



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		int     ,__UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		Tuple ):
		# load base model
		lowerCAmelCase_			:     Optional[Any]									= StableDiffusionPipeline.from_pretrained(__UpperCamelCase     ,torch_dtype=torch.floataa )

		# load LoRA weight from .safetensors
		lowerCAmelCase_			:     str									= load_file(__UpperCamelCase )

		lowerCAmelCase_			:     List[str]									= []

		# 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:
						lowerCAmelCase_			:     Optional[Any]									= key.split('''.''' )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_''' )[-1].split('''_''' )
						lowerCAmelCase_			:     Optional[int]									= pipeline.text_encoder
				else:
						lowerCAmelCase_			:     List[str]									= key.split('''.''' )[0].split(LORA_PREFIX_UNET + '''_''' )[-1].split('''_''' )
						lowerCAmelCase_			:     Dict									= pipeline.unet

				# find the target layer
				lowerCAmelCase_			:     int									= layer_infos.pop(0 )
				while len(__UpperCamelCase ) > -1:
						try:
								lowerCAmelCase_			:     Tuple									= curr_layer.__getattr__(__UpperCamelCase )
								if len(__UpperCamelCase ) > 0:
										lowerCAmelCase_			:     Optional[int]									= layer_infos.pop(0 )
								elif len(__UpperCamelCase ) == 0:
										break
						except Exception:
								if len(__UpperCamelCase ) > 0:
										temp_name += "_" + layer_infos.pop(0 )
								else:
										lowerCAmelCase_			:     Optional[Any]									= layer_infos.pop(0 )

				lowerCAmelCase_			:     Union[str, Any]									= []
				if "lora_down" in key:
						pair_keys.append(key.replace('''lora_down'''     ,'''lora_up''' ) )
						pair_keys.append(__UpperCamelCase )
				else:
						pair_keys.append(__UpperCamelCase )
						pair_keys.append(key.replace('''lora_up'''     ,'''lora_down''' ) )

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

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

		return pipeline


if __name__ == "__main__":
						A__  :  List[Any]								=					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=0.7_5, 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__  :  Dict								=					parser.parse_args()

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

						A__  :  int								=					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)










import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''						,'''False'''						)						) is not True						,reason='''Skipping test because should only be run when releasing minor transformers version'''						,)
@pytest.mark.usefixtures('''sm_env'''						)
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       int):
									if self.framework == "pytorch":
											subprocess.run(
											    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()				,				encoding='''utf-8'''				,				check=A_				,				)
									assert hasattr(self				,				'''env''')




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       str=1):
									# creates estimator
									return HuggingFace(
									    entry_point=self.script				,				source_dir=self.env.test_path				,				role=self.env.role				,				image_uri=self.env.image_uri				,				base_job_name=F"""{self.env.base_job_name}-single"""				,				instance_count=A_				,				instance_type=self.instance_type				,				debugger_hook_config=A_				,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}				,				metric_definitions=self.env.metric_definitions				,				py_version='''py36'''				,				)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[Any]):
									TrainingJobAnalytics(A_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







							def    UpperCAmelCase__			(       self     :       int):
									# create estimator
									lowerCAmelCase_			:     List[Any]									= self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowerCAmelCase_			:     Union[str, Any]									= TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

									# extract kpis
									lowerCAmelCase_			:     Optional[Any]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
									lowerCAmelCase_			:     Optional[int]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowerCAmelCase_			:     Dict									= (
									    Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''				,				9_9_9_9_9_9)
									)

									# assert kpis
									assert train_runtime <= self.results["train_runtime"]
									assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy)
									assert all(t <= self.results['''eval_loss'''] for t in eval_loss)

									# dump tests result into json file to share in PR
									with open(F"""{estimator.latest_training_job.name}.json"""				,				'''w''') as outfile:
											json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}				,				A_)





