






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_							(							__magic_name__					:  Optional[int]					)		->			Optional[Any]:






	"""simple docstring"""



	if hor == 128:
		UpperCamelCase     :List[Any]          =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
		UpperCamelCase     :Optional[Any]          =	(32, 128, 256)
		UpperCamelCase     :int          =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

	elif hor == 32:
		UpperCamelCase     :str          =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
		UpperCamelCase     :List[Any]          =	(32, 64, 128, 256)
		UpperCamelCase     :List[Any]          =	("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
	UpperCamelCase     :str          =	torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""					)
	UpperCamelCase     :List[Any]          =	model.state_dict()
	UpperCamelCase     :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""": 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""",
	}
	UpperCamelCase     :List[str]          =	UNetaDModel(**__magic_name__					)
	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     :Optional[int]          =	dict(zip(model.state_dict().keys()	,			hf_value_function.state_dict().keys()					)					)
	for k, v in mapping.items():
		UpperCamelCase     :str          =	state_dict.pop(__magic_name__					)
	hf_value_function.load_state_dict(__magic_name__					)

	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(__magic_name__	,			__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Union[str, 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""": 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""",
	}

	UpperCamelCase     :List[Any]          =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""					)
	UpperCamelCase     :str          =	model
	UpperCamelCase     :int          =	UNetaDModel(**__magic_name__					)
	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     :Tuple          =	dict(zip(state_dict.keys()	,			hf_value_function.state_dict().keys()					)					)
	for k, v in mapping.items():
		UpperCamelCase     :Optional[int]          =	state_dict.pop(__magic_name__					)

	hf_value_function.load_state_dict(__magic_name__					)

	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(__magic_name__	,			__magic_name__					)


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




"""simple docstring"""





def    _snake_case  (  _snake_case :	list	):

      def merge(_snake_case :	list       ,      _snake_case :	list	) -> list:

            def _merge():
                  while left and right:
                        yield (left if left[0] <= right[0] else right).pop(0	)
                  yield from left
                  yield from right

            return list(_merge()	)

      if len(_snake_case	) <= 1:
            return collection
      lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	) // 2
      return merge(merge_sort(collection[:mid]	)       ,      merge_sort(collection[mid:]	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 snake_case__						:   Optional[Any]			 = input('''Enter numbers separated by a comma:\n''').strip()
 snake_case__						:   Union[str, Any]			 = [int(item) for item in user_input.split(''',''')]
 print(*merge_sort(unsorted), sep=''',''')


