'''simple docstring'''

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    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						KandinskyVaaPriorEmbaEmbPipeline
   __UpperCAmelCase           =						["""prompt""", """image"""]
   __UpperCAmelCase           =						["""prompt""", """image"""]
   __UpperCAmelCase           =						[
       """num_images_per_prompt""",
       """strength""",
       """generator""",
       """num_inference_steps""",
       """latents""",
       """negative_prompt""",
       """guidance_scale""",
       """output_type""",
       """return_dict""",
   ]
   __UpperCAmelCase           =						False



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        return 32



   @property
   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        return 32



   @property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        return self.time_input_dim



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return self.time_input_dim * 4



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        return 100



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :str       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
        return tokenizer



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :List[str]       =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=1_000					,      )
        return CLIPTextModelWithProjection(lowerCAmelCase__     )



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        torch.manual_seed(0     )

        _UpperCamelCase  :Optional[int]       ={
            """num_attention_heads""": 2,
            """attention_head_dim""": 12,
            """embedding_dim""": self.text_embedder_hidden_size,
            """num_layers""": 1,
        }

        _UpperCamelCase  :List[str]       =PriorTransformer(**lowerCAmelCase__     )
        # 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
        _UpperCamelCase  :Dict       =nn.Parameter(torch.ones(model.clip_std.shape     )     )
        return model



   @property
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :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					,      )

        _UpperCamelCase  :Union[str, Any]       =CLIPVisionModelWithProjection(lowerCAmelCase__     )
        return model



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =CLIPImageProcessor(
            crop_size=224					,      do_center_crop=lowerCAmelCase__					,      do_normalize=lowerCAmelCase__					,      do_resize=lowerCAmelCase__					,      image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]					,      image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,      resample=3					,      size=224					,      )

        return image_processor



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =self.dummy_prior
        _UpperCamelCase  :Optional[int]       =self.dummy_image_encoder
        _UpperCamelCase  :Tuple       =self.dummy_text_encoder
        _UpperCamelCase  :Union[str, Any]       =self.dummy_tokenizer
        _UpperCamelCase  :Any       =self.dummy_image_processor

        _UpperCamelCase  :Dict       =UnCLIPScheduler(
            variance_type="""fixed_small_log"""					,      prediction_type="""sample"""					,      num_train_timesteps=1_000					,      clip_sample=lowerCAmelCase__					,      clip_sample_range=10.0					,      )

        _UpperCamelCase  :int       ={
            """prior""": prior,
            """image_encoder""": image_encoder,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """scheduler""": scheduler,
            """image_processor""": image_processor,
        }

        return components



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=0     )				->     Optional[Any]:
        """simple docstring"""





        if str(lowerCAmelCase__     ).startswith("""mps"""     ):
             _UpperCamelCase  :List[Any]       =torch.manual_seed(lowerCAmelCase__     )
        else:
             _UpperCamelCase  :Optional[int]       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       =floats_tensor((1, 3, 64, 64)					,      rng=random.Random(lowerCAmelCase__     )     ).to(lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =image.cpu().permute(0					,      2					,      3					,      1     )[0]
        _UpperCamelCase  :Dict       =Image.fromarray(np.uinta(lowerCAmelCase__     )     ).convert("""RGB"""     ).resize((256, 256)     )

        _UpperCamelCase  :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  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       ="""cpu"""

        _UpperCamelCase  :List[Any]       =self.get_dummy_components()

        _UpperCamelCase  :int       =self.pipeline_class(**lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =pipe.to(lowerCAmelCase__     )

        pipe.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =pipe(**self.get_dummy_inputs(lowerCAmelCase__     )     )
        _UpperCamelCase  :Any       =output.image_embeds

        _UpperCamelCase  :Optional[Any]       =pipe(
            **self.get_dummy_inputs(lowerCAmelCase__     )					,      return_dict=lowerCAmelCase__					,      )[0]

        _UpperCamelCase  :int       =image[0, -10:]
        _UpperCamelCase  :Optional[int]       =image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        _UpperCamelCase  :int       =np.array(
            [
                0.107_1284,
                1.333_0271,
                0.6126_0223,
                -0.669_1065,
                -0.384_6852,
                -1.030_3661,
                0.2271_6111,
                0.0334_8901,
                0.3004_0675,
                -0.2480_5029,
            ]     )

        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  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =torch_device == """cpu"""
        _UpperCamelCase  :List[Any]       =True
        _UpperCamelCase  :Dict       =False

        self._test_inference_batch_single_identical(
            test_max_difference=lowerCAmelCase__					,      relax_max_difference=lowerCAmelCase__					,      test_mean_pixel_difference=lowerCAmelCase__					,      )




   @skip_mps
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =torch_device == """cpu"""
        _UpperCamelCase  :Union[str, Any]       =False

        self._test_attention_slicing_forward_pass(
            test_max_difference=lowerCAmelCase__					,      test_mean_pixel_difference=lowerCAmelCase__					,      )
'''simple docstring'''
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 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						Dict					,  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	states.reshape(1					,  -1					,  model.config.state_dim      )
    __UpperCamelCase						:						Tuple		     =	actions.reshape(1					,  -1					,  model.config.act_dim      )
    __UpperCamelCase						:						Optional[Any]		     =	returns_to_go.reshape(1					,  -1					,  1      )
    __UpperCamelCase						:						Optional[int]		     =	timesteps.reshape(1					,  -1      )

    if model.config.max_length is not None:
        __UpperCamelCase						:						Any		     =	states[:, -model.config.max_length :]
        __UpperCamelCase						:						int		     =	actions[:, -model.config.max_length :]
        __UpperCamelCase						:						Any		     =	returns_to_go[:, -model.config.max_length :]
        __UpperCamelCase						:						Tuple		     =	timesteps[:, -model.config.max_length :]

        # pad all tokens to sequence length
        __UpperCamelCase						:						str		     =	torch.cat(
            [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
        __UpperCamelCase						:						int		     =	attention_mask.to(dtype=torch.long					,  device=states.device      ).reshape(1					,  -1      )
        __UpperCamelCase						:						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      )
        __UpperCamelCase						:						Tuple		     =	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      )
        __UpperCamelCase						:						Optional[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      )
        __UpperCamelCase						:						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:
        __UpperCamelCase						:						List[str]		     =	None

    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	model(
        states=_a					,  actions=_a					,  rewards=_a					,  returns_to_go=_a					,  timesteps=_a					,  attention_mask=_a					,  return_dict=_a					,  )

    return action_preds[0, -1]


# build the environment

a=	gym.make('''Hopper-v3''')
a=	env.observation_space.shape[0]
a=	env.action_space.shape[0]
a=	1_0_0_0
a=	'''cuda'''
a=	1_000.0  # normalization for rewards/returns
a=	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a=	np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
a=	np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
a=	torch.from_numpy(state_mean).to(device=device)
a=	torch.from_numpy(state_std).to(device=device)

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

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

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

    a=	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=	action
    a=	action.detach().cpu().numpy()

    a,      a,      a,      a=	env.step(action)

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

    a=	target_return[0, -1] - (reward / scale)
    a=	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    a=	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



