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

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

      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(UpperCamelCase__			,		UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : Tuple											=					{
          '''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__     : str											=					model
      UpperCamelCase__     : List[str]											=					UNetaDModel(**UpperCamelCase__		)
      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[Any]											=					dict(zip(state_dict.keys()			,		hf_value_function.state_dict().keys()		)		)
      for k, v in mapping.items():
            UpperCamelCase__     : Optional[Any]											=					state_dict.pop(UpperCamelCase__		)

      hf_value_function.load_state_dict(UpperCamelCase__		)

      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(UpperCamelCase__			,		UpperCamelCase__		)


if __name__ == "__main__":
    unet(3_2)
    # unet(128)
    value_function()




from typing import Any


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validation(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		)
      # Creates data structures and fill initial step
      UpperCamelCase__     : dict											=					{}
      UpperCamelCase__     : dict											=					{}
      for state in states_space:
            UpperCamelCase__     : Optional[int]											=					observations_space[0]
            UpperCamelCase__     : Any											=					(
                initial_probabilities[state] * emission_probabilities[state][observation]
            )
            UpperCamelCase__     : Union[str, Any]											=					None

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

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

                  UpperCamelCase__     : Optional[Any]											=					arg_max

    # The final observation
      UpperCamelCase__     : List[str]											=					observations_space[len(UpperCamelCase__		) - 1]

      # argmax for given final observation
      UpperCamelCase__     : Dict											=					''''''
      UpperCamelCase__     : Tuple											=					-1
      for k_state in states_space:
            UpperCamelCase__     : Any											=					probabilities[(k_state, final_observation)]
            if probability > max_probability:
                  UpperCamelCase__     : List[str]											=					probability
                  UpperCamelCase__     : Tuple											=					k_state
      UpperCamelCase__     : Any											=					arg_max

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

      return result


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_not_empty(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		)
      _validate_lists(UpperCamelCase__			,		UpperCamelCase__		)
      _validate_dicts(
          UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


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


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_list(UpperCamelCase__			,		'''observations_space'''		)
      _validate_list(UpperCamelCase__			,		'''states_space'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      if not isinstance(_object			,		UpperCamelCase__		):
            UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list'''
            raise ValueError(UpperCamelCase__		)
      else:
            for x in _object:
                  if not isinstance(UpperCamelCase__			,		UpperCamelCase__		):
                        UpperCamelCase__     : List[Any]											=					f'''{var_name} must be a list of strings'''
                        raise ValueError(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		):
      _validate_dict(UpperCamelCase__			,		'''initial_probabilities'''			,		UpperCamelCase__		)
      _validate_nested_dict(UpperCamelCase__			,		'''transition_probabilities'''		)
      _validate_nested_dict(UpperCamelCase__			,		'''emission_probabilities'''		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      _validate_dict(_object			,		UpperCamelCase__			,		UpperCamelCase__		)
      for x in _object.values():
            _validate_dict(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__ = False		):
      if not isinstance(_object			,		UpperCamelCase__		):
            UpperCamelCase__     : List[str]											=					f'''{var_name} must be a dict'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__			,		UpperCamelCase__		) for x in _object		):
            UpperCamelCase__     : Dict											=					f'''{var_name} all keys must be strings'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__			,		UpperCamelCase__		) for x in _object.values()		):
            UpperCamelCase__     : Optional[Any]											=					'''nested dictionary ''' if nested else ''''''
            UpperCamelCase__     : Optional[Any]											=					f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
            raise ValueError(UpperCamelCase__		)


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

    testmod()


