



"""simple docstring"""






import warnings

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


__snake_case							:     Dict   =   logging.get_logger(__name__)

__snake_case							:     Union[str, Any]   =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'mvp'
	SCREAMING_SNAKE_CASE					   =			['past_key_values']
	SCREAMING_SNAKE_CASE					   =			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

	def __init__(  self:	int ,    _SCREAMING_SNAKE_CASE:	Optional[int]=5_0267 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=1024 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	List[str]=4096 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=16 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	str=4096 ,    _SCREAMING_SNAKE_CASE:	List[str]=16 ,    _SCREAMING_SNAKE_CASE:	List[str]=0.0 ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    _SCREAMING_SNAKE_CASE:	List[Any]="gelu" ,    _SCREAMING_SNAKE_CASE:	int=1024 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=0.0 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.0 ,    _SCREAMING_SNAKE_CASE:	str=0.02 ,    _SCREAMING_SNAKE_CASE:	str=0.0 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=False ,    _SCREAMING_SNAKE_CASE:	List[Any]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=1 ,    _SCREAMING_SNAKE_CASE:	str=0 ,    _SCREAMING_SNAKE_CASE:	List[str]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=True ,    _SCREAMING_SNAKE_CASE:	Any=2 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Any=False ,    _SCREAMING_SNAKE_CASE:	Any=100 ,    _SCREAMING_SNAKE_CASE:	List[str]=800 ,    **_SCREAMING_SNAKE_CASE:	Tuple ,    )       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= vocab_size
				__lowerCAmelCase   :						Optional[int]				= max_position_embeddings
				__lowerCAmelCase   :						Dict				= d_model
				__lowerCAmelCase   :						Optional[int]				= encoder_ffn_dim
				__lowerCAmelCase   :						int				= encoder_layers
				__lowerCAmelCase   :						Dict				= encoder_attention_heads
				__lowerCAmelCase   :						Optional[Any]				= decoder_ffn_dim
				__lowerCAmelCase   :						List[Any]				= decoder_layers
				__lowerCAmelCase   :						Any				= decoder_attention_heads
				__lowerCAmelCase   :						int				= dropout
				__lowerCAmelCase   :						int				= attention_dropout
				__lowerCAmelCase   :						Dict				= activation_dropout
				__lowerCAmelCase   :						Any				= activation_function
				__lowerCAmelCase   :						Optional[Any]				= init_std
				__lowerCAmelCase   :						Optional[Any]				= encoder_layerdrop
				__lowerCAmelCase   :						Any				= decoder_layerdrop
				__lowerCAmelCase   :						Union[str, Any]				= classifier_dropout
				__lowerCAmelCase   :						int				= use_cache
				__lowerCAmelCase   :						Dict				= encoder_layers
				__lowerCAmelCase   :						Any				= scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase   :						Any				= use_prompt
				__lowerCAmelCase   :						Dict				= prompt_length
				__lowerCAmelCase   :						List[Any]				= prompt_mid_dim

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE ,    bos_token_id=_SCREAMING_SNAKE_CASE ,    eos_token_id=_SCREAMING_SNAKE_CASE ,    is_encoder_decoder=_SCREAMING_SNAKE_CASE ,    decoder_start_token_id=_SCREAMING_SNAKE_CASE ,    forced_eos_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE ,    )

				if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Any				= self.bos_token_id
							warnings.warn(
							    F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
							    "The config can simply be saved and uploaded again to be fixed.")




"""simple docstring"""






import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)


def     _lowercase       (						__snake_case			)		->     int:
			if hor == 128:
						__lowerCAmelCase   :						str				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						int				= (32, 128, 256)
						__lowerCAmelCase   :						Optional[Any]				= ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase   :						List[str]				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						Optional[Any]				= (32, 64, 128, 256)
						__lowerCAmelCase   :						Any				= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase   :						Union[str, Any]				= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""			)
			__lowerCAmelCase   :						List[Any]				= model.state_dict()
			__lowerCAmelCase   :						Optional[Any]				= {
			    "down_block_types": down_block_types,
			    "block_out_channels": block_out_channels,
			    "up_block_types": up_block_types,
			    "layers_per_block": 1,
			    "use_timestep_embedding": True,
			    "out_block_type": "OutConv1DBlock",
			    "norm_num_groups": 8,
			    "downsample_each_block": False,
			    "in_channels": 14,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "sample_size": 65_536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			__lowerCAmelCase   :						Dict				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)
			__lowerCAmelCase   :						Dict				= dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						int				= state_dict.pop(__snake_case			)
			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""			)
			with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


def     _lowercase       (						)		->     List[str]:
			__lowerCAmelCase   :						Union[str, Any]				= {
			    "in_channels": 14,
			    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
			    "up_block_types": (),
			    "out_block_type": "ValueFunction",
			    "mid_block_type": "ValueFunctionMidBlock1D",
			    "block_out_channels": (32, 64, 128, 256),
			    "layers_per_block": 1,
			    "downsample_each_block": True,
			    "sample_size": 65_536,
			    "out_channels": 14,
			    "extra_in_channels": 0,
			    "time_embedding_type": "positional",
			    "use_timestep_embedding": True,
			    "flip_sin_to_cos": False,
			    "freq_shift": 1,
			    "norm_num_groups": 8,
			    "act_fn": "mish",
			}

			__lowerCAmelCase   :						int				= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"			)
			__lowerCAmelCase   :						Any				= model
			__lowerCAmelCase   :						Optional[int]				= UNetaDModel(**__snake_case			)
			print(F"""length of state dict: {len(state_dict.keys()			)}"""			)
			print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()			)}"""			)

			__lowerCAmelCase   :						Any				= dict(zip(state_dict.keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						Union[str, Any]				= state_dict.pop(__snake_case			)

			hf_value_function.load_state_dict(__snake_case			)

			torch.save(hf_value_function.state_dict()      ,"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"			)
			with open("hub/hopper-medium-v2/value_function/config.json"      ,"w"			) as f:
						json.dump(__snake_case      ,__snake_case			)


if __name__ == "__main__":
	unet(32)
	# unet(128)
	value_function()