




"""simple docstring"""







import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


SCREAMING_SNAKE_CASE_	     =       {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_	     =       'hopper-medium-v2'
  SCREAMING_SNAKE_CASE_	     =       gym.make(env_name)

  SCREAMING_SNAKE_CASE_	     =       ValueGuidedRLPipeline.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32',
      env=env,
  )

  env.seed(0)
  SCREAMING_SNAKE_CASE_	     =       env.reset()
  SCREAMING_SNAKE_CASE_	     =       0
  SCREAMING_SNAKE_CASE_	     =       0
  SCREAMING_SNAKE_CASE_	     =       1000
  SCREAMING_SNAKE_CASE_	     =       [obs.copy()]
  try:
    for t in tqdm.tqdm(range(T)):
      # call the policy
      SCREAMING_SNAKE_CASE_	     =       pipeline(obs, planning_horizon=32)

      # execute action in environment
      SCREAMING_SNAKE_CASE_     ,    SCREAMING_SNAKE_CASE_     ,    SCREAMING_SNAKE_CASE_     ,    SCREAMING_SNAKE_CASE_	     =       env.step(denorm_actions)
      SCREAMING_SNAKE_CASE_	     =       env.get_normalized_score(total_reward)

      # update return
      total_reward += reward
      total_score += score
      print(
          f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
          f' {total_score}'
      )

      # save observations for rendering
      rollout.append(next_observation.copy())

      SCREAMING_SNAKE_CASE_	     =       next_observation
  except KeyboardInterrupt:
    pass

  print(f'Total reward: {total_reward}')



'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      int:
            _A				=							[]
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=_UpperCamelCase			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

                 _A				=							FlaxTransformeraDModel(
                     in_channels=self.out_channels			,     n_heads=self.num_attention_heads			,     d_head=self.out_channels // self.num_attention_heads			,     depth=1			,     use_linear_projection=self.use_linear_projection			,     only_cross_attention=self.only_cross_attention			,     use_memory_efficient_attention=self.use_memory_efficient_attention			,     dtype=self.dtype			,     )
                 attentions.append(_UpperCamelCase   )

            _A				=							resnets
            _A				=							attentions

            if self.add_downsample:
                 _A				=							FlaxDownsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            _A				=							()

            for resnet, attn in zip(self.resnets			,     self.attentions   ):
                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 output_states += (hidden_states,)

            if self.add_downsample:
                 _A				=							self.downsamplers_a(_UpperCamelCase   )
                 output_states += (hidden_states,)

            return hidden_states, output_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=_UpperCamelCase			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )
            _A				=							resnets

            if self.add_downsample:
                 _A				=							FlaxDownsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Union[str, Any]:
            _A				=							()

            for resnet in self.resnets:
                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 output_states += (hidden_states,)

            if self.add_downsample:
                 _A				=							self.downsamplers_a(_UpperCamelCase   )
                 output_states += (hidden_states,)

            return hidden_states, output_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      str:
            _A				=							[]
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
                 _A				=							self.prev_output_channel if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=resnet_in_channels + res_skip_channels			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

                 _A				=							FlaxTransformeraDModel(
                     in_channels=self.out_channels			,     n_heads=self.num_attention_heads			,     d_head=self.out_channels // self.num_attention_heads			,     depth=1			,     use_linear_projection=self.use_linear_projection			,     only_cross_attention=self.only_cross_attention			,     use_memory_efficient_attention=self.use_memory_efficient_attention			,     dtype=self.dtype			,     )
                 attentions.append(_UpperCamelCase   )

            _A				=							resnets
            _A				=							attentions

            if self.add_upsample:
                 _A				=							FlaxUpsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            for resnet, attn in zip(self.resnets			,     self.attentions   ):
                 # pop res hidden states
                 _A				=							res_hidden_states_tuple[-1]
                 _A				=							res_hidden_states_tuple[:-1]
                 _A				=							jnp.concatenate((hidden_states, res_hidden_states)			,     axis=-1   )

                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            if self.add_upsample:
                 _A				=							self.upsamplers_a(_UpperCamelCase   )

            return hidden_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
                 _A				=							self.prev_output_channel if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=resnet_in_channels + res_skip_channels			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

            _A				=							resnets

            if self.add_upsample:
                 _A				=							FlaxUpsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            for resnet in self.resnets:
                 # pop res hidden states
                 _A				=							res_hidden_states_tuple[-1]
                 _A				=							res_hidden_states_tuple[:-1]
                 _A				=							jnp.concatenate((hidden_states, res_hidden_states)			,     axis=-1   )

                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            if self.add_upsample:
                 _A				=							self.upsamplers_a(_UpperCamelCase   )

            return hidden_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Dict:
            # there is always at least one resnet
            _A				=							[
                FlaxResnetBlockaD(
                    in_channels=self.in_channels			,     out_channels=self.in_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
            ]

            _A				=							[]

            for _ in range(self.num_layers   ):
                 _A				=							FlaxTransformeraDModel(
                     in_channels=self.in_channels			,     n_heads=self.num_attention_heads			,     d_head=self.in_channels // self.num_attention_heads			,     depth=1			,     use_linear_projection=self.use_linear_projection			,     use_memory_efficient_attention=self.use_memory_efficient_attention			,     dtype=self.dtype			,     )
                 attentions.append(_UpperCamelCase   )

                 _A				=							FlaxResnetBlockaD(
                     in_channels=self.in_channels			,     out_channels=self.in_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

            _A				=							resnets
            _A				=							attentions






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      List[Any]:
            _A				=							self.resnets[0](_UpperCamelCase			,     _UpperCamelCase   )
            for attn, resnet in zip(self.attentions			,     self.resnets[1:]   ):
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            return hidden_states





