


'''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	:							Tuple							):







   '''simple docstring'''




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

   elif hor == 3_2:
      _UpperCAmelCase       :       str							   =("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
      _UpperCAmelCase       :       List[Any]							   =(3_2, 6_4, 1_2_8, 2_5_6)
      _UpperCAmelCase       :       Dict							   =("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
   _UpperCAmelCase       :       List[Any]							   =torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"							)
   _UpperCAmelCase       :       Union[str, Any]							   =model.state_dict()
   _UpperCAmelCase       :       List[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""": 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       :       Dict							   =UNetaDModel(**A__							)
   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       :       Optional[int]							   =dict(zip(model.state_dict().keys()	,   hf_value_function.state_dict().keys()							)							)
   for k, v in mapping.items():
      _UpperCAmelCase       :       Union[str, Any]							   =state_dict.pop(A__							)
   hf_value_function.load_state_dict(A__							)

   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(A__	,   A__							)





def lowerCamelCase__		(						):







   '''simple docstring'''




   _UpperCAmelCase       :       str							   ={
       """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       :       Optional[int]							   =torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'							)
   _UpperCAmelCase       :       Union[str, Any]							   =model
   _UpperCAmelCase       :       List[str]							   =UNetaDModel(**A__							)
   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       :       List[str]							   =dict(zip(state_dict.keys()	,   hf_value_function.state_dict().keys()							)							)
   for k, v in mapping.items():
      _UpperCAmelCase       :       List[Any]							   =state_dict.pop(A__							)

   hf_value_function.load_state_dict(A__							)

   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(A__	,   A__							)


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





'''simple docstring'''






from typing import Any





def lowerCamelCase__      (						__lowerCamelCase	:							list	,   __lowerCamelCase	:							list	,   __lowerCamelCase	:							dict	,   __lowerCamelCase	:							dict	,   __lowerCamelCase	:							dict	,   ):







   '''simple docstring'''




   _validation(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   )
   # Creates data structures and fill initial step
   _UpperCAmelCase       :       dict							   ={}
   _UpperCAmelCase       :       dict							   ={}
   for state in states_space:
      _UpperCAmelCase       :       int							   =observations_space[0]
      _UpperCAmelCase       :       int							   =(
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      _UpperCAmelCase       :       int							   =None

   # Fills the data structure with the probabilities of
   # different transitions and pointers to previous states
   for o in range(1	,   len(__lowerCamelCase							)							):
      _UpperCAmelCase       :       List[Any]							   =observations_space[o]
      _UpperCAmelCase       :       Optional[int]							   =observations_space[o - 1]
      for state in states_space:
         # Calculates the argmax for probability function
         _UpperCAmelCase       :       List[str]							   =''
         _UpperCAmelCase       :       Dict							   =-1
         for k_state in states_space:
            _UpperCAmelCase       :       List[str]							   =(
                probabilities[(k_state, prior_observation)]
                * transition_probabilities[k_state][state]
                * emission_probabilities[state][observation]
            )
            if probability > max_probability:
               _UpperCAmelCase       :       int							   =probability
               _UpperCAmelCase       :       List[Any]							   =k_state

            # Update probabilities and pointers dicts
         _UpperCAmelCase       :       str							   =(
             probabilities[(arg_max, prior_observation)]
             * transition_probabilities[arg_max][state]
             * emission_probabilities[state][observation]
         )

         _UpperCAmelCase       :       List[Any]							   =arg_max

    # The final observation
   _UpperCAmelCase       :       int							   =observations_space[len(__lowerCamelCase							) - 1]

   # argmax for given final observation
   _UpperCAmelCase       :       Any							   =''
   _UpperCAmelCase       :       Union[str, Any]							   =-1
   for k_state in states_space:
      _UpperCAmelCase       :       Optional[int]							   =probabilities[(k_state, final_observation)]
      if probability > max_probability:
         _UpperCAmelCase       :       Union[str, Any]							   =probability
         _UpperCAmelCase       :       int							   =k_state
   _UpperCAmelCase       :       int							   =arg_max

   # Process pointers backwards
   _UpperCAmelCase       :       List[str]							   =last_state
   _UpperCAmelCase       :       Optional[int]							   =[]
   for o in range(len(__lowerCamelCase							) - 1	,   -1	,   -1							):
      result.append(__lowerCamelCase							)
      _UpperCAmelCase       :       Optional[Any]							   =pointers[previous, observations_space[o]]
   result.reverse()

   return result





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   _validate_not_empty(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   )
   _validate_lists(__lowerCamelCase	,   __lowerCamelCase							)
   _validate_dicts(
       __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]							):
      raise ValueError('There\'s an empty parameter'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any							):







   '''simple docstring'''




   _validate_list(__lowerCamelCase	,   'observations_space'							)
   _validate_list(__lowerCamelCase	,   'states_space'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   if not isinstance(_object	,   __lowerCamelCase							):
      _UpperCAmelCase       :       Any							   =f"{var_name} must be a list"
      raise ValueError(__lowerCamelCase							)
   else:
      for x in _object:
         if not isinstance(__lowerCamelCase	,   __lowerCamelCase							):
            _UpperCAmelCase       :       Optional[int]							   =f"{var_name} must be a list of strings"
            raise ValueError(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Any	,   ):







   '''simple docstring'''




   _validate_dict(__lowerCamelCase	,   'initial_probabilities'	,   __lowerCamelCase							)
   _validate_nested_dict(__lowerCamelCase	,   'transition_probabilities'							)
   _validate_nested_dict(__lowerCamelCase	,   'emission_probabilities'							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str							):







   '''simple docstring'''




   _validate_dict(_object	,   __lowerCamelCase	,   __lowerCamelCase							)
   for x in _object.values():
      _validate_dict(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							type	,   __lowerCamelCase	:							bool = False							):







   '''simple docstring'''




   if not isinstance(_object	,   __lowerCamelCase							):
      _UpperCAmelCase       :       List[str]							   =f"{var_name} must be a dict"
      raise ValueError(__lowerCamelCase							)
   if not all(isinstance(__lowerCamelCase	,   __lowerCamelCase							) for x in _object							):
      _UpperCAmelCase       :       str							   =f"{var_name} all keys must be strings"
      raise ValueError(__lowerCamelCase							)
   if not all(isinstance(__lowerCamelCase	,   __lowerCamelCase							) for x in _object.values()							):
      _UpperCAmelCase       :       int							   ='nested dictionary ' if nested else ''
      _UpperCAmelCase       :       Optional[int]							   =f"{var_name} {nested_text}all values must be {value_type.__name__}"
      raise ValueError(__lowerCamelCase							)


if __name__ == "__main__":
 from doctest import testmod

 testmod()

