"""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  _A(		lowerCAmelCase							):
							if hor == 128:
														A__       :     List[str]								=			("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														A__       :     Optional[Any]								=			(32, 128, 256)
														A__       :     Any								=			("""UpResnetBlock1D""", """UpResnetBlock1D""")

							elif hor == 32:
														A__       :     Dict								=			("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														A__       :     Optional[Any]								=			(32, 64, 128, 256)
														A__       :     int								=			("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
							A__       :     Optional[Any]								=			torch.load(F'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''							)
							A__       :     Optional[int]								=			model.state_dict()
							A__       :     int								=			{
							    """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""": 6_5536,
							    """mid_block_type""": """MidResTemporalBlock1D""",
							    """act_fn""": """mish""",
							}
							A__       :     Optional[Any]								=			UNetaDModel(**lowerCAmelCase							)
							print(F'''length of state dict: {len(state_dict.keys()							)}'''							)
							print(F'''length of value function dict: {len(hf_value_function.state_dict().keys()							)}'''							)
							A__       :     Tuple								=			dict(zip(model.state_dict().keys()			,  hf_value_function.state_dict().keys()							)							)
							for k, v in mapping.items():
														A__       :     int								=			state_dict.pop(lowerCAmelCase							)
							hf_value_function.load_state_dict(lowerCAmelCase							)

							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(lowerCAmelCase			,  lowerCAmelCase							)

def  _A(		):
							A__       :     str								=			{
							    """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""": 6_5536,
							    """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""",
							}

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

							A__       :     Optional[Any]								=			dict(zip(state_dict.keys()			,  hf_value_function.state_dict().keys()							)							)
							for k, v in mapping.items():
														A__       :     Tuple								=			state_dict.pop(lowerCAmelCase							)

							hf_value_function.load_state_dict(lowerCAmelCase							)

							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(lowerCAmelCase			,  lowerCAmelCase							)


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






"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





