




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      (		snake_case       )						->				Union[str, Any]:
						if hor == 1_2_8:
												_UpperCAmelCase      =			("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												_UpperCAmelCase      =			(3_2, 1_2_8, 2_5_6)
												_UpperCAmelCase      =			("""UpResnetBlock1D""", """UpResnetBlock1D""")

						elif hor == 3_2:
												_UpperCAmelCase      =			("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
												_UpperCAmelCase      =			(3_2, 6_4, 1_2_8, 2_5_6)
												_UpperCAmelCase      =			("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
						_UpperCAmelCase      =			torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"       )
						_UpperCAmelCase      =			model.state_dict()
						_UpperCAmelCase      =			{
						    """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""",
						}
						_UpperCAmelCase      =			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()       )}"       )
						_UpperCAmelCase      =			dict(zip(model.state_dict().keys()			,     hf_value_function.state_dict().keys()       )       )
						for k, v in mapping.items():
												_UpperCAmelCase      =			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        _SCREAMING_SNAKE_CASE      (		)						->				int:
						_UpperCAmelCase      =			{
						    """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""",
						}

						_UpperCAmelCase      =			torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""       )
						_UpperCAmelCase      =			model
						_UpperCAmelCase      =			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()       )}"       )

						_UpperCAmelCase      =			dict(zip(state_dict.keys()			,     hf_value_function.state_dict().keys()       )       )
						for k, v in mapping.items():
												_UpperCAmelCase      =			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()





import logging

from transformers.configuration_utils import PretrainedConfig


a													=			logging.getLogger(__name__)




class       _A				(		__lowercase     ):
		__a					    =       """masked_bert"""






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3_0522						,		_SCREAMING_SNAKE_CASE=768						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=3072						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-12						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE="topK"						,		_SCREAMING_SNAKE_CASE="constant"						,		_SCREAMING_SNAKE_CASE=0.0						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			hidden_size
								_UpperCAmelCase      =			num_hidden_layers
								_UpperCAmelCase      =			num_attention_heads
								_UpperCAmelCase      =			hidden_act
								_UpperCAmelCase      =			intermediate_size
								_UpperCAmelCase      =			hidden_dropout_prob
								_UpperCAmelCase      =			attention_probs_dropout_prob
								_UpperCAmelCase      =			max_position_embeddings
								_UpperCAmelCase      =			type_vocab_size
								_UpperCAmelCase      =			initializer_range
								_UpperCAmelCase      =			layer_norm_eps
								_UpperCAmelCase      =			pruning_method
								_UpperCAmelCase      =			mask_init
								_UpperCAmelCase      =			mask_scale