


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	(							SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       if hor == 1_2_8:
              UpperCamelCase    :	int   	=		('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
              UpperCamelCase    :	Optional[int]   	=		(3_2, 1_2_8, 2_5_6)
              UpperCamelCase    :	Any   	=		('''UpResnetBlock1D''', '''UpResnetBlock1D''')

       elif hor == 3_2:
              UpperCamelCase    :	str   	=		('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
              UpperCamelCase    :	Any   	=		(3_2, 6_4, 1_2_8, 2_5_6)
              UpperCamelCase    :	Any   	=		('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
       UpperCamelCase    :	str   	=		torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""       )
       UpperCamelCase    :	str   	=		model.state_dict()
       UpperCamelCase    :	Dict   	=		{
           '''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''': 1_4,
           '''out_channels''': 1_4,
           '''extra_in_channels''': 0,
           '''time_embedding_type''': '''positional''',
           '''flip_sin_to_cos''': False,
           '''freq_shift''': 1,
           '''sample_size''': 6_5_5_3_6,
           '''mid_block_type''': '''MidResTemporalBlock1D''',
           '''act_fn''': '''mish''',
       }
       UpperCamelCase    :	List[str]   	=		UNetaDModel(**SCREAMING_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()       )}"""       )
       UpperCamelCase    :	Any   	=		dict(zip(model.state_dict().keys()							,			hf_value_function.state_dict().keys()       )       )
       for k, v in mapping.items():
              UpperCamelCase    :	Any   	=		state_dict.pop(SCREAMING_SNAKE_CASE_       )
       hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	int   	=		{
           '''in_channels''': 1_4,
           '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
           '''up_block_types''': (),
           '''out_block_type''': '''ValueFunction''',
           '''mid_block_type''': '''ValueFunctionMidBlock1D''',
           '''block_out_channels''': (3_2, 6_4, 1_2_8, 2_5_6),
           '''layers_per_block''': 1,
           '''downsample_each_block''': True,
           '''sample_size''': 6_5_5_3_6,
           '''out_channels''': 1_4,
           '''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''',
       }

       UpperCamelCase    :	int   	=		torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''       )
       UpperCamelCase    :	Tuple   	=		model
       UpperCamelCase    :	Optional[Any]   	=		UNetaDModel(**SCREAMING_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()       )}"""       )

       UpperCamelCase    :	Dict   	=		dict(zip(state_dict.keys()							,			hf_value_function.state_dict().keys()       )       )
       for k, v in mapping.items():
              UpperCamelCase    :	Any   	=		state_dict.pop(SCREAMING_SNAKE_CASE_       )

       hf_value_function.load_state_dict(SCREAMING_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(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
  unet(32)
  # unet(128)
  value_function()





from pathlib import Path

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


def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		cva.getAffineTransform(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       return cva.warpAffine(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			(rows, cols)       )


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

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

  # add all rotated images in a list
  __UpperCAmelCase : Union[str, Any] 			=				[
      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
  __UpperCAmelCase : List[str] 			=				plt.figure(1)
  __UpperCAmelCase : Dict 			=				["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()

