



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Tuple:







						"""simple docstring"""

						A__	:							List[Any]						    = StableDiffusionPipeline.from_pretrained(__A							,     torch_dtype=torch.floataa       )

						# load LoRA weight from .safetensors
						A__	:							str						    = load_file(__A       )

						A__	:							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:
																		A__	:							Union[str, Any]						    = key.split("."       )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"       )[-1].split("_"       )
																		A__	:							Any						    = pipeline.text_encoder
												else:
																		A__	:							Union[str, Any]						    = key.split("."       )[0].split(LORA_PREFIX_UNET + "_"       )[-1].split("_"       )
																		A__	:							Any						    = pipeline.unet

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

												A__	:							Optional[Any]						    = []
												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:
																		A__	:							int						    = state_dict[pair_keys[0]].squeeze(3       ).squeeze(2       ).to(torch.floataa       )
																		A__	:							Optional[Any]						    = 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:
																		A__	:							Tuple						    = state_dict[pair_keys[0]].to(torch.floataa       )
																		A__	:							Optional[Any]						    = 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__":
		snake_case    :						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=0.75, 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.)')

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

		snake_case    :						Tuple  =args.base_model_path
		snake_case    :						Dict  =args.checkpoint_path
		snake_case    :						Tuple  =args.dump_path
		snake_case    :						Dict  =args.lora_prefix_unet
		snake_case    :						List[Any]  =args.lora_prefix_text_encoder
		snake_case    :						Optional[Any]  =args.alpha

		snake_case    :						Optional[int]  =convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)