



'''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      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[int]:
		if hor == 128:
				_a     :      Tuple        =							('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
				_a     :      Union[str, Any]        =							(32, 128, 256)
				_a     :      Any        =							('UpResnetBlock1D', 'UpResnetBlock1D')

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

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

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









'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




