





from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def    A				(							UpperCAmelCase				):
					return getitem, k







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return setitem, k, v







def    A				(							UpperCAmelCase				):
					return delitem, k







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  *UpperCAmelCase				):
					try:
										return fun(UpperCAmelCase  ,  *UpperCAmelCase				), None
					except Exception as e:
										return None, e


__lowerCAmelCase :Dict     =    (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

__lowerCAmelCase :Optional[Any]     =    [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

__lowerCAmelCase :Any     =    [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

__lowerCAmelCase :List[Any]     =    [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

__lowerCAmelCase :List[str]     =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

__lowerCAmelCase :Tuple     =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]







@pytest.mark.parametrize(
    "operations"  ,  (
        pytest.param(_add_items  ,  id="add items"				),
        pytest.param(_overwrite_items  ,  id="overwrite items"				),
        pytest.param(_delete_items  ,  id="delete items"				),
        pytest.param(_access_absent_items  ,  id="access absent items"				),
        pytest.param(_add_with_resize_up  ,  id="add with resize up"				),
        pytest.param(_add_with_resize_down  ,  id="add with resize down"				),
    )  ,  )
def    A				(							UpperCAmelCase				):
					_snake_case       :					Any				       =   HashMap(initial_block_size=4				)
					_snake_case       :					List[Any]				       =   {}
					for _, (fun, *args) in enumerate(UpperCAmelCase				):
										_snake_case       , _snake_case       :					int				       =   _run_operation(UpperCAmelCase  ,  UpperCAmelCase  ,  *UpperCAmelCase				)
										_snake_case       , _snake_case       :					Tuple				       =   _run_operation(UpperCAmelCase  ,  UpperCAmelCase  ,  *UpperCAmelCase				)
										assert my_res == py_res
										assert str(UpperCAmelCase				) == str(UpperCAmelCase				)
										assert set(UpperCAmelCase				) == set(UpperCAmelCase				)
										assert len(UpperCAmelCase				) == len(UpperCAmelCase				)
										assert set(my.items()				) == set(py.items()				)







def    A				(							):
					def is_public(UpperCAmelCase				) -> bool:
										return not name.startswith("_"				)

					_snake_case       :					str				       =   {name for name in dir({}				) if is_public(UpperCAmelCase				)}
					_snake_case       :					List[str]				       =   {name for name in dir(HashMap()				) if is_public(UpperCAmelCase				)}

					assert dict_public_names > hash_public_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    A				(							UpperCAmelCase				):
					if hor == 128:
										_snake_case       :					int				       =   ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										_snake_case       :					Tuple				       =   (32, 128, 256)
										_snake_case       :					str				       =   ("UpResnetBlock1D", "UpResnetBlock1D")

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

					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(UpperCAmelCase  ,  UpperCAmelCase				)







def    A				(							):
					_snake_case       :					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",
					}

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

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

					hf_value_function.load_state_dict(UpperCAmelCase				)

					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(UpperCAmelCase  ,  UpperCAmelCase				)


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