



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        __lowerCAmelCase		(	__lowerCamelCase						:     int						)	->    List[Any]:
					if hor == 128:
										__lowerCAmelCase						 =("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
										__lowerCAmelCase						 =(32, 128, 256)
										__lowerCAmelCase						 =("""UpResnetBlock1D""", """UpResnetBlock1D""")

					elif hor == 32:
										__lowerCAmelCase						 =("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
										__lowerCAmelCase						 =(32, 64, 128, 256)
										__lowerCAmelCase						 =("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
					__lowerCAmelCase						 =torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""						)
					__lowerCAmelCase						 =model.state_dict()
					__lowerCAmelCase						 ={
					    """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""": 65536,
					    """mid_block_type""": """MidResTemporalBlock1D""",
					    """act_fn""": """mish""",
					}
					__lowerCAmelCase						 =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()						)}"""						)
					__lowerCAmelCase						 =dict(zip(model.state_dict().keys()					, hf_value_function.state_dict().keys()						)						)
					for k, v in mapping.items():
										__lowerCAmelCase						 =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        __lowerCAmelCase		(	)	->    Optional[int]:
					__lowerCAmelCase						 ={
					    """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""": 65536,
					    """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						 =torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""						)
					__lowerCAmelCase						 =model
					__lowerCAmelCase						 =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()						)}"""						)

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









from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =Counter(__lowerCamelCase						).most_common(1						)[0][0]
					return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))




