


import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Dict  ,			__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



	lowercase_			 = states.reshape(1  ,			-1  ,			model.config.state_dim  )
	lowercase_			 = actions.reshape(1  ,			-1  ,			model.config.act_dim  )
	lowercase_			 = returns_to_go.reshape(1  ,			-1  ,			1  )
	lowercase_			 = timesteps.reshape(1  ,			-1  )

	if model.config.max_length is not None:
		lowercase_			 = states[:, -model.config.max_length :]
		lowercase_			 = actions[:, -model.config.max_length :]
		lowercase_			 = returns_to_go[:, -model.config.max_length :]
		lowercase_			 = timesteps[:, -model.config.max_length :]

		# pad all tokens to sequence length
		lowercase_			 = torch.cat(
		    [torch.zeros(model.config.max_length - states.shape[1]  ), torch.ones(states.shape[1]  )]  )
		lowercase_			 = attention_mask.to(dtype=torch.long  ,			device=states.device  ).reshape(1  ,			-1  )
		lowercase_			 = torch.cat(
		    [
		        torch.zeros(
		            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)  ,			device=states.device  ,			),
		        states,
		    ]  ,			dim=1  ,			).to(dtype=torch.floataa  )
		lowercase_			 = torch.cat(
		    [
		        torch.zeros(
		            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)  ,			device=actions.device  ,			),
		        actions,
		    ]  ,			dim=1  ,			).to(dtype=torch.floataa  )
		lowercase_			 = torch.cat(
		    [
		        torch.zeros(
		            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)  ,			device=returns_to_go.device  ,			),
		        returns_to_go,
		    ]  ,			dim=1  ,			).to(dtype=torch.floataa  )
		lowercase_			 = torch.cat(
		    [
		        torch.zeros(
		            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])  ,			device=timesteps.device  ),
		        timesteps,
		    ]  ,			dim=1  ,			).to(dtype=torch.long  )
	else:
		lowercase_			 = None

	lowercase_			 = model(
	    states=_UpperCAmelCase  ,			actions=_UpperCAmelCase  ,			rewards=_UpperCAmelCase  ,			returns_to_go=_UpperCAmelCase  ,			timesteps=_UpperCAmelCase  ,			attention_mask=_UpperCAmelCase  ,			return_dict=_UpperCAmelCase  ,			)

	return action_preds[0, -1]


# build the environment

SCREAMING_SNAKE_CASE__       			=				gym.make("""Hopper-v3""")
SCREAMING_SNAKE_CASE__       			=				env.observation_space.shape[0]
SCREAMING_SNAKE_CASE__       			=				env.action_space.shape[0]
SCREAMING_SNAKE_CASE__       			=				1_0_0_0
SCREAMING_SNAKE_CASE__       			=				'cuda'
SCREAMING_SNAKE_CASE__       			=				1000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE__       			=				3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE__       			=				np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
SCREAMING_SNAKE_CASE__       			=				np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
SCREAMING_SNAKE_CASE__       			=				torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE__       			=				torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
SCREAMING_SNAKE_CASE__       			=				DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
SCREAMING_SNAKE_CASE__       			=				model.to(device)
model.eval()

for ep in range(1_0):
							SCREAMING_SNAKE_CASE__       			=				0, 0
							SCREAMING_SNAKE_CASE__       			=				env.reset()
							SCREAMING_SNAKE_CASE__       			=				torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
							SCREAMING_SNAKE_CASE__       			=				torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
							SCREAMING_SNAKE_CASE__       			=				torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
							SCREAMING_SNAKE_CASE__       			=				torch.zeros(0, device=device, dtype=torch.floataa)

							SCREAMING_SNAKE_CASE__       			=				torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
							for t in range(max_ep_len):
														env.render()
														# add padding
														SCREAMING_SNAKE_CASE__       			=				torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
														SCREAMING_SNAKE_CASE__       			=				torch.cat([rewards, torch.zeros(1, device=device)])

														SCREAMING_SNAKE_CASE__       			=				get_action(
														    model,
														    (states.to(dtype=torch.floataa) - state_mean) / state_std,
														    actions.to(dtype=torch.floataa),
														    rewards.to(dtype=torch.floataa),
														    target_return.to(dtype=torch.floataa),
														    timesteps.to(dtype=torch.long),
														)
														SCREAMING_SNAKE_CASE__       			=				action
														SCREAMING_SNAKE_CASE__       			=				action.detach().cpu().numpy()

														SCREAMING_SNAKE_CASE__       			=				env.step(action)

														SCREAMING_SNAKE_CASE__       			=				torch.from_numpy(state).to(device=device).reshape(1, state_dim)
														SCREAMING_SNAKE_CASE__       			=				torch.cat([states, cur_state], dim=0)
														SCREAMING_SNAKE_CASE__       			=				reward

														SCREAMING_SNAKE_CASE__       			=				target_return[0, -1] - (reward / scale)
														SCREAMING_SNAKE_CASE__       			=				torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
														SCREAMING_SNAKE_CASE__       			=				torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

														episode_return += reward
														episode_length += 1

														if done:
																					break








"""simple docstring"""




import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase = 4096   ):






  """simple docstring"""





  A_		,			A_		,			A_    :			List[Any]									=    key.shape[-3:]
  A_    :			Dict									=    value.shape[-1]
  A_    :			Union[str, Any]									=    min(_UpperCAmelCase					,   _UpperCAmelCase   )
  A_    :			str									=    query / jnp.sqrt(_UpperCAmelCase   )

  @functools.partial(jax.checkpoint					,   prevent_cse=_UpperCAmelCase   )
  def summarize_chunk(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):
    A_    :			Any									=    jnp.einsum('...qhd,...khd->...qhk'					,   _UpperCAmelCase					,   _UpperCAmelCase					,   precision=_UpperCAmelCase   )

    A_    :			int									=    jnp.max(_UpperCAmelCase					,   axis=-1					,   keepdims=_UpperCAmelCase   )
    A_    :			List[str]									=    jax.lax.stop_gradient(_UpperCAmelCase   )
    A_    :			Dict									=    jnp.exp(attn_weights - max_score   )

    A_    :			int									=    jnp.einsum('...vhf,...qhv->...qhf'					,   _UpperCAmelCase					,   _UpperCAmelCase					,   precision=_UpperCAmelCase   )
    A_    :			Optional[Any]									=    jnp.einsum('...qhk->...qh'					,   _UpperCAmelCase   )

    return (exp_values, exp_weights.sum(axis=-1   ), max_score)

  def chunk_scanner(_UpperCAmelCase   ):
    # julienne key array
    A_    :			Dict									=    jax.lax.dynamic_slice(
        operand=_UpperCAmelCase					,   start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]					,   slice_sizes=list(key.shape[:-3]   ) + [key_chunk_size, num_heads, k_features]					,   )

    # julienne value array
    A_    :			Optional[Any]									=    jax.lax.dynamic_slice(
        operand=_UpperCAmelCase					,   start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]					,   slice_sizes=list(value.shape[:-3]   ) + [key_chunk_size, num_heads, v_features]					,   )

    return summarize_chunk(_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   )

  A_		,			A_		,			A_    :			Dict									=    jax.lax.map(f=_UpperCAmelCase					,   xs=jnp.arange(0					,   _UpperCAmelCase					,   _UpperCAmelCase   )   )

  A_    :			int									=    jnp.max(_UpperCAmelCase					,   axis=0					,   keepdims=_UpperCAmelCase   )
  A_    :			Tuple									=    jnp.exp(chunk_max - global_max   )

  chunk_values *= jnp.expand_dims(_UpperCAmelCase					,   axis=-1   )
  chunk_weights *= max_diffs

  A_    :			List[Any]									=    chunk_values.sum(axis=0   )
  A_    :			Dict									=    jnp.expand_dims(_UpperCAmelCase					,   -1   ).sum(axis=0   )

  return all_values / all_weights





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=jax.lax.Precision.HIGHEST					,   _UpperCAmelCase = 1024					,   _UpperCAmelCase = 4096   ):






  """simple docstring"""





  A_		,			A_		,			A_    :			Any									=    query.shape[-3:]

  def chunk_scanner(_UpperCAmelCase					,   _UpperCAmelCase   ):
    # julienne query array
    A_    :			Tuple									=    jax.lax.dynamic_slice(
        operand=_UpperCAmelCase					,   start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]					,   slice_sizes=list(query.shape[:-3]   ) + [min(_UpperCAmelCase					,   _UpperCAmelCase   ), num_heads, q_features]					,   )

    return (
        chunk_idx + query_chunk_size,  # unused ignore it
        _query_chunk_attention(
            query=_UpperCAmelCase					,   key=_UpperCAmelCase					,   value=_UpperCAmelCase					,   precision=_UpperCAmelCase					,   key_chunk_size=_UpperCAmelCase   ),
    )

  A_		,			A_    :			Union[str, Any]									=    jax.lax.scan(
      f=_UpperCAmelCase					,   init=0					,   xs=_UpperCAmelCase					,   length=math.ceil(num_q / query_chunk_size   )   )

  return jnp.concatenate(_UpperCAmelCase					,   axis=-3   )  # fuse the chunked result back

class 					_UpperCAmelCase    (  nn.Module						):



      '''simple docstring'''
      lowercase_ :					int
      lowercase_ :					int             =  8
      lowercase_ :					int             =  64
      lowercase_ :					float             =  0.0
      lowercase_ :					bool             =  False
      lowercase_ :					jnp.dtype             =  jnp.floataa

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    self.dim_head * self.heads
        A_    :			Tuple									=    self.dim_head**-0.5

        # Weights were exported with old names {to_q, to_k, to_v, to_out}
        A_    :			Any									=    nn.Dense(snake_case_   , use_bias=snake_case_   , dtype=self.dtype   , name='to_q'    )
        A_    :			int									=    nn.Dense(snake_case_   , use_bias=snake_case_   , dtype=self.dtype   , name='to_k'    )
        A_    :			Optional[int]									=    nn.Dense(snake_case_   , use_bias=snake_case_   , dtype=self.dtype   , name='to_v'    )

        A_    :			Union[str, Any]									=    nn.Dense(self.query_dim   , dtype=self.dtype   , name='to_out_0'    )
        A_    :			str									=    nn.Dropout(rate=self.dropout    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_    :			Union[str, Any]									=    tensor.shape
        A_    :			List[str]									=    self.heads
        A_    :			str									=    tensor.reshape(snake_case_   , snake_case_   , snake_case_   , dim // head_size    )
        A_    :			Optional[Any]									=    jnp.transpose(snake_case_   , (0, 2, 1, 3)    )
        A_    :			Any									=    tensor.reshape(batch_size * head_size   , snake_case_   , dim // head_size    )
        return tensor

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_    :			str									=    tensor.shape
        A_    :			Optional[Any]									=    self.heads
        A_    :			List[Any]									=    tensor.reshape(batch_size // head_size   , snake_case_   , snake_case_   , snake_case_    )
        A_    :			List[Any]									=    jnp.transpose(snake_case_   , (0, 2, 1, 3)    )
        A_    :			Any									=    tensor.reshape(batch_size // head_size   , snake_case_   , dim * head_size    )
        return tensor

      def __call__(	self   , snake_case_   , snake_case_=None   , snake_case_=True    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    hidden_states if context is None else context

        A_    :			str									=    self.query(snake_case_    )
        A_    :			Any									=    self.key(snake_case_    )
        A_    :			Union[str, Any]									=    self.value(snake_case_    )

        A_    :			int									=    self.reshape_heads_to_batch_dim(snake_case_    )
        A_    :			List[str]									=    self.reshape_heads_to_batch_dim(snake_case_    )
        A_    :			str									=    self.reshape_heads_to_batch_dim(snake_case_    )

        if self.use_memory_efficient_attention:
          A_    :			str									=    query_states.transpose(1   , 0   , 2    )
          A_    :			Optional[int]									=    key_states.transpose(1   , 0   , 2    )
          A_    :			Optional[Any]									=    value_states.transpose(1   , 0   , 2    )

          # this if statement create a chunk size for each layer of the unet
          # the chunk size is equal to the query_length dimension of the deepest layer of the unet

          A_    :			Union[str, Any]									=    query_states.shape[-3]
          if flatten_latent_dim % 6_4 == 0:
            A_    :			Any									=    int(flatten_latent_dim / 6_4    )
          elif flatten_latent_dim % 1_6 == 0:
            A_    :			Optional[int]									=    int(flatten_latent_dim / 1_6    )
          elif flatten_latent_dim % 4 == 0:
            A_    :			Dict									=    int(flatten_latent_dim / 4    )
          else:
            A_    :			Optional[int]									=    int(snake_case_    )

          A_    :			Any									=    jax_memory_efficient_attention(
              snake_case_   , snake_case_   , snake_case_   , query_chunk_size=snake_case_   , key_chunk_size=4_0_9_6 * 4    )

          A_    :			Any									=    hidden_states.transpose(1   , 0   , 2    )
        else:
          # compute attentions
          A_    :			str									=    jnp.einsum('b i d, b j d->b i j'   , snake_case_   , snake_case_    )
          A_    :			Dict									=    attention_scores * self.scale
          A_    :			List[str]									=    nn.softmax(snake_case_   , axis=2    )

          # attend to values
          A_    :			Optional[Any]									=    jnp.einsum('b i j, b j d -> b i d'   , snake_case_   , snake_case_    )

        A_    :			List[str]									=    self.reshape_batch_dim_to_heads(snake_case_    )
        A_    :			Tuple									=    self.proj_attn(snake_case_    )
        return self.dropout_layer(snake_case_   , deterministic=snake_case_    )


class 					_UpperCAmelCase    (  nn.Module						):



      '''simple docstring'''
      lowercase_ :					int
      lowercase_ :					int
      lowercase_ :					int
      lowercase_ :					float             =  0.0
      lowercase_ :					bool             =  False
      lowercase_ :					jnp.dtype             =  jnp.floataa
      lowercase_ :					bool             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    FlaxAttention(
            self.dim   , self.n_heads   , self.d_head   , self.dropout   , self.use_memory_efficient_attention   , dtype=self.dtype    )
        # cross attention
        A_    :			Optional[Any]									=    FlaxAttention(
            self.dim   , self.n_heads   , self.d_head   , self.dropout   , self.use_memory_efficient_attention   , dtype=self.dtype    )
        A_    :			Optional[Any]									=    FlaxFeedForward(dim=self.dim   , dropout=self.dropout   , dtype=self.dtype    )
        A_    :			Any									=    nn.LayerNorm(epsilon=1E-5   , dtype=self.dtype    )
        A_    :			Dict									=    nn.LayerNorm(epsilon=1E-5   , dtype=self.dtype    )
        A_    :			Optional[int]									=    nn.LayerNorm(epsilon=1E-5   , dtype=self.dtype    )
        A_    :			List[str]									=    nn.Dropout(rate=self.dropout    )

      def __call__(	self   , snake_case_   , snake_case_   , snake_case_=True    ):

        """simple docstring"""

        A_    :			Optional[int]									=    hidden_states
        if self.only_cross_attention:
          A_    :			Union[str, Any]									=    self.attna(self.norma(snake_case_    )   , snake_case_   , deterministic=snake_case_    )
        else:
          A_    :			Tuple									=    self.attna(self.norma(snake_case_    )   , deterministic=snake_case_    )
        A_    :			Optional[Any]									=    hidden_states + residual

        # cross attention
        A_    :			List[str]									=    hidden_states
        A_    :			List[str]									=    self.attna(self.norma(snake_case_    )   , snake_case_   , deterministic=snake_case_    )
        A_    :			Any									=    hidden_states + residual

        # feed forward
        A_    :			Dict									=    hidden_states
        A_    :			str									=    self.ff(self.norma(snake_case_    )   , deterministic=snake_case_    )
        A_    :			Optional[int]									=    hidden_states + residual

        return self.dropout_layer(snake_case_   , deterministic=snake_case_    )


class 					_UpperCAmelCase    (  nn.Module						):



      '''simple docstring'''
      lowercase_ :					int
      lowercase_ :					int
      lowercase_ :					int
      lowercase_ :					int             =  1
      lowercase_ :					float             =  0.0
      lowercase_ :					bool             =  False
      lowercase_ :					bool             =  False
      lowercase_ :					jnp.dtype             =  jnp.floataa
      lowercase_ :					bool             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    nn.GroupNorm(num_groups=3_2   , epsilon=1E-5    )

        A_    :			Dict									=    self.n_heads * self.d_head
        if self.use_linear_projection:
          A_    :			Union[str, Any]									=    nn.Dense(snake_case_   , dtype=self.dtype    )
        else:
          A_    :			Any									=    nn.Conv(
              snake_case_   , kernel_size=(1, 1)   , strides=(1, 1)   , padding='VALID'   , dtype=self.dtype   , )

        A_    :			List[str]									=    [
            FlaxBasicTransformerBlock(
                snake_case_   , self.n_heads   , self.d_head   , dropout=self.dropout   , only_cross_attention=self.only_cross_attention   , dtype=self.dtype   , use_memory_efficient_attention=self.use_memory_efficient_attention   , )
            for _ in range(self.depth    )
        ]

        if self.use_linear_projection:
          A_    :			List[Any]									=    nn.Dense(snake_case_   , dtype=self.dtype    )
        else:
          A_    :			Dict									=    nn.Conv(
              snake_case_   , kernel_size=(1, 1)   , strides=(1, 1)   , padding='VALID'   , dtype=self.dtype   , )

        A_    :			List[str]									=    nn.Dropout(rate=self.dropout    )

      def __call__(	self   , snake_case_   , snake_case_   , snake_case_=True    ):

        """simple docstring"""

        A_		,			A_		,			A_		,			A_    :			Any									=    hidden_states.shape
        A_    :			Any									=    hidden_states
        A_    :			List[Any]									=    self.norm(snake_case_    )
        if self.use_linear_projection:
          A_    :			str									=    hidden_states.reshape(snake_case_   , height * width   , snake_case_    )
          A_    :			Optional[Any]									=    self.proj_in(snake_case_    )
        else:
          A_    :			List[str]									=    self.proj_in(snake_case_    )
          A_    :			List[Any]									=    hidden_states.reshape(snake_case_   , height * width   , snake_case_    )

        for transformer_block in self.transformer_blocks:
          A_    :			int									=    transformer_block(snake_case_   , snake_case_   , deterministic=snake_case_    )

        if self.use_linear_projection:
          A_    :			Union[str, Any]									=    self.proj_out(snake_case_    )
          A_    :			Any									=    hidden_states.reshape(snake_case_   , snake_case_   , snake_case_   , snake_case_    )
        else:
          A_    :			int									=    hidden_states.reshape(snake_case_   , snake_case_   , snake_case_   , snake_case_    )
          A_    :			Union[str, Any]									=    self.proj_out(snake_case_    )

        A_    :			Union[str, Any]									=    hidden_states + residual
        return self.dropout_layer(snake_case_   , deterministic=snake_case_    )


class 					_UpperCAmelCase    (  nn.Module						):



      '''simple docstring'''
      lowercase_ :					int
      lowercase_ :					float             =  0.0
      lowercase_ :					jnp.dtype             =  jnp.floataa

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    FlaxGEGLU(self.dim   , self.dropout   , self.dtype    )
        A_    :			int									=    nn.Dense(self.dim   , dtype=self.dtype    )

      def __call__(	self   , snake_case_   , snake_case_=True    ):

        """simple docstring"""

        A_    :			Dict									=    self.net_a(snake_case_   , deterministic=snake_case_    )
        A_    :			Any									=    self.net_a(snake_case_    )
        return hidden_states


class 					_UpperCAmelCase    (  nn.Module						):



      '''simple docstring'''
      lowercase_ :					int
      lowercase_ :					float             =  0.0
      lowercase_ :					jnp.dtype             =  jnp.floataa

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    self.dim * 4
        A_    :			List[str]									=    nn.Dense(inner_dim * 2   , dtype=self.dtype    )
        A_    :			int									=    nn.Dropout(rate=self.dropout    )

      def __call__(	self   , snake_case_   , snake_case_=True    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    self.proj(snake_case_    )
        A_		,			A_    :			Any									=    jnp.split(snake_case_   , 2   , axis=2    )
        return self.dropout_layer(hidden_linear * nn.gelu(snake_case_    )   , deterministic=snake_case_    )