"""simple docstring"""




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   StableDiffusionPipeline.from_pretrained(lowerCamelCase__			,  torch_dtype=torch.floataa     )

		# load LoRA weight from .safetensors
		lowerCAmelCase__        =   load_file(lowerCamelCase__     )

		lowerCAmelCase__        =   []

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

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

				lowerCAmelCase__        =   []
				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:
						lowerCAmelCase__        =   state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
						lowerCAmelCase__        =   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:
						lowerCAmelCase__        =   state_dict[pair_keys[0]].to(torch.floataa     )
						lowerCAmelCase__        =   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    :							Dict													=						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    :							Any													=						parser.parse_args()

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

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

			__lowerCAmelCase    :							str													=						pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





"""simple docstring"""




import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
		lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
				lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )

		set_param(
		    torch_layer.self_attention.query_key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   np.asarray(weights[0]     )
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		lowerCAmelCase__        =   np.asarray(weights[2]     )
		lowerCAmelCase__        =   np.asarray(weights[3]     )

		set_param(
		    torch_layer.self_attention.query			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   weights[0][0][0]
		lowerCAmelCase__        =   np.asarray(layer_norm_a[0]     )
		lowerCAmelCase__        =   np.asarray(layer_norm_a[1]     )
		set_param(
		    torch_block.attention.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# lsh weights + output
		lowerCAmelCase__        =   weights[0][1]
		if len(lowerCamelCase__     ) < 4:
				set_layer_weights_in_torch_lsh(lowerCamelCase__			,  torch_block.attention			,  lowerCamelCase__     )
		else:
				set_layer_weights_in_torch_local(lowerCamelCase__			,  torch_block.attention			,  lowerCamelCase__     )

		# intermediate weighs
		lowerCAmelCase__        =   weights[2][0][1][2]

		# Chunked Feed Forward
		if len(lowerCamelCase__     ) == 4:
				lowerCAmelCase__        =   intermediate_weights[2]

		# layernorm 2
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[0][1]     )
		set_param(
		    torch_block.feed_forward.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# intermediate dense
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[1][1]     )
		set_param(
		    torch_block.feed_forward.dense.dense			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )

		# intermediate out
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][1]     )
		set_param(
		    torch_block.feed_forward.output.dense			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   torch_model.reformer

		# word embeds
		lowerCAmelCase__        =   np.asarray(weights[1]     )
		set_param(
		    torch_model_reformer.embeddings.word_embeddings			,  torch.tensor(lowerCamelCase__     )			,  )

		if isinstance(weights[3]			,  lowerCamelCase__     ):
				lowerCAmelCase__        =   torch_model_reformer.embeddings.position_embeddings
				for emb_idx in range(len(position_embeddings.weights     )     ):
						lowerCAmelCase__        =   np.asarray(weights[3][emb_idx][0]     )
						assert (
						    position_embeddings.weights[emb_idx].shape == emb_weights.shape
						), f"""{position_embeddings[emb_idx]} emb does not match"""
						lowerCAmelCase__        =   nn.Parameter(torch.tensor(lowerCamelCase__     )     )

		lowerCAmelCase__        =   weights[5]
		assert len(torch_model_reformer.encoder.layers     ) * 4 == len(
		    lowerCamelCase__     ), "HF and trax model do not have the same number of layers"
		for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers     ):
				lowerCAmelCase__        =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
				set_block_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

		# output layer norm
		lowerCAmelCase__        =   np.asarray(weights[7][0]     )
		lowerCAmelCase__        =   np.asarray(weights[7][1]     )
		set_param(
		    torch_model_reformer.encoder.layer_norm			,  torch.tensor(lowerCamelCase__     )			,  torch.tensor(lowerCamelCase__     )			,  )

		# output embeddings
		lowerCAmelCase__        =   np.asarray(weights[9][0]     )
		lowerCAmelCase__        =   np.asarray(weights[9][1]     )
		set_param(
		    torch_model.lm_head.decoder			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   ReformerConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   ReformerModelWithLMHead(lowerCamelCase__     )

		with open(lowerCamelCase__			,  """rb"""     ) as f:
				lowerCAmelCase__        =   pickle.load(lowerCamelCase__     )["""weights"""]

		set_model_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  config.hidden_size     )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained Reformer model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




