual_seed"])
										if is_xpu_available():
															torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"])
										else:
															torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"])
										if is_tpu_available():
															xm.set_rng_state(states["xm_seed"])
										logger.info("All random states loaded successfully")
					except Exception:
										logger.info("Could not load random states")



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = 0):
					UpperCamelCase_			= Path(_lowerCAmelCase) / f"""custom_checkpoint_{index}.pkl"""
					logger.info(f"""Saving the state of {get_pretty_name(_lowerCAmelCase)} to {save_location}""")
					torch.save(obj.state_dict() ,						_lowerCAmelCase)






def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = 0):
					UpperCamelCase_			= f"""{path}/custom_checkpoint_{index}.pkl"""
					logger.info(f"""Loading the state of {get_pretty_name(_lowerCAmelCase)} from {load_location}""")
					obj.load_state_dict(torch.load(_lowerCAmelCase ,						map_location="cpu"))


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

		A				=checkpoint[list(checkpoint.keys()		)[-4]].data
		A				=checkpoint[list(checkpoint.keys()		)[-3]].data
		A				=checkpoint[list(checkpoint.keys()		)[-2]].data
		A				=checkpoint[list(checkpoint.keys()		)[-1]].data

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

				if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
						if has_attentions:
								set_attention_weights(block.attentions[j]  ,				a_  ,				a_		)
								module_index += 1

				if hasattr(a_  ,				"downsamplers"		) and block.downsamplers is not None:
						set_resnet_weights(block.resnet_down  ,				a_  ,				a_		)
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
						module_index += 1

		set_resnet_weights(new_model_architecture.mid_block.resnets[0]  ,				a_  ,				a_		)
		module_index += 1
		set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,				a_  ,				a_		)
		module_index += 1
		set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,				a_  ,				a_		)
		module_index += 1

		for i, block in enumerate(new_model_architecture.up_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
				if has_attentions:
						set_attention_weights(
						    block.attentions[0]  ,				a_  ,				a_		)  # why can there only be a single attention layer for up?
						module_index += 1

				if hasattr(a_  ,				"resnet_up"		) and block.resnet_up is not None:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
	    type=str,
	    required=False,
	    help="""Path to the checkpoint to convert.""",
	)

	parser.add_argument(
	    """--config_file""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
	    type=str,
	    required=False,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument(
	    """--dump_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
	    type=str,
	    required=False,
	    help="""Path to the output model.""",
	)

	__a							  =						parser.parse_args()

	__a							  =						torch.load(args.checkpoint_path, map_location="""cpu""")

	with open(args.config_file) as f:
		__a							  =						json.loads(f.read())

	__a							  =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	__a							  =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__a							  =						ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		__a							  =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)




