code: 
  
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from torch import nn
  from transformers import (
      CLIPImageProcessor,
      CLIPTextConfig,
      CLIPTextModelWithProjection,
      CLIPTokenizer,
      CLIPVisionConfig,
      CLIPVisionModelWithProjection,
  )
  
  from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
  from diffusers.utils import floats_tensor, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
  
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase_						,						unittest.TestCase ):
      lowerCAmelCase_							:		List[Any]						=							KandinskyVaaPriorEmbaEmbPipeline
      lowerCAmelCase_							:		str						=							["""prompt""", """image"""]
      lowerCAmelCase_							:		List[str]						=							["""prompt""", """image"""]
      lowerCAmelCase_							:		Any						=							[
          """num_images_per_prompt""",
          """strength""",
          """generator""",
          """num_inference_steps""",
          """latents""",
          """negative_prompt""",
          """guidance_scale""",
          """output_type""",
          """return_dict""",
      ]
      lowerCAmelCase_							:		Dict						=							False
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Optional[int]    ):
          return 32
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Any    ):
          return 32
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    int    ):
          return self.time_input_dim
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Optional[int]    ):
          return self.time_input_dim * 4
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Any    ):
          return 100
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Any    			=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
          return tokenizer
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    List[str]    ):
          torch.manual_seed(0    )
          lowerCAmelCase				:			Union[str, Any]    			=      CLIPTextConfig(
              bos_token_id=0    ,				eos_token_id=2    ,				hidden_size=self.text_embedder_hidden_size    ,				projection_dim=self.text_embedder_hidden_size    ,				intermediate_size=37    ,				layer_norm_eps=1E-05    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				pad_token_id=1    ,				vocab_size=1000    ,				)
          return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Optional[Any]    ):
          torch.manual_seed(0    )
  
          lowerCAmelCase				:			List[str]    			=      {
              'num_attention_heads': 2,
              'attention_head_dim': 12,
              'embedding_dim': self.text_embedder_hidden_size,
              'num_layers': 1,
          }
  
          lowerCAmelCase				:			Union[str, Any]    			=      PriorTransformer(**__SCREAMING_SNAKE_CASE    )
          # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
          lowerCAmelCase				:			Optional[Any]    			=      nn.Parameter(torch.ones(model.clip_std.shape    )    )
          return model
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          torch.manual_seed(0    )
          lowerCAmelCase				:			List[Any]    			=      CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size    ,				image_size=224    ,				projection_dim=self.text_embedder_hidden_size    ,				intermediate_size=37    ,				num_attention_heads=4    ,				num_channels=3    ,				num_hidden_layers=5    ,				patch_size=14    ,				)
  
          lowerCAmelCase				:			Optional[Any]    			=      CLIPVisionModelWithProjection(__SCREAMING_SNAKE_CASE    )
          return model
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			Optional[int]    			=      CLIPImageProcessor(
              crop_size=224    ,				do_center_crop=__SCREAMING_SNAKE_CASE    ,				do_normalize=__SCREAMING_SNAKE_CASE    ,				do_resize=__SCREAMING_SNAKE_CASE    ,				image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]    ,				image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]    ,				resample=3    ,				size=224    ,				)
  
          return image_processor
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Any    			=      self.dummy_prior
          lowerCAmelCase				:			Dict    			=      self.dummy_image_encoder
          lowerCAmelCase				:			Optional[int]    			=      self.dummy_text_encoder
          lowerCAmelCase				:			Any    			=      self.dummy_tokenizer
          lowerCAmelCase				:			Optional[Any]    			=      self.dummy_image_processor
  
          lowerCAmelCase				:			Tuple    			=      UnCLIPScheduler(
              variance_type='fixed_small_log'    ,				prediction_type='sample'    ,				num_train_timesteps=1000    ,				clip_sample=__SCREAMING_SNAKE_CASE    ,				clip_sample_range=10.0    ,				)
  
          lowerCAmelCase				:			Dict    			=      {
              'prior': prior,
              'image_encoder': image_encoder,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'scheduler': scheduler,
              'image_processor': image_processor,
          }
  
          return components
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]=0    ):
  
          if str(__SCREAMING_SNAKE_CASE    ).startswith('mps'    ):
              lowerCAmelCase				:			List[str]    			=      torch.manual_seed(__SCREAMING_SNAKE_CASE    )
          else:
              lowerCAmelCase				:			List[Any]    			=      torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(__SCREAMING_SNAKE_CASE    )
  
          lowerCAmelCase				:			Tuple    			=      floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(__SCREAMING_SNAKE_CASE    )    ).to(__SCREAMING_SNAKE_CASE    )
          lowerCAmelCase				:			int    			=      image.cpu().permute(0    ,				2    ,				3    ,				1    )[0]
          lowerCAmelCase				:			Dict    			=      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert('RGB'    ).resize((256, 256)    )
  
          lowerCAmelCase				:			Tuple    			=      {
              'prompt': 'horse',
              'image': init_image,
              'strength': 0.5,
              'generator': generator,
              'guidance_scale': 4.0,
              'num_inference_steps': 2,
              'output_type': 'np',
          }
          return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			int    			=      'cpu'
  
          lowerCAmelCase				:			Union[str, Any]    			=      self.get_dummy_components()
  
          lowerCAmelCase				:			Tuple    			=      self.pipeline_class(**__SCREAMING_SNAKE_CASE    )
          lowerCAmelCase				:			int    			=      pipe.to(__SCREAMING_SNAKE_CASE    )
  
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
          lowerCAmelCase				:			Union[str, Any]    			=      pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    )    )
          lowerCAmelCase				:			Optional[Any]    			=      output.image_embeds
  
          lowerCAmelCase				:			Tuple    			=      pipe(
              **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE    )    ,				return_dict=__SCREAMING_SNAKE_CASE    ,				)[0]
  
          lowerCAmelCase				:			Any    			=      image[0, -10:]
          lowerCAmelCase				:			Optional[int]    			=      image_from_tuple[0, -10:]
  
          assert image.shape == (1, 32)
  
          lowerCAmelCase				:			str    			=      np.array(
              [
                  0.1_07_12_84,
                  1.3_33_02_71,
                  0.61_26_02_23,
                  -0.6_69_10_65,
                  -0.3_84_68_52,
                  -1.0_30_36_61,
                  0.22_71_61_11,
                  0.03_34_89_01,
                  0.30_04_06_75,
                  -0.24_80_50_29,
              ]    )
  
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
  
  
  
  
  
  
      @skip_mps
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			List[Any]    			=      torch_device == 'cpu'
          lowerCAmelCase				:			Dict    			=      True
          lowerCAmelCase				:			Dict    			=      False
  
          self._test_inference_batch_single_identical(
              test_max_difference=__SCREAMING_SNAKE_CASE    ,				relax_max_difference=__SCREAMING_SNAKE_CASE    ,				test_mean_pixel_difference=__SCREAMING_SNAKE_CASE    ,				)
  
  
  
  
  
  
      @skip_mps
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			str    			=      torch_device == 'cpu'
          lowerCAmelCase				:			Optional[int]    			=      False
  
          self._test_attention_slicing_forward_pass(
              test_max_difference=__SCREAMING_SNAKE_CASE    ,				test_mean_pixel_difference=__SCREAMING_SNAKE_CASE    ,				)
  
  
  
  
  
  
style_context: 
  
  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__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
      lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
      lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
      lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)
  
      if model.config.max_length is not None:
          lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
          lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
          lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
          lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]
  
          # pad all tokens to sequence length
          lowerCAmelCase				:			List[str]    			=      torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
          lowerCAmelCase				:			List[Any]    			=      attention_mask.to(dtype=torch.long,      device=states.device				).reshape(1,      -1				)
          lowerCAmelCase				:			Any    			=      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				)
          lowerCAmelCase				:			int    			=      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				)
          lowerCAmelCase				:			Union[str, Any]    			=      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				)
          lowerCAmelCase				:			Tuple    			=      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:
          lowerCAmelCase				:			Optional[Any]    			=      None
  
      lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      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
  
  __A   :     Dict      =			gym.make('''Hopper-v3''')
  __A   :     Any      =			env.observation_space.shape[0]
  __A   :     Optional[Any]      =			env.action_space.shape[0]
  __A   :     Any      =			1000
  __A   :     Tuple      =			'''cuda'''
  __A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
  __A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __A   :     str      =			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  __A   :     Dict      =			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  __A   :     int      =			torch.from_numpy(state_mean).to(device=device)
  __A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
  __A   :     Any      =			model.to(device)
  model.eval()
  
  for ep in range(10):
   __A     ,      __A   :     Optional[int]      =			0, 0
   __A   :     List[str]      =			env.reset()
   __A   :     Optional[int]      =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   __A   :     str      =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   __A   :     Optional[int]      =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   __A   :     Any      =			torch.zeros(0, device=device, dtype=torch.floataa)
  
   __A   :     List[Any]      =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    __A   :     Union[str, Any]      =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    __A   :     List[Any]      =			torch.cat([rewards, torch.zeros(1, device=device)])
  
    __A   :     int      =			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),
    )
    __A   :     List[str]      =			action
    __A   :     List[str]      =			action.detach().cpu().numpy()
  
    __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)
  
    __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    __A   :     Any      =			torch.cat([states, cur_state], dim=0)
    __A   :     Dict      =			reward
  
    __A   :     str      =			target_return[0, -1] - (reward / scale)
    __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    __A   :     List[Any]      =			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
  
  
  
  
  
  