






'''simple docstring'''







import numpy as np

import datasets


UpperCamelCase    :      List[Any]  			=		'\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

UpperCamelCase    :      Dict  			=		'\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

UpperCamelCase    :      Any  			=		'\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION			)
class 		UpperCamelCase__    (datasets.Metric			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								return datasets.MetricInfo(
								    description=_DESCRIPTION   ,citation=_CITATION   ,inputs_description=_KWARGS_DESCRIPTION   ,features=datasets.Features(
								        {
								            """X""": datasets.Sequence(datasets.Value("""float"""   ,id="""sequence"""       )   ,id="""X"""       ),
								        }       )   ,)






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								# convert to numpy arrays
								lowerCamelCase__         =  np.array(_lowerCAmelCase       )
								lowerCamelCase__         =  np.array(_lowerCAmelCase       )

								# Assert that arrays are 2D
								if len(X.shape       ) != 2:
														raise ValueError("""Expected `X` to be a 2D vector"""       )
								if len(reference_distribution.shape       ) != 2:
														raise ValueError("""Expected `reference_distribution` to be a 2D vector"""       )
								if reference_distribution.shape[0] < 2:
														raise ValueError(
														    """Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension"""       )

								# Get mahalanobis distance for each prediction
								lowerCamelCase__         =  X - np.mean(_lowerCAmelCase       )
								lowerCamelCase__         =  np.cov(reference_distribution.T       )
								try:
														lowerCamelCase__         =  np.linalg.inv(_lowerCAmelCase       )
								except np.linalg.LinAlgError:
														lowerCamelCase__         =  np.linalg.pinv(_lowerCAmelCase       )
								lowerCamelCase__         =  np.dot(_lowerCAmelCase   ,_lowerCAmelCase       )
								lowerCamelCase__         =  np.dot(_lowerCAmelCase   ,X_minus_mu.T       ).diagonal()

								return {"mahalanobis": mahal_dist}









'''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		:			Union[str, 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""": 6_5536,
						    """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 						A__			(				):
						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""": 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""",
						}

						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()

