




'''simple docstring'''

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


a       :						Tuple								=		re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')



@total_ordering
@dataclass
class 		SCREAMING_SNAKE_CASE__  :
			__SCREAMING_SNAKE_CASE    =			42
			__SCREAMING_SNAKE_CASE    =			None
			__SCREAMING_SNAKE_CASE    =			None
			__SCREAMING_SNAKE_CASE    =			None
			__SCREAMING_SNAKE_CASE    =			None





			def 							A (      self						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case		,     __snake_case		,     __snake_case			=		_str_to_version_tuple(self.version_str							)





			def __repr__(      self						:    Tuple							):



						"""simple docstring"""




						return f'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''





			@property
			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						return self.major, self.minor, self.patch





			def 							A (      self						:    Tuple      ,		a_						:    str							):



						"""simple docstring"""




						if isinstance(a_      ,		a_							):
									return Version(a_							)
						elif isinstance(a_      ,		a_							):
									return other
						raise TypeError(f'''{other} (type {type(a_							)}) cannot be compared to version.'''							)





			def __eq__(      self						:    str      ,		a_						:    Tuple							):



						"""simple docstring"""




						try:
									__snake_case			=		self._validate_operand(a_							)
						except (TypeError, ValueError):
									return False
						else:
									return self.tuple == other.tuple





			def __lt__(      self						:    Optional[Any]      ,		a_						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		self._validate_operand(a_							)
						return self.tuple < other.tuple





			def __hash__(      self						:    Union[str, Any]							):



						"""simple docstring"""




						return hash(_version_tuple_to_str(self.tuple							)							)





			@classmethod
			def 							A (      cls						:    Optional[int]      ,		a_						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		{f.name for f in dataclasses.fields(cls							)}
						return cls(**{k: v for k, v in dic.items() if k in field_names}							)






			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						return self.version_str







def __UpperCAmelCase	(   _UpperCAmelCase		:   List[Any]						)      ->      List[Any]:
			__snake_case			=		_VERSION_REG.match(_UpperCAmelCase						)
			if not res:
						raise ValueError(F'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'''						)
			return tuple(int(_UpperCAmelCase						) for v in [res.group("major"						), res.group("minor"						), res.group("patch"						)]						)






def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]						)      ->      List[Any]:
			return ".".join(str(_UpperCAmelCase						) for v in version_tuple						)








"""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       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Optional[Any]:
 if hor == 1_2_8:
  lowerCAmelCase__   :				str 			=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  lowerCAmelCase__   :				int 			=				(3_2, 1_2_8, 2_5_6)
  lowerCAmelCase__   :				Optional[Any] 			=				("UpResnetBlock1D", "UpResnetBlock1D")

 elif hor == 3_2:
  lowerCAmelCase__   :				Any 			=				("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
  lowerCAmelCase__   :				Dict 			=				(3_2, 6_4, 1_2_8, 2_5_6)
  lowerCAmelCase__   :				Optional[Any] 			=				("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
 lowerCAmelCase__   :				Tuple 			=				torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""   )
 lowerCAmelCase__   :				str 			=				model.state_dict()
 lowerCAmelCase__   :				Union[str, 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": 1_4,
     "out_channels": 1_4,
     "extra_in_channels": 0,
     "time_embedding_type": "positional",
     "flip_sin_to_cos": False,
     "freq_shift": 1,
     "sample_size": 6_5_5_3_6,
     "mid_block_type": "MidResTemporalBlock1D",
     "act_fn": "mish",
 }
 lowerCAmelCase__   :				str 			=				UNetaDModel(**lowercase__   )
 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__   :				Tuple 			=				state_dict.pop(lowercase__   )
 hf_value_function.load_state_dict(lowercase__   )

 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(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( )			->						Any:
 lowerCAmelCase__   :				Dict 			=				{
     "in_channels": 1_4,
     "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
     "up_block_types": (),
     "out_block_type": "ValueFunction",
     "mid_block_type": "ValueFunctionMidBlock1D",
     "block_out_channels": (3_2, 6_4, 1_2_8, 2_5_6),
     "layers_per_block": 1,
     "downsample_each_block": True,
     "sample_size": 6_5_5_3_6,
     "out_channels": 1_4,
     "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__   :				Any 			=				torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"   )
 lowerCAmelCase__   :				Optional[int] 			=				model
 lowerCAmelCase__   :				Any 			=				UNetaDModel(**lowercase__   )
 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__   :				str 			=				dict(zip(state_dict.keys()					,					hf_value_function.state_dict().keys()   )   )
 for k, v in mapping.items():
  lowerCAmelCase__   :				List[Any] 			=				state_dict.pop(lowercase__   )

 hf_value_function.load_state_dict(lowercase__   )

 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(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    unet(3_2)
    # unet(128)
    value_function()






