"""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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				str:
  if hor == 128:
    _lowerCAmelCase						:	Tuple        =					("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    _lowerCAmelCase						:	Optional[int]        =					(32, 128, 256)
    _lowerCAmelCase						:	List[Any]        =					("""UpResnetBlock1D""", """UpResnetBlock1D""")

  elif hor == 32:
    _lowerCAmelCase						:	int        =					("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
    _lowerCAmelCase						:	Tuple        =					(32, 64, 128, 256)
    _lowerCAmelCase						:	Optional[Any]        =					("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
  _lowerCAmelCase						:	List[str]        =					torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"     )
  _lowerCAmelCase						:	str        =					model.state_dict()
  _lowerCAmelCase						:	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""",
  }
  _lowerCAmelCase						:	List[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()     )}"     )
  _lowerCAmelCase						:	str        =					dict(zip(model.state_dict().keys()      ,hf_value_function.state_dict().keys()     )     )
  for k, v in mapping.items():
    _lowerCAmelCase						:	Any        =					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 							SCREAMING_SNAKE_CASE					(      )	->				Dict:
  _lowerCAmelCase						:	Tuple        =					{
      """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						:	int        =					torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""     )
  _lowerCAmelCase						:	Tuple        =					model
  _lowerCAmelCase						:	Tuple        =					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						:	Optional[Any]        =					dict(zip(state_dict.keys()      ,hf_value_function.state_dict().keys()     )     )
  for k, v in mapping.items():
    _lowerCAmelCase						:	str        =					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 manim import *
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					Rectangle(height=0.5    ,					width=0.5  )
       _lowerCAmelCase						:	List[Any]        =					Rectangle(height=0.4_6    ,					width=0.4_6  ).set_stroke(width=0  )

       _lowerCAmelCase						:	List[str]        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	Any        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	Optional[int]        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )
       _lowerCAmelCase						:	Tuple        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )
       _lowerCAmelCase						:	Optional[Any]        =					VGroup(a__    ,					a__  ).arrange(a__    ,					buff=0  )
       _lowerCAmelCase						:	Dict        =					Text("""CPU"""    ,					font_size=24  )
       _lowerCAmelCase						:	str        =					Group(a__    ,					a__  ).arrange(a__    ,					buff=0.5    ,					aligned_edge=a__  )
       cpu.move_to([-2.5, -0.5, 0]  )
       self.add(a__  )

       _lowerCAmelCase						:	Dict        =					[mem.copy() for i in range(4  )]
       _lowerCAmelCase						:	Any        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )
       _lowerCAmelCase						:	Tuple        =					Text("""GPU"""    ,					font_size=24  )
       _lowerCAmelCase						:	Optional[int]        =					Group(a__    ,					a__  ).arrange(a__    ,					buff=0.5    ,					aligned_edge=a__  )
       gpu.move_to([-1, -1, 0]  )
       self.add(a__  )

       _lowerCAmelCase						:	Optional[int]        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	Any        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )

       _lowerCAmelCase						:	List[str]        =					Text("""Model"""    ,					font_size=24  )
       _lowerCAmelCase						:	Any        =					Group(a__    ,					a__  ).arrange(a__    ,					buff=0.5    ,					aligned_edge=a__  )
       model.move_to([3, -1.0, 0]  )
       self.add(a__  )

       _lowerCAmelCase						:	Tuple        =					[]
       for i, rect in enumerate(a__  ):
         rect.set_stroke(a__  )
         # target = fill.copy().set_fill(YELLOW, opacity=0.7)
         # target.move_to(rect)
         # self.add(target)

         _lowerCAmelCase						:	List[str]        =					Rectangle(height=0.4_6 / 4    ,					width=0.4_6 / 3  ).set_stroke(width=0.0  ).set_fill(a__    ,					opacity=0.7  )

         if i == 0:
           cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT  )    ,					buff=0.0_2    ,					direction=a__  )
           cpu_target.set_x(cpu_target.get_x() + 0.1  )
         elif i == 3:
           cpu_target.next_to(cpu_targs[0]    ,					direction=a__    ,					buff=0.0  )
         else:
           cpu_target.next_to(cpu_targs[i - 1]    ,					direction=a__    ,					buff=0.0  )
         self.add(a__  )
         cpu_targs.append(a__  )

       _lowerCAmelCase						:	Any        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	List[str]        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )

       _lowerCAmelCase						:	int        =					Text("""Loaded Checkpoint"""    ,					font_size=24  )
       _lowerCAmelCase						:	Optional[int]        =					Group(a__    ,					a__  ).arrange(a__    ,					aligned_edge=a__    ,					buff=0.4  )
       checkpoint.move_to([3, 0.5, 0]  )

       _lowerCAmelCase						:	List[Any]        =					Square(side_length=2.2  )
       key.move_to([-5, 2, 0]  )

       _lowerCAmelCase						:	List[str]        =					MarkupText(
           F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"    ,					font_size=18    ,					)

       key_text.move_to([-5, 2.4, 0]  )

       self.add(a__    ,					a__  )

       _lowerCAmelCase						:	int        =					MarkupText(
           F"<span fgcolor='{BLUE}'>●</span> Checkpoint"    ,					font_size=18    ,					)

       blue_text.next_to(a__    ,					DOWN * 2.4    ,					aligned_edge=key_text.get_left()  )

       _lowerCAmelCase						:	List[Any]        =					MarkupText(
           F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."    ,					font_size=24    ,					)
       step_a.move_to([2, 2, 0]  )
       self.play(Write(a__  )    ,					Write(a__  )  )

       self.play(Write(a__    ,					run_time=1  )    ,					Create(a__    ,					run_time=1  )  )

       _lowerCAmelCase						:	int        =					[]
       _lowerCAmelCase						:	List[Any]        =					[]
       for i, rect in enumerate(a__  ):
         _lowerCAmelCase						:	Tuple        =					fill.copy().set_fill(a__    ,					opacity=0.7  )
         target.move_to(a__  )
         first_animations.append(GrowFromCenter(a__    ,					run_time=1  )  )

         _lowerCAmelCase						:	Optional[Any]        =					target.copy()
         cpu_target.generate_target()
         if i < 5:
           cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
         else:
           cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
         second_animations.append(MoveToTarget(a__    ,					run_time=1.5  )  )

       self.play(*a__  )
       self.play(*a__  )
       self.wait()




