






'''simple docstring'''







import cva
import numpy as np




class 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):

								if k in (0.04, 0.06):
														lowerCamelCase__         =  k
														lowerCamelCase__         =  window_size
								else:
														raise ValueError("""invalid k value"""       )






		def __str__(      self       ):
								return str(self.k       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  cva.imread(_lowerCAmelCase   ,0       )
								lowerCamelCase__       ,			lowerCamelCase__         =  img.shape
								lowerCamelCase__         =  []
								lowerCamelCase__         =  img.copy()
								lowerCamelCase__         =  cva.cvtColor(_lowerCAmelCase   ,cva.COLOR_GRAY2RGB       )
								lowerCamelCase__       ,			lowerCamelCase__         =  np.gradient(_lowerCAmelCase       )
								lowerCamelCase__         =  dx**2
								lowerCamelCase__         =  dy**2
								lowerCamelCase__         =  dx * dy
								lowerCamelCase__         =  0.04
								lowerCamelCase__         =  self.window_size // 2
								for y in range(_lowerCAmelCase   ,h - offset       ):
														for x in range(_lowerCAmelCase   ,w - offset       ):
																				lowerCamelCase__         =  ixx[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowerCamelCase__         =  iyy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()
																				lowerCamelCase__         =  ixy[
																				    y - offset : y + offset + 1, x - offset : x + offset + 1
																				].sum()

																				lowerCamelCase__         =  (wxx * wyy) - (wxy**2)
																				lowerCamelCase__         =  wxx + wyy
																				lowerCamelCase__         =  det - k * (trace**2)
																				# Can change the value
																				if r > 0.5:
																										corner_list.append([x, y, r]       )
																										color_img.itemset((y, x, 0)   ,0       )
																										color_img.itemset((y, x, 1)   ,0       )
																										color_img.itemset((y, x, 2)   ,2_55       )
								return color_img, corner_list


if __name__ == "__main__":
							UpperCamelCase    :      Optional[Any]  			=		HarrisCorner(0.04, 3)
							UpperCamelCase			,						UpperCamelCase    :      Tuple  			=		edge_detect.detect('path_to_image')
							cva.imwrite('detect.png', color_img)









'''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()

