



from ....configuration_utils import PretrainedConfig
from ....utils import logging


_lowerCamelCase    :		Optional[int]		      =    logging.get_logger(__name__)

_lowerCamelCase    :		Any		      =    {
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		List[str]		=		'trajectory_transformer'
						UpperCAmelCase			:		int		=		['past_key_values']
						UpperCAmelCase			:		Dict		=		{
						    'hidden_size': 'n_embd',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}







						def __init__(  self    :				List[Any]       ,	snake_case    :				List[Any]=100       ,	snake_case    :				Union[str, Any]=5       ,	snake_case    :				int=1       ,	snake_case    :				Optional[Any]=1       ,	snake_case    :				int=249       ,	snake_case    :				Optional[Any]=6       ,	snake_case    :				Dict=17       ,	snake_case    :				str=25       ,	snake_case    :				Dict=4       ,	snake_case    :				Dict=4       ,	snake_case    :				List[Any]=128       ,	snake_case    :				List[str]=0.1       ,	snake_case    :				int=0.1       ,	snake_case    :				List[str]=0.1       ,	snake_case    :				Optional[Any]=0.0006       ,	snake_case    :				Tuple=512       ,	snake_case    :				Optional[int]=0.02       ,	snake_case    :				Optional[int]=1E-12       ,	snake_case    :				Any=1       ,	snake_case    :				List[Any]=True       ,	snake_case    :				Tuple=1       ,	snake_case    :				Optional[int]=50256       ,	snake_case    :				List[Any]=50256       ,	**snake_case    :				str       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     int						=				action_weight
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				reward_weight
											SCREAMING_SNAKE_CASE							:     Tuple						=				value_weight
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     int						=				block_size
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				action_dim
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				observation_dim
											SCREAMING_SNAKE_CASE							:     int						=				transition_dim
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				learning_rate
											SCREAMING_SNAKE_CASE							:     List[Any]						=				n_layer
											SCREAMING_SNAKE_CASE							:     int						=				n_head
											SCREAMING_SNAKE_CASE							:     Tuple						=				n_embd
											SCREAMING_SNAKE_CASE							:     str						=				embd_pdrop
											SCREAMING_SNAKE_CASE							:     Dict						=				attn_pdrop
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				resid_pdrop
											SCREAMING_SNAKE_CASE							:     str						=				initializer_range
											SCREAMING_SNAKE_CASE							:     Tuple						=				layer_norm_eps
											SCREAMING_SNAKE_CASE							:     List[str]						=				kaiming_initializer_range
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				use_cache
											super().__init__(pad_token_id=snake_case       ,	bos_token_id=snake_case       ,	eos_token_id=snake_case       ,	**snake_case	)




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)