


'''simple docstring'''



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def 				lowerCamelCase__				(	A       :      str				,       A       :      Tuple				,       A       :      Optional[Any]				,       A       :      Optional[int]				,       A       :      int					):




						'''simple docstring'''






						UpperCAmelCase					    =     StableDiffusionPipeline.from_pretrained(a_				,       torch_dtype=torch.floataa					)

						# load LoRA weight from .safetensors
						UpperCAmelCase					    =     load_file(a_					)

						UpperCAmelCase					    =     []

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

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

												UpperCAmelCase					    =     []
												if "lora_down" in key:
																		pair_keys.append(key.replace('''lora_down'''				,       '''lora_up'''					)					)
																		pair_keys.append(a_					)
												else:
																		pair_keys.append(a_					)
																		pair_keys.append(key.replace('''lora_up'''				,       '''lora_down'''					)					)

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

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

						return pipeline


if __name__ == "__main__":
					_lowercase      :      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.)""")

					_lowercase      :      Dict            =					parser.parse_args()

					_lowercase      :      int            =					args.base_model_path
					_lowercase      :      Optional[int]            =					args.checkpoint_path
					_lowercase      :      Union[str, Any]            =					args.dump_path
					_lowercase      :      List[Any]            =					args.lora_prefix_unet
					_lowercase      :      Union[str, Any]            =					args.lora_prefix_text_encoder
					_lowercase      :      List[str]            =					args.alpha

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

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









'''simple docstring'''



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
	import os

	import jax.numpy as jnp
	from jax import jit

	from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
	from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

	_lowercase      :      List[str]            =					"""0.12"""  # assumed parallelism: 8


if is_torch_available():
	import torch


def 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       A       :      List[Any]=None					):




						'''simple docstring'''






						if rng is None:
												UpperCAmelCase					    =     random.Random()

						UpperCAmelCase					    =     1
						for dim in shape:
												total_dims *= dim

						UpperCAmelCase					    =     []
						for _ in range(A					):
												values.append(rng.randint(0				,       vocab_size - 1					)					)

						UpperCAmelCase					    =     np.array(A				,       dtype=jnp.intaa					).reshape(A					)

						return output


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[int]=None					):




						'''simple docstring'''






						UpperCAmelCase					    =     ids_tensor(A				,       vocab_size=2				,       rng=A					)
						# make sure that at least one token is attended to for each batch
						UpperCAmelCase					    =     1
						return attn_mask






@require_flax
class 	UpperCamelCase__:
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[Any]  		=	()




				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										# cut to half length & take max batch_size 3
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     inputs['''input_ids'''].shape[-1] // 2
										UpperCAmelCase					    =     inputs['''input_ids'''][:max_batch_size, :sequence_length]

										UpperCAmelCase					    =     jnp.ones_like(lowerCAmelCase				)
										UpperCAmelCase					    =     attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										UpperCAmelCase					    =     input_ids.shape[-1] + 5
										if config.eos_token_id is not None and config.pad_token_id is None:
																# hack to allow generate for models such as GPT2 as is done in `generate()`
																UpperCAmelCase					    =     config.eos_token_id
										return config, input_ids, attention_mask, max_length




				@is_pt_flax_cross_test
				def 					a__(		self      :			Dict				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model_class.__name__[4:]  # Skip the "Flax" at the beginning
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     pt_model_class(lowerCAmelCase				).eval()
																UpperCAmelCase					    =     load_flax_weights_in_pytorch_model(lowerCAmelCase				,   flax_model.params				)

																UpperCAmelCase					    =     flax_model.generate(lowerCAmelCase				).sequences
																UpperCAmelCase					    =     pt_model.generate(torch.tensor(lowerCAmelCase				,   dtype=torch.long				)				)

																if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						UpperCAmelCase					    =     flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																self.assertListEqual(pt_generation_outputs.numpy().tolist()				,   flax_generation_outputs.tolist()				)




				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     2

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[0]				,   input_ids.shape[0] * config.num_return_sequences				)




				def 					a__(		self      :			Tuple				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     0.8
										UpperCAmelCase					    =     10
										UpperCAmelCase					    =     0.3
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()
										UpperCAmelCase					    =     max_length
										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     1
										UpperCAmelCase					    =     8
										UpperCAmelCase					    =     9

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Union[str, Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     False
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)




				def 					a__(		self      :			Optional[Any]				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)





				def 					a__(		self      :			Tuple				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     self._get_input_ids_and_config()

										# pad attention mask on the left
										UpperCAmelCase					    =     attention_mask.at[(0, 0)].set(0				)

										UpperCAmelCase					    =     2
										UpperCAmelCase					    =     max_length

										for model_class in self.all_generative_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)

																UpperCAmelCase					    =     model.generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences
																self.assertEqual(generation_outputs.shape[-1]				,   lowerCAmelCase				)

																UpperCAmelCase					    =     jit(model.generate				)
																UpperCAmelCase					    =     jit_generate(lowerCAmelCase				,   attention_mask=lowerCAmelCase				).sequences

																self.assertListEqual(generation_outputs.tolist()				,   jit_generation_outputs.tolist()				)







@require_flax
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			Union[str, Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
										UpperCAmelCase					    =     FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

										UpperCAmelCase					    =     '''Hello world'''
										UpperCAmelCase					    =     tokenizer(lowerCAmelCase				,   return_tensors='''np'''				).input_ids

										# typos are quickly detected (the correct argument is `do_sample`)
										with self.assertRaisesRegex(lowerCAmelCase				,   '''do_samples'''				):
																model.generate(lowerCAmelCase				,   do_samples=lowerCAmelCase				)

										# arbitrary arguments that will not be used anywhere are also not accepted
										with self.assertRaisesRegex(lowerCAmelCase				,   '''foo'''				):
																UpperCAmelCase					    =     {'''foo''': '''bar'''}
																model.generate(lowerCAmelCase				,   **lowerCAmelCase				)





