






"""simple docstring"""

import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase       )						->    Any:

		"""simple docstring"""







		lowerCAmelCase_   :							List[str]									=			checkpoint

		lowerCAmelCase_   :							Optional[int]									=			{}

		lowerCAmelCase_   :							Any									=			vae_state_dict["""encoder.conv_in.weight"""]
		lowerCAmelCase_   :							Union[str, Any]									=			vae_state_dict["""encoder.conv_in.bias"""]
		lowerCAmelCase_   :							Optional[int]									=			vae_state_dict["""encoder.conv_out.weight"""]
		lowerCAmelCase_   :							str									=			vae_state_dict["""encoder.conv_out.bias"""]
		lowerCAmelCase_   :							str									=			vae_state_dict["""encoder.norm_out.weight"""]
		lowerCAmelCase_   :							Optional[int]									=			vae_state_dict["""encoder.norm_out.bias"""]

		lowerCAmelCase_   :							Optional[int]									=			vae_state_dict["""decoder.conv_in.weight"""]
		lowerCAmelCase_   :							int									=			vae_state_dict["""decoder.conv_in.bias"""]
		lowerCAmelCase_   :							str									=			vae_state_dict["""decoder.conv_out.weight"""]
		lowerCAmelCase_   :							Union[str, Any]									=			vae_state_dict["""decoder.conv_out.bias"""]
		lowerCAmelCase_   :							Optional[Any]									=			vae_state_dict["""decoder.norm_out.weight"""]
		lowerCAmelCase_   :							List[Any]									=			vae_state_dict["""decoder.norm_out.bias"""]

		lowerCAmelCase_   :							List[str]									=			vae_state_dict["""quant_conv.weight"""]
		lowerCAmelCase_   :							Any									=			vae_state_dict["""quant_conv.bias"""]
		lowerCAmelCase_   :							int									=			vae_state_dict["""post_quant_conv.weight"""]
		lowerCAmelCase_   :							List[Any]									=			vae_state_dict["""post_quant_conv.bias"""]

		# Retrieves the keys for the encoder down blocks only
		lowerCAmelCase_   :							List[str]									=			len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "encoder.down" in layer}       )
		lowerCAmelCase_   :							Optional[int]									=			{
		    layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(__UpperCamelCase       )
		}

		# Retrieves the keys for the decoder up blocks only
		lowerCAmelCase_   :							Optional[int]									=			len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "decoder.up" in layer}       )
		lowerCAmelCase_   :							Optional[Any]									=			{
		    layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(__UpperCamelCase       )
		}

		for i in range(__UpperCamelCase       ):
				lowerCAmelCase_   :							Tuple									=			[key for key in down_blocks[i] if f'''down.{i}''' in key and f'''down.{i}.downsample''' not in key]

				if f'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
						lowerCAmelCase_   :							Any									=			vae_state_dict.pop(
						    f'''encoder.down.{i}.downsample.conv.weight'''       )
						lowerCAmelCase_   :							Union[str, Any]									=			vae_state_dict.pop(
						    f'''encoder.down.{i}.downsample.conv.bias'''       )

				lowerCAmelCase_   :							Any									=			renew_vae_resnet_paths(__UpperCamelCase       )
				lowerCAmelCase_   :							str									=			{"""old""": f'''down.{i}.block''', """new""": f'''down_blocks.{i}.resnets'''}
				assign_to_checkpoint(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       additional_replacements=[meta_path]      ,       config=__UpperCamelCase       )

		lowerCAmelCase_   :							Optional[Any]									=			[key for key in vae_state_dict if """encoder.mid.block""" in key]
		lowerCAmelCase_   :							Dict									=			2
		for i in range(1      ,       num_mid_res_blocks + 1       ):
				lowerCAmelCase_   :							int									=			[key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]

				lowerCAmelCase_   :							List[Any]									=			renew_vae_resnet_paths(__UpperCamelCase       )
				lowerCAmelCase_   :							str									=			{"""old""": f'''mid.block_{i}''', """new""": f'''mid_block.resnets.{i - 1}'''}
				assign_to_checkpoint(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       additional_replacements=[meta_path]      ,       config=__UpperCamelCase       )

		lowerCAmelCase_   :							List[Any]									=			[key for key in vae_state_dict if """encoder.mid.attn""" in key]
		lowerCAmelCase_   :							List[str]									=			renew_vae_attention_paths(__UpperCamelCase       )
		lowerCAmelCase_   :							List[str]									=			{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
		assign_to_checkpoint(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       additional_replacements=[meta_path]      ,       config=__UpperCamelCase       )
		conv_attn_to_linear(__UpperCamelCase       )

		for i in range(__UpperCamelCase       ):
				lowerCAmelCase_   :							Optional[int]									=			num_up_blocks - 1 - i
				lowerCAmelCase_   :							List[Any]									=			[
				    key for key in up_blocks[block_id] if f'''up.{block_id}''' in key and f'''up.{block_id}.upsample''' not in key
				]

				if f'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
						lowerCAmelCase_   :							str									=			vae_state_dict[
						    f'''decoder.up.{block_id}.upsample.conv.weight'''
						]
						lowerCAmelCase_   :							int									=			vae_state_dict[
						    f'''decoder.up.{block_id}.upsample.conv.bias'''
						]

				lowerCAmelCase_   :							int									=			renew_vae_resnet_paths(__UpperCamelCase       )
				lowerCAmelCase_   :							List[Any]									=			{"""old""": f'''up.{block_id}.block''', """new""": f'''up_blocks.{i}.resnets'''}
				assign_to_checkpoint(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       additional_replacements=[meta_path]      ,       config=__UpperCamelCase       )

		lowerCAmelCase_   :							Optional[int]									=			[key for key in vae_state_dict if """decoder.mid.block""" in key]
		lowerCAmelCase_   :							str									=			2
		for i in range(1      ,       num_mid_res_blocks + 1       ):
				lowerCAmelCase_   :							Optional[Any]									=			[key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]

				lowerCAmelCase_   :							List[str]									=			renew_vae_resnet_paths(__UpperCamelCase       )
				lowerCAmelCase_   :							int									=			{"""old""": f'''mid.block_{i}''', """new""": f'''mid_block.resnets.{i - 1}'''}
				assign_to_checkpoint(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       additional_replacements=[meta_path]      ,       config=__UpperCamelCase       )

		lowerCAmelCase_   :							List[Any]									=			[key for key in vae_state_dict if """decoder.mid.attn""" in key]
		lowerCAmelCase_   :							Optional[Any]									=			renew_vae_attention_paths(__UpperCamelCase       )
		lowerCAmelCase_   :							Union[str, Any]									=			{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
		assign_to_checkpoint(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       additional_replacements=[meta_path]      ,       config=__UpperCamelCase       )
		conv_attn_to_linear(__UpperCamelCase       )
		return new_checkpoint

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       )						->    Optional[int]:

		"""simple docstring"""







		lowerCAmelCase_   :							Tuple									=			requests.get(
		    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"       )
		lowerCAmelCase_   :							int									=			io.BytesIO(r.content       )

		lowerCAmelCase_   :							str									=			OmegaConf.load(__UpperCamelCase       )
		lowerCAmelCase_   :							str									=			512
		lowerCAmelCase_   :							List[Any]									=			"""cuda""" if torch.cuda.is_available() else """cpu"""
		if checkpoint_path.endswith("safetensors"       ):
				from safetensors import safe_open

				lowerCAmelCase_   :							List[str]									=			{}
				with safe_open(__UpperCamelCase      ,       framework="pt"      ,       device="cpu"       ) as f:
						for key in f.keys():
								lowerCAmelCase_   :							List[str]									=			f.get_tensor(__UpperCamelCase       )
		else:
				lowerCAmelCase_   :							Optional[int]									=			torch.load(__UpperCamelCase      ,       map_location=__UpperCamelCase       )["""state_dict"""]

		# Convert the VAE model.
		lowerCAmelCase_   :							Union[str, Any]									=			create_vae_diffusers_config(__UpperCamelCase      ,       image_size=__UpperCamelCase       )
		lowerCAmelCase_   :							Union[str, Any]									=			custom_convert_ldm_vae_checkpoint(__UpperCamelCase      ,       __UpperCamelCase       )

		lowerCAmelCase_   :							str									=			AutoencoderKL(**__UpperCamelCase       )
		vae.load_state_dict(__UpperCamelCase       )
		vae.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
					lowercase__          = argparse.ArgumentParser()

					parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

					lowercase__          = parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)








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


UpperCAmelCase_ :						List[str]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						int      =      {
    '''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     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """trajectory_transformer"""
	snake_case__							:     Optional[Any]     	=  ["""past_key_values"""]
	snake_case__							:     Tuple     	=  {
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any=100 ,  __lowerCamelCase				:					str=5 ,  __lowerCamelCase				:					str=1 ,  __lowerCamelCase				:					Optional[int]=1 ,  __lowerCamelCase				:					int=249 ,  __lowerCamelCase				:					str=6 ,  __lowerCamelCase				:					Dict=17 ,  __lowerCamelCase				:					Optional[Any]=25 ,  __lowerCamelCase				:					List[str]=4 ,  __lowerCamelCase				:					str=4 ,  __lowerCamelCase				:					Tuple=128 ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					str=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					int=0.0006 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Any=1E-12 ,  __lowerCamelCase				:					int=1 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple=1 ,  __lowerCamelCase				:					int=50_256 ,  __lowerCamelCase				:					Union[str, Any]=50_256 ,  **__lowerCamelCase				:					Dict ,  ):
		UpperCamelCase     :Dict          =	vocab_size
		UpperCamelCase     :int          =	action_weight
		UpperCamelCase     :Tuple          =	reward_weight
		UpperCamelCase     :str          =	value_weight
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Tuple          =	block_size
		UpperCamelCase     :Optional[int]          =	action_dim
		UpperCamelCase     :int          =	observation_dim
		UpperCamelCase     :List[str]          =	transition_dim
		UpperCamelCase     :List[Any]          =	learning_rate
		UpperCamelCase     :Optional[Any]          =	n_layer
		UpperCamelCase     :Any          =	n_head
		UpperCamelCase     :List[str]          =	n_embd
		UpperCamelCase     :Any          =	embd_pdrop
		UpperCamelCase     :str          =	attn_pdrop
		UpperCamelCase     :Union[str, Any]          =	resid_pdrop
		UpperCamelCase     :Optional[Any]          =	initializer_range
		UpperCamelCase     :List[Any]          =	layer_norm_eps
		UpperCamelCase     :Optional[int]          =	kaiming_initializer_range
		UpperCamelCase     :Tuple          =	use_cache
		super().__init__(pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase	)
