



"""simple docstring"""






def     _lowercase       (						__snake_case      ,__snake_case			)		->     list:
			__lowerCAmelCase   :						List[str]				= len(__snake_case			)
			__lowerCAmelCase   :						Dict				= []
			for i in range(len(__snake_case			) - pat_len + 1			):
						__lowerCAmelCase   :						List[Any]				= True
						for j in range(__snake_case			):
									if s[i + j] != pattern[j]:
												__lowerCAmelCase   :						Union[str, Any]				= False
												break
						if match_found:
									position.append(__snake_case			)
			return position


if __name__ == "__main__":
	assert naive_pattern_search('ABCDEFG', 'DE') == [3]
	print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))




"""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     _lowercase       (						__snake_case			)		->     int:
			if hor == 128:
						__lowerCAmelCase   :						str				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						int				= (32, 128, 256)
						__lowerCAmelCase   :						Optional[Any]				= ("UpResnetBlock1D", "UpResnetBlock1D")

			elif hor == 32:
						__lowerCAmelCase   :						List[str]				= ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
						__lowerCAmelCase   :						Optional[Any]				= (32, 64, 128, 256)
						__lowerCAmelCase   :						Any				= ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
			__lowerCAmelCase   :						Union[str, Any]				= torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""			)
			__lowerCAmelCase   :						List[Any]				= model.state_dict()
			__lowerCAmelCase   :						Optional[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": 65_536,
			    "mid_block_type": "MidResTemporalBlock1D",
			    "act_fn": "mish",
			}
			__lowerCAmelCase   :						Dict				= 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()			)}"""			)
			__lowerCAmelCase   :						Dict				= dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						int				= 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     _lowercase       (						)		->     List[str]:
			__lowerCAmelCase   :						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": 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",
			}

			__lowerCAmelCase   :						int				= torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"			)
			__lowerCAmelCase   :						Any				= model
			__lowerCAmelCase   :						Optional[int]				= 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()			)}"""			)

			__lowerCAmelCase   :						Any				= dict(zip(state_dict.keys()      ,hf_value_function.state_dict().keys()			)			)
			for k, v in mapping.items():
						__lowerCAmelCase   :						Union[str, Any]				= 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()