
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='bit'
			lowerCamelCase__           =['preactivation', 'bottleneck']
			lowerCamelCase__           =['SAME', 'VALID']
			def __init__(			self							: Union[str, Any]      ,      a							: Dict=3      ,      a							: Any=64      ,      a							: Optional[Any]=[256, 512, 1024, 2048]      ,      a							: List[str]=[3, 4, 6, 3]      ,      a							: str="preactivation"      ,      a							: int="relu"      ,      a							: int=None      ,      a							: str=32      ,      a							: Any=0.0      ,      a							: Tuple=False      ,      a							: int=32      ,      a							: Dict=1      ,      a							: Any=None      ,      a							: Optional[Any]=None      ,      **a							: Any      ,      )							->       List[Any]:

				"""simple docstring"""

				super().__init__(**a	)
				if layer_type not in self.layer_types:
					raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types	)}"	)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       global_padding.upper()
					else:
						raise ValueError(F"Padding strategy {global_padding} not supported"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       embedding_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_sizes
				SCREAMING_SNAKE_CASE   :      Dict				     =       depths
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer_type
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Tuple				     =       global_padding
				SCREAMING_SNAKE_CASE   :      str				     =       num_groups
				SCREAMING_SNAKE_CASE   :      int				     =       drop_path_rate
				SCREAMING_SNAKE_CASE   :      str				     =       embedding_dynamic_padding
				SCREAMING_SNAKE_CASE   :      Tuple				     =       output_stride
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       width_factor

				SCREAMING_SNAKE_CASE   :      int				     =       ["stem"] + [F"stage{idx}" for idx in range(1      ,      len(a	) + 1	)]
				SCREAMING_SNAKE_CASE   :      str				     =       get_aligned_output_features_output_indices(
				    out_features=a      ,      out_indices=a      ,      stage_names=self.stage_names	)


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  lowerCamelCase__ (					_a):
	if hor == 128:
		SCREAMING_SNAKE_CASE   :      str				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (32, 128, 256)
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ("UpResnetBlock1D", "UpResnetBlock1D")

	elif hor == 32:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (32, 64, 128, 256)
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
	SCREAMING_SNAKE_CASE   :      int				     =       model.state_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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": 65536,
	    "mid_block_type": "MidResTemporalBlock1D",
	    "act_fn": "mish",
	}
	SCREAMING_SNAKE_CASE   :      str				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
	SCREAMING_SNAKE_CASE   :      List[str]				     =       dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      int				     =       state_dict.pop(_a)
	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       {
	    "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": 65536,
	    "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",
	}

	SCREAMING_SNAKE_CASE   :      Dict				     =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
	SCREAMING_SNAKE_CASE   :      Dict				     =       model
	SCREAMING_SNAKE_CASE   :      List[str]				     =       UNetaDModel(**_a)
	print(f"length of state dict: {len(state_dict.keys())}")
	print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")

	SCREAMING_SNAKE_CASE   :      int				     =       dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()))
	for k, v in mapping.items():
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       state_dict.pop(_a)

	hf_value_function.load_state_dict(_a)

	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(_a							,			_a)


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