


import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


A			:						List[str]        =  logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm
	import torch_xla.debug.metrics as met



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Dict ,   *_UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any]=None ,   _UpperCAmelCase       :		Dict=None ,   _UpperCAmelCase       :		Optional[int]=None ,   **_UpperCAmelCase       :		List[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							super().__init__(*_UpperCAmelCase ,   **_UpperCAmelCase						)
							lowercase__	     =						eval_examples
							lowercase__	     =						post_process_function
							lowercase__	     =						quant_trainer_args
							lowercase__	     =						128  # default number of calibration samples

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Dict=None						)		->  List[Any]:




							"""simple docstring"""


							if calib_dataset is None and self.calib_dataset is None:
									raise ValueError("""Trainer: calibration requires an calib_dataset."""						)
							lowercase__	     =						calib_dataset if calib_dataset is not None else self.calib_dataset

							lowercase__	     =						self._remove_unused_columns(_UpperCAmelCase ,   description="""Calibration"""						)

							return DataLoader(
							    _UpperCAmelCase ,   batch_size=self.args.eval_batch_size ,   collate_fn=self.data_collator ,   drop_last=self.args.dataloader_drop_last ,   num_workers=self.args.dataloader_num_workers ,   pin_memory=self.args.dataloader_pin_memory ,   shuffle=_UpperCAmelCase ,   )

					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		Dict=None						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.train_dataset if calib_dataset is None else calib_dataset
							lowercase__	     =						self.get_calib_dataloader(_UpperCAmelCase						)

							lowercase__	     =						self.model
							quant_trainer.configure_model(_UpperCAmelCase ,   self.quant_trainer_args ,   calib=_UpperCAmelCase						)
							model.eval()
							quant_trainer.enable_calibration(_UpperCAmelCase						)

							logger.info("""***** Running calibration *****"""						)
							logger.info(f'''  Num examples = {self.calib_num}'''						)
							logger.info(f'''  Batch size = {calib_dataloader.batch_size}'''						)

							for step, inputs in enumerate(_UpperCAmelCase						):
									# Prediction step
									lowercase__     ,      lowercase__     ,      lowercase__	     =						self.prediction_step(_UpperCAmelCase ,   _UpperCAmelCase ,   prediction_loss_only=_UpperCAmelCase						)
									if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
											break

							quant_trainer.finish_calibration(_UpperCAmelCase ,   self.quant_trainer_args						)
							lowercase__	     =						model

					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		str=None ,   _UpperCAmelCase       :		Optional[int]=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		str = "eval"						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						self.eval_dataset if eval_dataset is None else eval_dataset
							lowercase__	     =						self.get_eval_dataloader(_UpperCAmelCase						)
							lowercase__	     =						self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							lowercase__	     =						self.compute_metrics
							lowercase__	     =						None
							lowercase__	     =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
									lowercase__	     =						eval_loop(
									    _UpperCAmelCase ,   description="""Evaluation""" ,   prediction_loss_only=True if compute_metrics is None else None ,   ignore_keys=_UpperCAmelCase ,   )
							finally:
									lowercase__	     =						compute_metrics

							if self.post_process_function is not None and self.compute_metrics is not None:
									lowercase__	     =						self.post_process_function(_UpperCAmelCase ,   _UpperCAmelCase ,   output.predictions						)
									lowercase__	     =						self.compute_metrics(_UpperCAmelCase						)

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys()						):
											if not key.startswith(f'''{metric_key_prefix}_'''						):
													lowercase__	     =						metrics.pop(_UpperCAmelCase						)

									self.log(_UpperCAmelCase						)
							else:
									lowercase__	     =						{}

							if self.args.tpu_metrics_debug or self.args.debug:
									# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
									xm.master_print(met.metrics_report()						)

							lowercase__	     =						self.callback_handler.on_evaluate(self.args ,   self.state ,   self.control ,   _UpperCAmelCase						)
							return metrics

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any=None ,   _UpperCAmelCase       :		str = "test"						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.get_test_dataloader(_UpperCAmelCase						)

							# Temporarily disable metric computation, we will do it in the loop here.
							lowercase__	     =						self.compute_metrics
							lowercase__	     =						None
							lowercase__	     =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
									lowercase__	     =						eval_loop(
									    _UpperCAmelCase ,   description="""Prediction""" ,   prediction_loss_only=True if compute_metrics is None else None ,   ignore_keys=_UpperCAmelCase ,   )
							finally:
									lowercase__	     =						compute_metrics

							if self.post_process_function is None or self.compute_metrics is None:
									return output

							lowercase__	     =						self.post_process_function(_UpperCAmelCase ,   _UpperCAmelCase ,   output.predictions ,   """predict"""						)
							lowercase__	     =						self.compute_metrics(_UpperCAmelCase						)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()						):
									if not key.startswith(f'''{metric_key_prefix}_'''						):
											lowercase__	     =						metrics.pop(_UpperCAmelCase						)

							return PredictionOutput(predictions=predictions.predictions ,   label_ids=predictions.label_ids ,   metrics=_UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		List[str]="./"						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.eval_dataset
							lowercase__	     =						self.get_eval_dataloader(_UpperCAmelCase						)

							lowercase__	     =						next(iter(_UpperCAmelCase						)						)

							# saving device - to make it consistent
							lowercase__	     =						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)

							# convert to tuple
							lowercase__	     =						tuple(v.to(_UpperCAmelCase						) for k, v in batch.items()						)

							logger.info("""Converting model to be onnx compatible"""						)
							from pytorch_quantization.nn import TensorQuantizer

							lowercase__	     =						True

							lowercase__	     =						self.model.to(_UpperCAmelCase						)

							model.eval()
							model.float()

							lowercase__	     =						model.module if hasattr(_UpperCAmelCase ,   """module"""						) else model
							quant_trainer.configure_model(_UpperCAmelCase ,   self.quant_trainer_args						)

							lowercase__	     =						os.path.join(_UpperCAmelCase ,   """model.onnx"""						)
							logger.info(f'''exporting model to {output_model_file}'''						)

							lowercase__	     =						{0: """batch_size""", 1: """seq_len"""}

							torch.onnx.export(
							    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   export_params=_UpperCAmelCase ,   opset_version=13 ,   do_constant_folding=_UpperCAmelCase ,   input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] ,   output_names=["""output_start_logits""", """output_end_logits"""] ,   dynamic_axes={
							        """input_ids""": axes,
							        """attention_mask""": axes,
							        """token_type_ids""": axes,
							        """output_start_logits""": axes,
							        """output_end_logits""": axes,
							    } ,   verbose=_UpperCAmelCase ,   )
							logger.info("""onnx export finished"""						)





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		lowercase__	     =						[]

		# directly update weight in diffusers model
		for key in state_dict:
				# it is suggested to print out the key, it usually will be something like below
				# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

				# as we have set the alpha beforehand, so just skip
				if ".alpha" in key or key in visited:
						continue

				if "text" in key:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.text_encoder
				else:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.unet

				# find the target layer
				lowercase__	     =						layer_infos.pop(0	)
				while len(__magic_name__	) > -1:
						try:
								lowercase__	     =						curr_layer.__getattr__(__magic_name__	)
								if len(__magic_name__	) > 0:
										lowercase__	     =						layer_infos.pop(0	)
								elif len(__magic_name__	) == 0:
										break
						except Exception:
								if len(__magic_name__	) > 0:
										temp_name += "_" + layer_infos.pop(0	)
								else:
										lowercase__	     =						layer_infos.pop(0	)

				lowercase__	     =						[]
				if "lora_down" in key:
						pair_keys.append(key.replace("""lora_down"""       ,       """lora_up"""	)	)
						pair_keys.append(__magic_name__	)
				else:
						pair_keys.append(__magic_name__	)
						pair_keys.append(key.replace("""lora_up"""       ,       """lora_down"""	)	)

				# update weight
				if len(state_dict[pair_keys[0]].shape	) == 4:
						lowercase__	     =						state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	).unsqueeze(2	).unsqueeze(3	)
				else:
						lowercase__	     =						state_dict[pair_keys[0]].to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	)

				# update visited list
				for item in pair_keys:
						visited.append(__magic_name__	)

		return pipeline


if __name__ == "__main__":
	A			:						int        =  argparse.ArgumentParser()

	parser.add_argument(
	    '--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
	)
	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)
	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	parser.add_argument(
	    '--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
	)
	parser.add_argument(
	    '--lora_prefix_text_encoder',
	    default='lora_te',
	    type=str,
	    help='The prefix of text encoder weight in safetensors',
	)
	parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
	parser.add_argument(
	    '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
	)
	parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

	A			:						str        =  parser.parse_args()

	A			:						Tuple        =  args.base_model_path
	A			:						List[str]        =  args.checkpoint_path
	A			:						Optional[int]        =  args.dump_path
	A			:						Optional[int]        =  args.lora_prefix_unet
	A			:						Any        =  args.lora_prefix_text_encoder
	A			:						Any        =  args.alpha

	A			:						List[str]        =  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

