



import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    Adafactor,
			    AdamW,
			    get_constant_schedule,
			    get_constant_schedule_with_warmup,
			    get_cosine_schedule_with_warmup,
			    get_cosine_with_hard_restarts_schedule_with_warmup,
			    get_inverse_sqrt_schedule,
			    get_linear_schedule_with_warmup,
			    get_polynomial_decay_schedule_with_warmup,
			)

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase=10   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     List[Any]						=				[]
					for _ in range(__lowerCAmelCase   ):
										lrs.append(scheduler.get_lr()[0]   )
										scheduler.step()
					return lrs

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase=10   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				[]
					for step in range(__lowerCAmelCase   ):
										lrs.append(scheduler.get_lr()[0]   )
										scheduler.step()
										if step == num_steps // 2:
															with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE							:     Dict						=				os.path.join(__lowerCAmelCase      ,	'schedule.bin'   )
																				torch.save(scheduler.state_dict()      ,	__lowerCAmelCase   )

																				SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.load(__lowerCAmelCase   )
																				scheduler.load_state_dict(__lowerCAmelCase   )
					return lrs


@require_torch
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				str       ,	snake_case    :				Tuple       ,	snake_case    :				str	):



											'''simple docstring'''


											self.assertEqual(len(snake_case	)       ,	len(snake_case	)	)
											for a, b in zip(snake_case       ,	snake_case	):
																self.assertAlmostEqual(snake_case       ,	snake_case       ,	delta=snake_case	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.tensor([0.1, -0.2, -0.1]       ,	requires_grad=snake_case	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.tensor([0.4, 0.2, -0.5]	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				nn.MSELoss()
											# No warmup, constant schedule, no gradient clipping
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				AdamW(params=[w]       ,	lr=2E-1       ,	weight_decay=0.0	)
											for _ in range(100	):
																SCREAMING_SNAKE_CASE							:     List[str]						=				criterion(snake_case       ,	snake_case	)
																loss.backward()
																optimizer.step()
																w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																w.grad.zero_()
											self.assertListAlmostEqual(w.tolist()       ,	[0.4, 0.2, -0.5]       ,	tol=1E-2	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.tensor([0.1, -0.2, -0.1]       ,	requires_grad=snake_case	)
											SCREAMING_SNAKE_CASE							:     int						=				torch.tensor([0.4, 0.2, -0.5]	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				nn.MSELoss()
											# No warmup, constant schedule, no gradient clipping
											SCREAMING_SNAKE_CASE							:     Tuple						=				Adafactor(
											    params=[w]       ,	lr=1E-2       ,	eps=(1E-30, 1E-3)       ,	clip_threshold=1.0       ,	decay_rate=-0.8       ,	betaa=snake_case       ,	weight_decay=0.0       ,	relative_step=snake_case       ,	scale_parameter=snake_case       ,	warmup_init=snake_case       ,	)
											for _ in range(1000	):
																SCREAMING_SNAKE_CASE							:     List[Any]						=				criterion(snake_case       ,	snake_case	)
																loss.backward()
																optimizer.step()
																w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
																w.grad.zero_()
											self.assertListAlmostEqual(w.tolist()       ,	[0.4, 0.2, -0.5]       ,	tol=1E-2	)


@require_torch
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[Any]		=		nn.Linear(50      ,    50) if is_torch_available() else None
						UpperCAmelCase			:		Optional[Any]		=		AdamW(m.parameters()      ,    lr=10.0) if is_torch_available() else None
						UpperCAmelCase			:		Tuple		=		10







						def       lowerCamelCase_				(  self    :				Dict       ,	snake_case    :				List[str]       ,	snake_case    :				Optional[Any]       ,	snake_case    :				int       ,	snake_case    :				Union[str, Any]=None	):



											'''simple docstring'''


											self.assertEqual(len(snake_case	)       ,	len(snake_case	)	)
											for a, b in zip(snake_case       ,	snake_case	):
																self.assertAlmostEqual(snake_case       ,	snake_case       ,	delta=snake_case       ,	msg=snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				{'num_warmup_steps': 2, 'num_training_steps': 10}
											# schedulers doct format
											# function: (sched_args_dict, expected_learning_rates)
											SCREAMING_SNAKE_CASE							:     List[str]						=				{
											    get_constant_schedule: ({}, [10.0] * self.num_steps),
											    get_constant_schedule_with_warmup: (
											        {'num_warmup_steps': 4},
											        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
											    ),
											    get_linear_schedule_with_warmup: (
											        {**common_kwargs},
											        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
											    ),
											    get_cosine_schedule_with_warmup: (
											        {**common_kwargs},
											        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
											    ),
											    get_cosine_with_hard_restarts_schedule_with_warmup: (
											        {**common_kwargs, 'num_cycles': 2},
											        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
											    ),
											    get_polynomial_decay_schedule_with_warmup: (
											        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
											        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
											    ),
											    get_inverse_sqrt_schedule: (
											        {'num_warmup_steps': 2},
											        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
											    ),
											}

											for scheduler_func, data in scheds.items():
																SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				data

																SCREAMING_SNAKE_CASE							:     Any						=				scheduler_func(self.optimizer       ,	**snake_case	)
																self.assertEqual(len([scheduler.get_lr()[0]]	)       ,	1	)
																SCREAMING_SNAKE_CASE							:     List[str]						=				unwrap_schedule(snake_case       ,	self.num_steps	)
																self.assertListAlmostEqual(
																    snake_case       ,	snake_case       ,	tol=1E-2       ,	msg=f'''failed for {scheduler_func} in normal scheduler'''       ,	)

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				scheduler_func(self.optimizer       ,	**snake_case	)
																if scheduler_func.__name__ != "get_constant_schedule":
																					LambdaScheduleWrapper.wrap_scheduler(snake_case	)  # wrap to test picklability of the schedule
																SCREAMING_SNAKE_CASE							:     List[str]						=				unwrap_and_save_reload_schedule(snake_case       ,	self.num_steps	)
																self.assertListEqual(snake_case       ,	snake_case       ,	msg=f'''failed for {scheduler_func} in save and reload'''	)


class 						lowercase    :
						'''simple docstring'''


						def __init__(  self    :				List[Any]       ,	snake_case    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				fn







						def __call__(  self    :				Union[str, Any]       ,	*snake_case    :				List[Any]       ,	**snake_case    :				List[Any]	):



											'''simple docstring'''


											return self.fn(*snake_case       ,	**snake_case	)







						@classmethod
						def       lowerCamelCase_				(  self    :				Dict       ,	snake_case    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				list(map(self       ,	scheduler.lr_lambdas	)	)




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

					# load LoRA weight from .safetensors
					SCREAMING_SNAKE_CASE							:     Any						=				load_file(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]

					# 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:
															SCREAMING_SNAKE_CASE							:     Dict						=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline.text_encoder
										else:
															SCREAMING_SNAKE_CASE							:     Any						=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Dict						=				pipeline.unet

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

										SCREAMING_SNAKE_CASE							:     str						=				[]
										if "lora_down" in key:
															pair_keys.append(key.replace('lora_down'      ,	'lora_up'   )   )
															pair_keys.append(__lowerCAmelCase   )
										else:
															pair_keys.append(__lowerCAmelCase   )
															pair_keys.append(key.replace('lora_up'      ,	'lora_down'   )   )

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

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

					return pipeline


if __name__ == "__main__":
			_lowerCamelCase    :		Tuple		      =    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.)""")

			_lowerCamelCase    :		List[Any]		      =    parser.parse_args()

			_lowerCamelCase    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)